Repository: Noble-Lab/casanovo (language: python)

Issue:
IndexError on particularly ugly spectrum
With --mode=denovo and pretrained human weights  this spectrum:

BEGIN IONS
TITLE=crasher
RTINSECONDS=2800
PEPMASS=1551
CHARGE=4+
1552.40144197  301.23929755
1551.20606409 1051.24587242
1550.88090936 1053.2816881
END IONS

raises an IndexError at https://github.com/Noble-Lab/casanovo/blob/main/casanovo/data/datasets.py#L185

To trigger, the peaks need to be sufficiently close to each  other and to PEPMASS.


Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index 75c5d0ff..7c19a775 100644
--- a/README.md
+++ b/README.md
@@ -1,117 +1,146 @@
-# casanovo
+# Casanovo
+
 **_De Novo_ Mass Spectrometry Peptide Sequencing with a Transformer Model**
 
 ![image](https://user-images.githubusercontent.com/32707537/152622912-ca87da20-a64c-4e3f-9ca1-721c6b0d9c64.png)
 
-Data and pre-trained model weights are available [here](https://zenodo.org/record/6791263).
+If you use Casanovo in your work, please cite the following publication:
+
+- Yilmaz, M., Fondrie, W. E., Bittremieux, W., Oh, S. & Noble, W. S. *De novo* mass spectrometry peptide sequencing with a transformer model. in *Proceedings of the 39th International Conference on Machine Learning - ICML '22* vol. 162 25514–25522 (PMLR, 2022). [https://proceedings.mlr.press/v162/yilmaz22a.html](https://proceedings.mlr.press/v162/yilmaz22a.html)
+
+Data and pre-trained model weights are available [on Zenodo](https://zenodo.org/record/6791263).
 
-A link to the preprint of the paper where we discuss our methods and tests can be found [here](https://www.biorxiv.org/content/10.1101/2022.02.07.479481v1).
+## Documentation
 
-# Documentation:
 #### https://casanovo.readthedocs.io/en/latest/
 
-# How to get started with Casanovo?
-## Our recommendation:
+## Getting started with Casanovo
 
-Install **Anaconda**! It helps keep your environment for casanovo and its dependencies separate from your other Python environments. **This is especially helpful because casanovo works within a specific range of Python versions (3.7 > Python version > 3.10).**
+We recommend to run Casanovo in a dedicated **Anaconda** environment.
+This helps keep your environment for Casanovo and its dependencies separate from your other Python environments.
+**This is especially helpful because Casanovo works within a specific range of Python versions (3.7 > Python version > 3.10).**
 
 - Check out the [Windows](https://docs.anaconda.com/anaconda/install/windows/#), [MacOS](https://docs.anaconda.com/anaconda/install/mac-os/), and [Linux](https://docs.anaconda.com/anaconda/install/linux/) installation instructions.
 
 Once you have Anaconda installed, you can use this helpful [cheat sheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/52a95608c49671267e40c689e0bc00ca/conda-cheatsheet.pdf) to see common commands and what they do.
 
-## Environment creation:
+### Environment creation
 
 Open up the anaconda prompt and run this command:
+
 ```
 conda create --name casanovo_env python=3.7
 ```
-This will create an anaconda environment called `casanovo_env` that has python 3.7 installed (You can check if it was created by typing `conda env list`). 
+
+This will create an anaconda environment called `casanovo_env` that has Python 3.7 installed.
+(You can check if it was created by typing `conda env list`.)
 
 You can activate this environment by typing:
+
 ```
 conda activate casanovo_env
 ```
-To the left of your anaconda prompt line it should now say **(casanovo_env)** instead of **(base)**. If this is the case, then you have set up anaconda and the environment properly.
 
-**Be sure to retype in the activation command into your terminal when you reopen anaconda and want to use casanovo.** The base environment most likely will not work.
+To the left of your anaconda prompt line it should now say **(casanovo_env)** instead of **(base)**.
+If this is the case, then you have set up anaconda and the environment properly.
 
-## Installation:
+**Be sure to retype in the activation command into your terminal when you reopen anaconda and want to use Casanovo.**
+The base environment most likely will not work.
+
+### Installation
+
+Install Casanovo as a Python package from this repository (requires 3.7 > [Python version] > 3.10 , dependencies will be installed automatically as needed):
 
-Install `casanovo` as a Python package from this repo (requires 3.7 > [Python version] > 3.10 , dependencies will be installed automatically as needed):
 ```
 pip install git+https://github.com/Noble-Lab/casanovo.git#egg=casanovo
 ```
 
-Once installed, Casanovo can be used with a simple command line interface. **Run `casanovo --help` for more details.** All auxiliary data, model and training-related variables can be specified in a user created `.yaml` file, see `casanovo/config.yaml` for the default configuration that was used to obtain the reported results.
+Once installed, Casanovo can be used with a simple command line interface.
+**Run `casanovo --help` for more details.**
+All auxiliary data, model, and training-related parameters can be specified in a user created `.yaml` configuration file.
+See [`casanovo/config.yaml`](https://github.com/Noble-Lab/casanovo/blob/main/casanovo/config.yaml) for the default configuration that was used to obtain the reported results.
+
+### Example commands
 
-# Example Commands:
+- To run _de novo_ sequencing:
 
-- To evaluate _de novo_ sequencing performance of a pre-trained model (peptide annotations are needed for spectra):
 ```
-casanovo --mode=eval --model_path='path/to/pretrained' --test_data_path='path/to/test/mgf/files/dir' --config_path='path/to/config'
+casanovo --mode=denovo --model='path/to/pretrained.ckpt' --peak_dir='path/to/predict/mgf/files/dir' --config='path/to/config.yaml' --output='path/to/output'
 ```
 
-- To run _de novo_ sequencing without evaluation (specify directory path for output csv file with _de novo_ sequences, see `casanovo_sample_output.csv` for a sample output file):
+This will write peptide predictions for the given MS/MS spectra to the specified output file in a tab-separated format (extension: .csv).
+
+- To evaluate _de novo_ sequencing performance based on known spectrum annotations:
+
 ```
-casanovo --mode=denovo --model_path='path/to/pretrained' --test_data_path='path/to/test/mgf/files/dir' --config_path='path/to/config' --output_path='path/to/output'
+casanovo --mode=eval --model='path/to/pretrained.ckpt' --peak_dir='path/to/test/predict/files/dir' --config='path/to/config.yaml'
 ```
 
-- To train a model from scratch or continue training a pre-trained model:
+Note that to evaluate the peptide predictions, ground truth peptide labels in an annotated MGF file need to be present.
+
+- To train a model from scratch:
+
 ```
-casanovo --mode=train --model_path='path/to/pretrained' --train_data_path='path/to/train/mgf/files/dir'  --val_data_path='path/to/validation/mgf/files/dir' --config_path='path/to/config'
+casanovo --mode=train --peak_dir='path/to/train/mgf/files/dir' --peak_dir_val='path/to/validation/mgf/files/dir' --config='path/to/config.yaml'
 ```
-# Example Job:
-## A small walkthrough on how to use casanovo with a very small spectra (~100) set
 
-### The spectra file (.mgf) that we will be running this job on can be seen in the sample_data folder.
+If a training is continued for a previously trained model, specify the starting model weights using `--model`.
+
+### Example job
 
-- Step 1: Install casanovo (see above for details)
-- Step 2: Download the casanovo_pretrained_model_weights.zip from [here](https://zenodo.org/record/6791263). Place these models in a location that you can easily access and know the path of.
-    - We will be using pretrained_excl_mouse.ckpt for this job.
-- Step 3: Copy the example config.yaml file into a location you can easily access. 
-- Step 4: Change the `num_workers` and the `gpus` fields to reflect the number of cores and gpus on the machine you are running the job on.
-    - For example, if you have 4 CPU cores and 0 gpus, then num_workers would be 4, and gpus would be None
-- Step 5: Ensure you are in the proper anaconda environment by typing ```conda activate casanovo_env```. (If you named it differently, type in that name instead)
-- Step 6: Run this command:
+We will demonstrate how to use Casanovo using a small walkthrough example on a small MGF file (~100 MS/MS spectra).
+The example MGF file is available at [`sample_data/sample_preprocessed_spectra.mgf`](https://github.com/Noble-Lab/casanovo/blob/main/sample_data/sample_preprocessed_spectra.mgf`).
+
+1. Install Casanovo (see above for details).
+2. Download the `casanovo_pretrained_model_weights.zip` from [Zenodo](https://zenodo.org/record/6791263). Place these models in a location that you can easily access and know the path of.
+    - We will be `using pretrained_excl_mouse.ckpt` for this job.
+3. Copy the example `config.yaml` file into a location you can easily access. 
+4. Ensure you are in the proper anaconda environment by typing `conda activate casanovo_env`. (If you named your environment differently, type in that name instead.)
+5. Run this command:
 ```
-casanovo --mode=denovo --model_path='[PATH_TO]/pretrained_excl_mouse.ckpt' --test_data_path='sample_data' --config_path='path/to/config.yaml' --preprocess_spec=False
+casanovo --mode=denovo --model='[PATH_TO]/pretrained_excl_mouse.ckpt' --peak_dir='sample_data' --config='path/to/config.yaml'
 ```
-Make sure you have the proper filepath to the pretrained_excl_mouse.ckpt file.
- - Note: If you want to get the ouput csv in a place OTHER than where you ran this command, specify where you would like the output to be placed by specifying a directory in the --output_path CLI field
-    - It would look like ```--output_path='path/to/output/location'``` appended onto the end of the above command. Be sure to provide a directory, not a file!
+Make sure you use the proper filepath to the `pretrained_excl_mouse.ckpt` file.
+    - Note: If you want to get the ouput CSV file in different location than the working directory, specify an alternative output location using the `--output` parameter.
 
-This job should take very little time to run (< 1 minute), and the result should be a file named ```casanovo_output.csv``` wherever you specified.
+This job will take very little time to run (< 1 minute).
 
 If the first few lines look like:
+
 ```
-spectrum_id,denovo_seq,peptide_score,aa_scores
-0,LAHYNKR,0.9912219984190804,"[1.0, 1.0, 1.0, 0.99948...
+spectrum_id     sequence            score       aa_scores
+0               LAHYNKR             0.98197     1.0,0.999...
+1               VKEDYGQM+15.995PR   0.77206     0.999,0.999...
 ```
-Congratulations! You got casanovo to work!
 
-# Common Troubleshooting/FAQ
+Congratulations! You got Casanovo to work.
+
+## Common Troubleshooting / FAQ
+
+**I installed Casanovo and it worked before, but I after reopening Anaconda it says that Casanovo is not installed.**
+
+Make sure you are in the `casanovo_env` environment. You can ensure this by typing:
 
-## Installed casanovo and it worked before, but I reopened Anaconda again and now it says casanovo is not installed
-Make sure you are in the `casanovo_env` environment. You can make sure you are in it by typing
 ```
 conda activate casanovo_env
 ```
-## What CLI Prompts can I use?
-Run the following command in your command prompt:
+
+**Which command-line options are available?**
+
+Run the following command in your command prompt to see all possible command-line configuration options:
 ```
 casanovo --help
 ```
-It should give you a comprehensive list of all CLI options you can tag onto a casanovo job and how/why to use them.
 
-# Release Notes
+## Release notes
 
-- Release 1.0 1-28-22: Initial commit
-- Release 1.1 2-4-22: Add data infrastructure, model and training/testing functionality
-- Release 1.11 2-10-22: Add more cli options and specify custom config file
-- Release 1.12 2-20-22: Add support for multiple mgf files in a directory
-- Release 1.2 3-7-22: Add peptide and amino acid confidence scores to output file
-- Release 2.0 6-5-22: Added additional CLI functionality, changed config file format, added pytest functionality, tutorial added, documentation with sphinx/ReadTheDocs added
-- Release 2.01 6-13-22: Release notes added
-- Release 2.11 7-2-22: Import latest Depthcharge version with stable memory usage and fix to positional encoding for AA
-- Release 2.12 7-27-22: Update tutorial
+- Release 2.1.1 (2022-07-27): Update tutorial
+- Release 2.1.0 (2022-07-02): Import latest depthcharge version with stable memory usage and fix to positional encoding for amino acids
+- Release 2.0.1 (2022-06-13): Release notes added
+- Release 2.0.0 (2022-06-05): Added additional CLI functionality, changed config file format, added pytest functionality, tutorial added, documentation with sphinx/ReadTheDocs added
+- Release 1.2.0 (2022-03-07): Add peptide and amino acid confidence scores to output file
+- Release 1.1.2 (2022-02-20): Add support for multiple MGF files in a directory
+- Release 1.1.1 (2022-02-10): Add more CLI options and specify custom config file
+- Release 1.1.0 (2022-02-04): Add data infrastructure, model and training/testing functionality
+- Release 1.0.0 (2022-01-28): Initial commit
 
diff --git a/casanovo/__init__.py b/casanovo/__init__.py
index 9a1c5a33..1afa731a 100644
--- a/casanovo/__init__.py
+++ b/casanovo/__init__.py
@@ -1,4 +1,4 @@
-"""See the README for detailed documentation and examples."""
 from .version import _get_version
 
+
 __version__ = _get_version()
diff --git a/casanovo/casanovo.py b/casanovo/casanovo.py
index 46eed044..ee794231 100644
--- a/casanovo/casanovo.py
+++ b/casanovo/casanovo.py
@@ -1,136 +1,148 @@
-"""The command line entry point for casanovo"""
-from email.policy import default
-import click, logging
-import yaml
+"""The command line entry point for Casanovo."""
+import datetime
+import logging
 import os
-from casanovo.denovo import train, evaluate, denovo
+import sys
+
+import click
+import pytorch_lightning as pl
+import yaml
+
+from . import __version__
+from casanovo.denovo import model_runner
+
+
+logger = logging.getLogger("casanovo")
+
 
-# Required options
 @click.command()
 @click.option(
     "--mode",
     required=True,
-    default="eval",
-    help='Choose on a high level what the program will do. "train" will train a model from scratch or continue training a pre-trained model. "eval" will evaluate de novo sequencing performance of a pre-trained model (peptide annotations are needed for spectra). "denovo" will run de novo sequencing without evaluation (specificy directory path for output csv file with de novo sequences).',
-    type=click.Choice(["train", "eval", "denovo"]),
+    default="denovo",
+    help="\b\nThe mode in which to run Casanovo:\n"
+    '- "denovo" will predict peptide sequences for\nunknown MS/MS spectra.\n'
+    '- "train" will train a model (from scratch or by\ncontinuing training a '
+    "previously trained model).\n"
+    '- "eval" will evaluate the performance of a\ntrained model using '
+    "previously acquired spectrum\nannotations.",
+    type=click.Choice(["denovo", "train", "eval"]),
 )
 @click.option(
-    "--model_path",
-    required=True,
-    help="Specify path to pre-trained model weights (.ckpt file) for testing or to continue to train.",
-    type=click.Path(exists=True, dir_okay=False, file_okay=True),
-)
-# Base options
-@click.option(
-    "--train_data_path",
-    help="Specify path to .mgf files to be used as training data",
-    type=click.Path(exists=True, dir_okay=True, file_okay=False),
+    "--model",
+    help="The file name of the model weights (.ckpt file).",
+    type=click.Path(exists=True, dir_okay=False),
 )
 @click.option(
-    "--val_data_path",
-    help="Specify path to .mgf files to be used as validation data",
-    type=click.Path(exists=True, dir_okay=True, file_okay=False),
-)
-@click.option(
-    "--test_data_path",
-    help="Specify path to .mgf files to be used as test data",
-    type=click.Path(exists=True, dir_okay=True, file_okay=False),
+    "--peak_dir",
+    required=True,
+    help="The directory with peak files for predicting peptide sequences or "
+    "training Casanovo.",
+    type=click.Path(exists=True, file_okay=False),
 )
 @click.option(
-    "--config_path",
-    help="Specify path to custom config file which includes data and model related options. If not included, the default config.yaml will be used.",
-    type=click.Path(exists=True, dir_okay=False, file_okay=True),
+    "--peak_dir_val",
+    help="The directory with peak files to be used as validation data during "
+    "training.",
+    type=click.Path(exists=True, file_okay=False),
 )
 @click.option(
-    "--output_path",
-    help="Specify path to output de novo sequences. Output format is .csv",
-    type=click.Path(exists=True, dir_okay=True, file_okay=False),
+    "--config",
+    help="The file name of the configuration file with custom options. If not "
+    "specified, a default configuration will be used.",
+    type=click.Path(exists=True, dir_okay=False),
 )
-# De Novo sequencing options
 @click.option(
-    "--preprocess_spec",
-    default=None,
-    help="True if spectra data should be preprocessed, False if using preprocessed data.",
-    type=click.BOOL,
+    "--output",
+    help="The base output file name to store logging (extension: .log) and "
+    "(optionally) prediction results (extension: .csv).",
+    type=click.Path(dir_okay=False),
 )
 @click.option(
     "--num_workers",
     default=None,
-    help="Number of workers to use for spectra reading.",
+    help="The number of worker threads to use.",
     type=click.INT,
 )
-@click.option(
-    "--gpus",
-    default=(),
-    help="Specify gpus for usage. For multiple gpus, use format: --gpus=0 --gpus=1 --gpus=2... etc. etc.",
-    type=click.INT,
-    multiple=True,
-)
 def main(
-    # Req + base vars
-    mode,
-    model_path,
-    train_data_path,
-    val_data_path,
-    test_data_path,
-    config_path,
-    output_path,
-    # De Novo vars
-    preprocess_spec,
-    num_workers,
-    gpus,
+    mode: str,
+    model: str,
+    peak_dir: str,
+    peak_dir_val: str,
+    config: str,
+    output: str,
+    num_workers: int,
 ):
     """
-    The command line function for casanovo. De Novo Mass Spectrometry Peptide Sequencing with a Transformer Model.
-
     \b
-    Training option requirements:
-    mode, model_path, train_data_path, val_data_path, config_path
+    Casanovo: De novo mass spectrometry peptide sequencing with a transformer model.
+    ================================================================================
 
-    \b
-    Evaluation option requirements:
-    mode, model_path, test_data_path, config_path
+    Yilmaz, M., Fondrie, W. E., Bittremieux, W., Oh, S. & Noble, W. S. De novo
+    mass spectrometry peptide sequencing with a transformer model. Proceedings
+    of the 39th International Conference on Machine Learning - ICML '22 (2022)
+    doi:10.1101/2022.02.07.479481.
 
-    \b
-    De Novo option requirements:
-    mode, model_path, test_data_path, config_path, output_path
+    Official code website: https://github.com/Noble-Lab/casanovo
     """
-    logging.basicConfig(
-        level=logging.INFO,
-        format="%(levelname)s: %(message)s",
-    )
-    if config_path == None:
-        abs_path = os.path.join(
-            os.path.dirname(os.path.abspath(__file__)), "config.yaml"
+    if output is None:
+        output = os.path.join(
+            os.getcwd(),
+            f"casanovo_{datetime.datetime.now().strftime('%Y%m%d%H%M%S')}",
         )
-        with open(abs_path) as f:
-            config = yaml.safe_load(f)
-    else:
-        with open(config_path) as f:
-            config = yaml.safe_load(f)
-
-    if preprocess_spec != None:
-        config["preprocess_spec"] = preprocess_spec
-    if num_workers != None:
-        config["num_workers"] = num_workers
-    if gpus != ():
-        config["gpus"] = gpus
-    if mode == "train":
 
-        logging.info("Training Casanovo...")
-        train(train_data_path, val_data_path, model_path, config)
+    # Configure logging.
+    logging.captureWarnings(True)
+    root = logging.getLogger()
+    root.setLevel(logging.DEBUG)
+    log_formatter = logging.Formatter(
+        "{asctime} {levelname} [{name}/{processName}] {module}.{funcName} : "
+        "{message}",
+        style="{",
+    )
+    console_handler = logging.StreamHandler(sys.stderr)
+    console_handler.setLevel(logging.DEBUG)
+    console_handler.setFormatter(log_formatter)
+    root.addHandler(console_handler)
+    file_handler = logging.FileHandler(f"{os.path.splitext(output)[0]}.log")
+    file_handler.setFormatter(log_formatter)
+    root.addHandler(file_handler)
+    # Disable dependency non-critical log messages.
+    logging.getLogger("depthcharge").setLevel(logging.INFO)
+    logging.getLogger("h5py").setLevel(logging.WARNING)
+    logging.getLogger("numba").setLevel(logging.WARNING)
+    logging.getLogger("pytorch_lightning").setLevel(logging.WARNING)
+    logging.getLogger("torch").setLevel(logging.WARNING)
 
-    elif mode == "eval":
+    # Read parameters from the config file.
+    if config is None:
+        config = os.path.join(
+            os.path.dirname(os.path.realpath(__file__)), "config.yaml"
+        )
+    with open(config) as f_in:
+        config = yaml.safe_load(f_in)
 
-        logging.info("Evaluating Casanovo...")
-        evaluate(test_data_path, model_path, config)
+    # Overwrite any parameters that were provided as command-line arguments.
+    if num_workers is not None:
+        config["num_workers"] = num_workers
 
-    elif mode == "denovo":
+    pl.utilities.seed.seed_everything(seed=config["random_seed"], workers=True)
 
-        logging.info("De novo sequencing with Casanovo...")
-        denovo(test_data_path, model_path, config, output_path)
+    # Log the active configuration.
+    logger.info("Casanovo version %s", str(__version__))
+    for key, value in config.items():
+        logger.debug("%s = %s", str(key), str(value))
 
-    pass
+    # Run Casanovo in the specified mode.
+    if mode == "denovo":
+        logger.info("Predict peptide sequences with Casanovo.")
+        model_runner.predict(peak_dir, model, output, config)
+    elif mode == "eval":
+        logger.info("Evaluate a trained Casanovo model.")
+        model_runner.evaluate(peak_dir, model, config)
+    elif mode == "train":
+        logger.info("Train the Casanovo model.")
+        model_runner.train(peak_dir, peak_dir_val, model, config)
 
 
 if __name__ == "__main__":
diff --git a/casanovo/config.yaml b/casanovo/config.yaml
index 35fff0ca..030d1c4c 100644
--- a/casanovo/config.yaml
+++ b/casanovo/config.yaml
@@ -1,44 +1,29 @@
 ###
-# Config file for casanovo. Edit this to change parameters for train, eval, and denovo.
-# Note that blank entries are interpreted as "None"
+# Casanovo configuration.
+# Blank entries are interpreted as "None"
 ###
 
-#Random seed to be used across the pipeline
+# Random seed to ensure reproducible results.
 random_seed: 454
 
-#Train data options
-train_annot_spec_idx_path: casanovo_train.hdf5 #path to write the training data index file, starts at os.getcwd()
-train_spec_idx_overwrite: True
-
-#Validation data options
-val_annot_spec_idx_path: casanovo_val.hdf5 #path to write the validation data index file, starts at os.getcwd()
-val_spec_idx_overwrite: True
-
-#Test data options
-test_annot_spec_idx_path: casanovo_test.hdf5 #path to write the test data index file
-test_spec_idx_overwrite: True
-
-#Preprocessing parameters
-preprocess_spec: True #Should be True when using raw mass spec data. Use False when reproducing Casanovo results with the provided benchmark data set which is pre-processed
+# Spectrum processing options.
 n_peaks: 150
-min_mz: 50.52564895 #1.0005079 * 50.5 
-max_mz: 2500
+min_mz: 50.52564895  # 1.0005079 * 50.5
+max_mz: 2500.0
 min_intensity: 0.01
-fragment_tol_mass: 2  # Da
-precursor_mass_tol: 50  # ppm
+remove_precursor_tol: 2.0  # Da
 
-#Hardware options
-num_workers: 1
-gpus: [] #[] for CPU, int list (ex. [0, 1, ...]) to specify GPUs
+# Hardware options.
+num_workers:
 
-#Model options
+# Model architecture options.
 dim_model: 512
 n_head: 8
 dim_feedforward: 1024
 n_layers: 9
 dropout: 0
-dim_intensity: 
-custom_encoder: 
+dim_intensity:
+custom_encoder:
 max_length: 100
 residues:
   G: 57.021463735
@@ -47,7 +32,7 @@ residues:
   P: 97.052763875
   V: 99.068413945
   T: 101.047678505
-  C+57.021: 160.030644505 #103.009184505 + 57.02146
+  C+57.021: 160.030644505 # 103.009184505 + 57.02146
   L: 113.084064015
   I: 113.084064015
   N: 114.042927470
@@ -61,31 +46,30 @@ residues:
   R: 156.101111050
   Y: 163.063328575
   W: 186.079312980
-  # AA mods:
-  M+15.995: 147.035399645 #131.040484645 + 15.994915  # Met Oxidation
-  N+0.984: 115.02694347 #114.042927470 + 0.984016,  # Asn Deamidation
-  Q+0.984: 129.04259354 #128.058577540 + 0.984016,  # Gln Deamidation
+  # Amino acid modifications.
+  M+15.995: 147.035399645 # Met oxidation: 131.040484645 + 15.994915
+  N+0.984: 115.02694347   # Asn deamidation: 114.042927470 + 0.984016
+  Q+0.984: 129.04259354   # Gln deamidation: 128.058577540 + 0.984016
 max_charge: 10
 n_log: 1
-tb_summarywriter: 
-warmup_iters: 100000
-max_iters: 600000
+tb_summarywriter:
+warmup_iters: 100_000
+max_iters: 600_000
 learning_rate: 5e-4
 weight_decay: 1e-5
 
-#Training/inference options
+# Training/inference options.
 train_batch_size: 32
-val_batch_size: 1024
-test_batch_size: 1024
+predict_batch_size: 1024
 
-accelerator: "ddp"
-logger: 
+logger:
 max_epochs: 30
 num_sanity_val_steps: 0
+strategy: "ddp"
 
 train_from_scratch: True
 
-save_model: False
-model_save_folder_path: ''
+save_model: True
+model_save_folder_path: ""
 save_weights_only: True
-every_n_epochs: 1
+every_n_train_steps: 50_000
diff --git a/casanovo/data/__init__.py b/casanovo/data/__init__.py
index 8d62cf78..e69de29b 100644
--- a/casanovo/data/__init__.py
+++ b/casanovo/data/__init__.py
@@ -1,5 +0,0 @@
-"""The Pytorch Datasets"""
-from .datasets import (
-    SpectrumDataset,
-    AnnotatedSpectrumDataset,
-)
diff --git a/casanovo/data/datasets.py b/casanovo/data/datasets.py
index 4550e425..dcd9fd59 100644
--- a/casanovo/data/datasets.py
+++ b/casanovo/data/datasets.py
@@ -1,68 +1,49 @@
 """A PyTorch Dataset class for annotated spectra."""
-import math
+from typing import Optional, Tuple
 
-import torch
+import depthcharge
 import numpy as np
-from torch.utils.data import Dataset, IterableDataset
+import spectrum_utils.spectrum as sus
+import torch
+from torch.utils.data import Dataset
 
 
 class SpectrumDataset(Dataset):
-    """Parse and retrieve collections of mass spectra.
+    """
+    Parse and retrieve collections of MS/MS spectra.
 
     Parameters
     ----------
     spectrum_index : depthcharge.data.SpectrumIndex
-        The collection of spectra to use as a dataset.
-    n_peaks : int, optional
-        Keep only the top-n most intense peaks in any spectrum. ``None``
-        retains all of the peaks.
-    min_mz : float, optional
-        The minimum m/z to include. The default is 140 m/z, in order to
-        exclude TMT and iTRAQ reporter ions.
-    max_mz : float, optional
-        The maximum m/z to include.
-    min_intensity : float, optional
-        Remove peaks whose intensity is below `min_intensity` percentage
-        of the intensity of the most intense peak
-    fragment_tol_mass : float, optional
-        Fragment mass tolerance around the precursor mass in Da to remove the
-        precursor peak.
-    random_state : int or RandomState, optional.
-        The numpy random state. ``None`` leaves mass spectra in the order
-        they were parsed.
-    preprocess_spec : bool, optional
-        Preprocess the provided spectra
-
-    Attributes
-    ----------
-    n_peaks : int
-        The maximum number of mass speak to consider for each mass spectrum.
+        The MS/MS spectra to use as a dataset.
+    n_peaks : Optional[int]
+        The number of top-n most intense peaks to keep in each spectrum. `None`
+        retains all peaks.
     min_mz : float
-        The minimum m/z to consider for each mass spectrum.
+        The minimum m/z to include. The default is 140 m/z, in order to exclude
+        TMT and iTRAQ reporter ions.
     max_mz : float
         The maximum m/z to include.
     min_intensity : float
-        Remove peaks whose intensity is below `min_intensity` percentage
-        of the intensity of the most intense peak
-    fragment_tol_mass : float
-        Fragment mass tolerance around the precursor mass in Da to remove the
-        precursor peak.
-    n_spectra : int
-    index : depthcharge.data.SpectrumIndex
-    rng : numpy.random.Generator
-    preprocess_spec : bool
+        Remove peaks whose intensity is below `min_intensity` percentage of the
+        base peak intensity.
+    remove_precursor_tol : float
+        Remove peaks within the given mass tolerance in Dalton around the
+        precursor mass.
+    random_state : Optional[int]
+        The NumPy random state. ``None`` leaves mass spectra in the order they
+        were parsed.
     """
 
     def __init__(
         self,
-        spectrum_index,
-        n_peaks=200,
-        min_mz=140,
-        max_mz=2500,
-        min_intensity=0.01,
-        fragment_tol_mass=2,
-        random_state=None,
-        preprocess_spec=False,
+        spectrum_index: depthcharge.data.SpectrumIndex,
+        n_peaks: int = 150,
+        min_mz: float = 140.0,
+        max_mz: float = 2500.0,
+        min_intensity: float = 0.01,
+        remove_precursor_tol: float = 2.0,
+        random_state: Optional[int] = None,
     ):
         """Initialize a SpectrumDataset"""
         super().__init__()
@@ -70,304 +51,190 @@ def __init__(
         self.min_mz = min_mz
         self.max_mz = max_mz
         self.min_intensity = min_intensity
-        self.fragment_tol_mass = fragment_tol_mass
+        self.remove_precursor_tol = remove_precursor_tol
         self.rng = np.random.default_rng(random_state)
         self._index = spectrum_index
-        self.preprocess_spec = preprocess_spec
 
-    def __len__(self):
+    def __len__(self) -> int:
         """The number of spectra."""
         return self.n_spectra
 
-    def __getitem__(self, idx):
-        """Return a single mass spectrum.
+    def __getitem__(self, idx) -> Tuple[torch.Tensor, float, int, str]:
+        """
+        Return the MS/MS spectrum with the given index.
 
         Parameters
         ----------
         idx : int
-            The index to return.
+            The index of the spectrum to return.
 
         Returns
         -------
         spectrum : torch.Tensor of shape (n_peaks, 2)
-            The mass spectrum where ``spectrum[:, 0]`` are the m/z values and
-            ``spectrum[:, 1]`` are their associated intensities.
+            A tensor of the spectrum with the m/z and intensity peak values.
         precursor_mz : float
-            The m/z of the precursor.
+            The precursor m/z.
         precursor_charge : int
-            The charge of the precursor.
-        spectrum_order_id: str
-            The unique identifier for spectrum based on its order in the original mgf file
+            The precursor charge.
+        spectrum_id: str
+            The unique spectrum identifier, as determined by its index in the
+            original peak file.
         """
-        mz_array, int_array, prec_mz, prec_charge = self.index[idx]
-
-        if self.preprocess_spec == True:
-            spec = self._process_peaks(
-                mz_array, int_array, prec_mz, prec_charge
-            )
-        else:
-            spec = torch.tensor(np.array([mz_array, int_array])).T.float()
-
-        if not spec.sum():
-            spec = torch.tensor([[0, 1]]).float()
-
-        spectrum_order_id = f"{idx}"
-
-        return spec, prec_mz, prec_charge, spectrum_order_id
+        mz_array, int_array, precursor_mz, precursor_charge = self.index[idx]
+        spectrum = self._process_peaks(
+            mz_array, int_array, precursor_mz, precursor_charge
+        )
+        return spectrum, precursor_mz, precursor_charge, str(idx)
 
-    def _get_non_precursor_peak_mask(
+    def _process_peaks(
         self,
-        mz: np.ndarray,
-        pep_mass: float,
-        max_charge: int,
-        fragment_tol_mass: float,
-    ):
-        """
-        Get a mask to remove peaks that are close to the precursor mass peak (at
-        different charges and isotopes).
-        ----------
-        mz : np.ndarray
-            The mass-to-charge ratios of the spectrum fragment peaks.
-        pep_mass : float
-            The mono-isotopic mass of the uncharged peptide.
-        max_charge : int
-            The maximum precursor loss charge.
-        fragment_tol_mass : float
-                Fragment mass tolerance around the precursor mass to remove the
-                precursor peak (Da).
-
-        Returns
-        -------
-        np.ndarray
-            Index mask specifying which peaks are retained after precursor peak
-            filtering.
-        """
-        isotope = 0
-        remove_mz = []
-        for charge in range(max_charge, 0, -1):
-            for iso in range(isotope + 1):
-                remove_mz.append((pep_mass + iso) / charge + 1.0072766)
-
-        mask = np.full_like(mz, True, np.bool_)
-        mz_i = remove_i = 0
-        while mz_i < len(mz) and remove_i < len(remove_mz):
-            md = mz[mz_i] - remove_mz[remove_i]  # in Da
-            if md < -fragment_tol_mass:
-                mz_i += 1
-            elif md > fragment_tol_mass:
-                remove_i += 1
-            else:
-                mask[mz_i] = False
-                mz_i += 1
-
-        return mask
-
-    def _get_filter_intensity_mask(
-        self, intensity, min_intensity, max_num_peaks
-    ):
+        mz_array: np.ndarray,
+        int_array: np.ndarray,
+        precursor_mz: float,
+        precursor_charge: int,
+    ) -> torch.Tensor:
         """
-        Get a mask to remove low-intensity peaks and retain only the given number
-        of most intense peaks.
-
-        Parameters
-        ----------
-        intensity : np.ndarray
-            The intensities of the spectrum fragment peaks.
-        min_intensity : float
-            Remove peaks whose intensity is below `min_intensity` percentage of the
-            intensity of the most intense peak.
-        max_num_peaks : int
-            Only retain the `max_num_peaks` most intense peaks.
-        Returns
-        -------
-        np.ndarray
-            Index mask specifying which peaks are retained after filtering the at
-            most `max_num_peaks` most intense intensities above the minimum
-            intensity threshold.
-        """
-        intensity_idx = np.argsort(intensity)
-        min_intensity *= intensity[intensity_idx[-1]]
-        # Discard low-intensity noise peaks.
-        start_i = 0
-        for intens in intensity[intensity_idx]:
-            if intens > min_intensity:
-                break
-            start_i += 1
-        # Only retain at most the `max_num_peaks` most intense peaks.
-        mask = np.full_like(intensity, False, np.bool_)
-        mask[
-            intensity_idx[max(start_i, len(intensity_idx) - max_num_peaks) :]
-        ] = True
-        return mask
-
-    def _process_peaks(self, mz_array, int_array, prec_mz, prec_charge):
-        """Choose the top n peaks and normalize the spectrum intensities.
+        Preprocess the spectrum by removing noise peaks and scaling the peak
+        intensities.
 
         Parameters
         ----------
         mz_array : numpy.ndarray of shape (n_peaks,)
-            The m/z values of the peaks in the spectrum.
+            The spectrum peak m/z values.
         int_array : numpy.ndarray of shape (n_peaks,)
-            The intensity values of the peaks in the spectrum.
+            The spectrum peak intensity values.
         precursor_mz : float
-            The m/z of the precursor.
+            The precursor m/z.
         precursor_charge : int
-            The charge of the precursor.
+            The precursor charge.
 
         Returns
         -------
         torch.Tensor of shape (n_peaks, 2)
-            The mass spectrum where ``spectrum[:, 0]`` are the m/z values and
-            ``spectrum[:, 1]`` are their associated intensities.
+            A tensor of the spectrum with the m/z and intensity peak values.
         """
-        # Set m/z range for fragments
-        if self.min_mz is not None:
-            keep = (mz_array >= self.min_mz) & (mz_array <= self.max_mz)
-            mz_array = mz_array[keep]
-            int_array = int_array[keep]
-
-        # Remove fragment peak(s) close to the precursor m/z
-        neutral_mass = (prec_mz - 1.0072766) * prec_charge
-        peak_mask = self._get_non_precursor_peak_mask(
-            mz_array,
-            neutral_mass,
-            prec_charge,
-            self.fragment_tol_mass,
+        spectrum = sus.MsmsSpectrum(
+            "",
+            precursor_mz,
+            precursor_charge,
+            mz_array.astype(np.float64),
+            int_array.astype(np.float32),
         )
-        mz_array = mz_array[peak_mask]
-        int_array = int_array[peak_mask]
-
-        # Remove low-intensity fragment peaks and keep a maximum pre-specified number of peaks
-        top_p = self._get_filter_intensity_mask(
-            intensity=int_array,
-            min_intensity=self.min_intensity,
-            max_num_peaks=self.n_peaks,
-        )
-        mz_array = mz_array[top_p]
-        int_array = int_array[top_p]
-
-        # Square root normalize the peak intensities
-        int_array = np.sqrt(int_array)
-        int_array = int_array / np.linalg.norm(int_array)
-
-        return torch.tensor(np.array([mz_array, int_array])).T.float()
+        try:
+            spectrum.set_mz_range(self.min_mz, self.max_mz)
+            if len(spectrum.mz) == 0:
+                raise ValueError
+            spectrum.remove_precursor_peak(self.remove_precursor_tol, "Da")
+            if len(spectrum.mz) == 0:
+                raise ValueError
+            spectrum.filter_intensity(self.min_intensity, self.n_peaks)
+            if len(spectrum.mz) == 0:
+                raise ValueError
+            spectrum.scale_intensity("root", 1)
+            intensities = spectrum.intensity / np.linalg.norm(
+                spectrum.intensity
+            )
+            return torch.tensor(np.array([spectrum.mz, intensities])).T.float()
+        except ValueError:
+            # Replace invalid spectra by a dummy spectrum.
+            return torch.tensor([[0, 1]]).float()
 
     @property
-    def n_spectra(self):
+    def n_spectra(self) -> int:
         """The total number of spectra."""
         return self.index.n_spectra
 
     @property
-    def index(self):
-        """The underyling SpectrumIndex."""
+    def index(self) -> depthcharge.data.SpectrumIndex:
+        """The underlying SpectrumIndex."""
         return self._index
 
     @property
     def rng(self):
-        """The numpy random number generator."""
+        """The NumPy random number generator."""
         return self._rng
 
     @rng.setter
     def rng(self, seed):
-        """Set the numpy random number generator."""
+        """Set the NumPy random number generator."""
         self._rng = np.random.default_rng(seed)
 
 
 class AnnotatedSpectrumDataset(SpectrumDataset):
-    """Parse and retrieve collections of mass spectra
+    """
+    Parse and retrieve collections of annotated MS/MS spectra.
 
     Parameters
     ----------
     annotated_spectrum_index : depthcharge.data.SpectrumIndex
-        The collection of annotated mass spectra to use as a dataset.
-    n_peaks : int, optional
-        Keep only the top-n most intense peaks in any spectrum. ``None``
-        retains all of the peaks.
-    min_mz : float, optional
-        The minimum m/z to include. The default is 140 m/z, in order to
-        exclude TMT and iTRAQ reporter ions.
-    max_mz : float
-        The maximum m/z to include.
-    min_intensity : float
-        Remove peaks whose intensity is below `min_intensity` percentage
-        of the intensity of the most intense peak
-    fragment_tol_mass : float
-        Fragment mass tolerance around the precursor mass in Da to remove the
-        precursor peak.
-    random_state : int or RandomState, optional.
-        The numpy random state. ``None`` leaves mass spectra in the order
-        they were parsed.
-    preprocess_spec : bool, optional
-        Preprocess the provided spectra
-
-    Attributes
-    ----------
-    n_peaks : int
-        The maximum number of mass speak to consider for each mass spectrum.
+        The MS/MS spectra to use as a dataset.
+    n_peaks : Optional[int]
+        The number of top-n most intense peaks to keep in each spectrum. `None`
+        retains all peaks.
     min_mz : float
-        The minimum m/z to consider for each mass spectrum.
+        The minimum m/z to include. The default is 140 m/z, in order to exclude
+        TMT and iTRAQ reporter ions.
     max_mz : float
         The maximum m/z to include.
     min_intensity : float
-        Remove peaks whose intensity is below `min_intensity` percentage
-        of the intensity of the most intense peak
-    fragment_tol_mass : float
-        Fragment mass tolerance around the precursor mass in Da to remove the
-        precursor peak.
-    n_spectra : int
-    index : depthcharge.data.SpectrumIndex
-    rng : numpy.random.Generator
-    preprocess_spec : bool
+        Remove peaks whose intensity is below `min_intensity` percentage of the
+        base peak intensity.
+    remove_precursor_tol : float
+        Remove peaks within the given mass tolerance in Dalton around the
+        precursor mass.
+    random_state : Optional[int]
+        The NumPy random state. ``None`` leaves mass spectra in the order they
+        were parsed.
     """
 
     def __init__(
         self,
-        annotated_spectrum_index,
-        n_peaks=200,
-        min_mz=140,
-        max_mz=2500,
-        min_intensity=0.01,
-        fragment_tol_mass=2,
-        random_state=None,
-        preprocess_spec=False,
+        annotated_spectrum_index: depthcharge.data.SpectrumIndex,
+        n_peaks: int = 150,
+        min_mz: float = 140.0,
+        max_mz: float = 2500.0,
+        min_intensity: float = 0.01,
+        remove_precursor_tol: float = 2.0,
+        random_state: Optional[int] = None,
     ):
-        """Initialize an AnnotatedSpectrumDataset"""
         super().__init__(
             annotated_spectrum_index,
             n_peaks=n_peaks,
             min_mz=min_mz,
             max_mz=max_mz,
             min_intensity=min_intensity,
-            fragment_tol_mass=fragment_tol_mass,
+            remove_precursor_tol=remove_precursor_tol,
             random_state=random_state,
-            preprocess_spec=preprocess_spec,
         )
 
-    def __getitem__(self, idx):
-        """Return a single annotated mass spectrum.
+    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, float, int, str]:
+        """
+        Return the annotated MS/MS spectrum with the given index.
 
         Parameters
         ----------
         idx : int
-            The index to return.
+            The index of the spectrum to return.
 
         Returns
         -------
         spectrum : torch.Tensor of shape (n_peaks, 2)
-            The mass spectrum where ``spectrum[:, 0]`` are the m/z values and
-            ``spectrum[:, 1]`` are their associated intensities.
+            A tensor of the spectrum with the m/z and intensity peak values.
         precursor_mz : float
-            The m/z of the precursor.
+            The precursor m/z.
         precursor_charge : int
-            The charge of the precursor.
+            The precursor charge.
         annotation : str
-            The annotation for the mass spectrum.
+            The peptide annotation of the spectrum.
         """
-        mz_array, int_array, prec_mz, prec_charge, pep = self.index[idx]
-        if self.preprocess_spec == True:
-            spec = self._process_peaks(
-                mz_array, int_array, prec_mz, prec_charge
-            )
-        else:
-            spec = torch.tensor(np.array([mz_array, int_array])).T.float()
-        return spec, prec_mz, prec_charge, pep
+        (
+            mz_array,
+            int_array,
+            precursor_mz,
+            precursor_charge,
+            peptide,
+        ) = self.index[idx]
+        spectrum = self._process_peaks(
+            mz_array, int_array, precursor_mz, precursor_charge
+        )
+        return spectrum, precursor_mz, precursor_charge, peptide
diff --git a/casanovo/denovo/__init__.py b/casanovo/denovo/__init__.py
index a90f7d00..e69de29b 100644
--- a/casanovo/denovo/__init__.py
+++ b/casanovo/denovo/__init__.py
@@ -1,4 +0,0 @@
-"""The de novo sequencing model"""
-from .model import Spec2Pep
-from .dataloaders import DeNovoDataModule
-from .train_test import train, denovo, evaluate
diff --git a/casanovo/denovo/dataloaders.py b/casanovo/denovo/dataloaders.py
index a96ebba1..28e481ca 100644
--- a/casanovo/denovo/dataloaders.py
+++ b/casanovo/denovo/dataloaders.py
@@ -1,68 +1,66 @@
-"""Data loaders for the de novo sequencing task.
-
-This module also ensure consistent train, validation, and test splits.
-"""
+"""Data loaders for the de novo sequencing task."""
+import functools
 import os
-from functools import partial
+from typing import List, Optional, Tuple
 
-import torch
 import numpy as np
 import pytorch_lightning as pl
+import torch
+from depthcharge.data import AnnotatedSpectrumIndex
 
-from ..data import AnnotatedSpectrumDataset, SpectrumDataset
+from ..data.datasets import AnnotatedSpectrumDataset, SpectrumDataset
 
 
 class DeNovoDataModule(pl.LightningDataModule):
-    """Prepare data for a Spec2Pep.
+    """
+    Data loader to prepare MS/MS spectra for a Spec2Pep predictor.
 
     Parameters
     ----------
-    train_index : AnnotatedSpectrumIndex
-        The spectrum index file for training.
-    valid_index : AnnotatedSpectrumIndex
-        The spectrum index file for validation.
-    test_index : AnnotatedSpectrumIndex
-        The spectrum index file for testing.
-    batch_size : int, optional
-        The batch size to use for training and evaluations
-    n_peaks : int, optional
-        Keep only the top-n most intense peaks in any spectrum. ``None``
-        retains all of the peaks.
-    min_mz : float, optional
-        The minimum m/z to include. The default is 140 m/z, in order to
-        exclude TMT and iTRAQ reporter ions.
-    max_mz : float, optional
+    train_index : Optional[AnnotatedSpectrumIndex]
+        The spectrum index file corresponding to the training data.
+    valid_index : Optional[AnnotatedSpectrumIndex]
+        The spectrum index file corresponding to the validation data.
+    test_index : Optional[AnnotatedSpectrumIndex]
+        The spectrum index file corresponding to the testing data.
+    batch_size : int
+        The batch size to use for training and evaluating.
+    n_peaks : Optional[int]
+        The number of top-n most intense peaks to keep in each spectrum. `None`
+        retains all peaks.
+    min_mz : float
+        The minimum m/z to include. The default is 140 m/z, in order to exclude
+        TMT and iTRAQ reporter ions.
+    max_mz : float
         The maximum m/z to include.
-    min_intensity : float, optional
-        Remove peaks whose intensity is below `min_intensity` percentage
-        of the intensity of the most intense peak
-    fragment_tol_mass : float, optional
-        Fragment mass tolerance around the precursor mass in Da to remove the
-        precursor peak.
-    num_workers : int, optional
-        The number of workers to use for data loading. By default, the number
-        of available CPU cores on the current machine is used.
-    random_state : int or Generator, optional.
-        The numpy random state. ``None`` leaves mass spectra in the order
-        they were parsed.
+    min_intensity : float
+        Remove peaks whose intensity is below `min_intensity` percentage of the
+        base peak intensity.
+    remove_precursor_tol : float
+        Remove peaks within the given mass tolerance in Dalton around the
+        precursor mass.
+    n_workers : int, optional
+        The number of workers to use for data loading. By default, the number of
+        available CPU cores on the current machine is used.
+    random_state : Optional[int]
+        The NumPy random state. ``None`` leaves mass spectra in the order they
+        were parsed.
     """
 
     def __init__(
         self,
-        train_index=None,
-        valid_index=None,
-        test_index=None,
-        batch_size=128,
-        n_peaks=200,
-        min_mz=140,
-        max_mz=2500,
-        min_intensity=0.01,
-        fragment_tol_mass=2,
-        num_workers=None,
-        random_state=None,
-        preprocess_spec=False,
+        train_index: Optional[AnnotatedSpectrumIndex] = None,
+        valid_index: Optional[AnnotatedSpectrumIndex] = None,
+        test_index: Optional[AnnotatedSpectrumIndex] = None,
+        batch_size: int = 128,
+        n_peaks: Optional[int] = 150,
+        min_mz: float = 140.0,
+        max_mz: float = 2500.0,
+        min_intensity: float = 0.01,
+        remove_precursor_tol: float = 2.0,
+        n_workers: Optional[int] = None,
+        random_state: Optional[int] = None,
     ):
-        """Initialize the PairedSpectrumDataModule."""
         super().__init__()
         self.train_index = train_index
         self.valid_index = valid_index
@@ -72,38 +70,34 @@ def __init__(
         self.min_mz = min_mz
         self.max_mz = max_mz
         self.min_intensity = min_intensity
-        self.fragment_tol_mass = fragment_tol_mass
-        self.num_workers = num_workers
+        self.remove_precursor_tol = remove_precursor_tol
+        self.n_workers = n_workers if n_workers is not None else os.cpu_count()
         self.rng = np.random.default_rng(random_state)
-        self.preprocess_spec = preprocess_spec
         self.train_dataset = None
         self.valid_dataset = None
         self.test_dataset = None
 
-        if self.num_workers is None:
-            self.num_workers = os.cpu_count()
-
-    def setup(self, stage=None, annotated=True):
-        """Set up the PyTorch Datasets.
+    def setup(self, stage: str = None, annotated: bool = True) -> None:
+        """
+        Set up the PyTorch Datasets.
 
         Parameters
         ----------
         stage : str {"fit", "validate", "test"}
-            The stage indicating which Datasets to prepare. All are prepared
-            by default.
+            The stage indicating which Datasets to prepare. All are prepared by
+            default.
         annotated: bool
-            True if peptide sequence annotations available for test data
+            True if peptide sequence annotations are available for the test
+            data.
         """
-
         if stage in (None, "fit", "validate"):
-            make_dataset = partial(
+            make_dataset = functools.partial(
                 AnnotatedSpectrumDataset,
                 n_peaks=self.n_peaks,
                 min_mz=self.min_mz,
                 max_mz=self.max_mz,
                 min_intensity=self.min_intensity,
-                fragment_tol_mass=self.fragment_tol_mass,
-                preprocess_spec=self.preprocess_spec,
+                remove_precursor_tol=self.remove_precursor_tol,
             )
             if self.train_index is not None:
                 self.train_dataset = make_dataset(
@@ -112,33 +106,23 @@ def setup(self, stage=None, annotated=True):
                 )
             if self.valid_index is not None:
                 self.valid_dataset = make_dataset(self.valid_index)
-
         if stage in (None, "test"):
-            if annotated == True:
-                make_dataset = partial(
-                    AnnotatedSpectrumDataset,
-                    n_peaks=self.n_peaks,
-                    min_mz=self.min_mz,
-                    max_mz=self.max_mz,
-                    min_intensity=self.min_intensity,
-                    fragment_tol_mass=self.fragment_tol_mass,
-                    preprocess_spec=self.preprocess_spec,
-                )
-            else:
-                make_dataset = partial(
-                    SpectrumDataset,
-                    n_peaks=self.n_peaks,
-                    min_mz=self.min_mz,
-                    max_mz=self.max_mz,
-                    min_intensity=self.min_intensity,
-                    fragment_tol_mass=self.fragment_tol_mass,
-                    preprocess_spec=self.preprocess_spec,
-                )
+            make_dataset = functools.partial(
+                AnnotatedSpectrumDataset if annotated else SpectrumDataset,
+                n_peaks=self.n_peaks,
+                min_mz=self.min_mz,
+                max_mz=self.max_mz,
+                min_intensity=self.min_intensity,
+                remove_precursor_tol=self.remove_precursor_tol,
+            )
             if self.test_index is not None:
                 self.test_dataset = make_dataset(self.test_index)
 
-    def _make_loader(self, dataset):
-        """Create a PyTorch DataLoader.
+    def _make_loader(
+        self, dataset: torch.utils.data.Dataset
+    ) -> torch.utils.data.DataLoader:
+        """
+        Create a PyTorch DataLoader.
 
         Parameters
         ----------
@@ -155,47 +139,60 @@ def _make_loader(self, dataset):
             batch_size=self.batch_size,
             collate_fn=prepare_batch,
             pin_memory=True,
-            num_workers=self.num_workers,
+            num_workers=self.n_workers,
         )
 
-    def train_dataloader(self):
+    def train_dataloader(self) -> torch.utils.data.DataLoader:
         """Get the training DataLoader."""
         return self._make_loader(self.train_dataset)
 
-    def val_dataloader(self):
+    def val_dataloader(self) -> torch.utils.data.DataLoader:
         """Get the validation DataLoader."""
         return self._make_loader(self.valid_dataset)
 
-    def test_dataloader(self):
+    def test_dataloader(self) -> torch.utils.data.DataLoader:
         """Get the test DataLoader."""
         return self._make_loader(self.test_dataset)
 
+    def predict_dataloader(self) -> torch.utils.data.DataLoader:
+        """Get the predict DataLoader."""
+        return self._make_loader(self.test_dataset)
+
 
-def prepare_batch(batch):
-    """This is the collate function
+def prepare_batch(
+    batch: List[Tuple[torch.Tensor, float, int, str]]
+) -> Tuple[torch.Tensor, torch.Tensor, np.ndarray]:
+    """
+    Collate MS/MS spectra into a batch.
 
-    The mass spectra must be padded so that they fit nicely as a tensor.
+    The MS/MS spectra will be padded so that they fit nicely as a tensor.
     However, the padded elements are ignored during the subsequent steps.
 
     Parameters
     ----------
-    batch : tuple of tuple of torch.Tensor
-        A batch of data from an AnnotatedSpectrumDataset.
+    batch : List[Tuple[torch.Tensor, float, int, str]]
+        A batch of data from an AnnotatedSpectrumDataset, consisting of for each
+        spectrum (i) a tensor with the m/z and intensity peak values, (ii), the
+        precursor m/z, (iii) the precursor charge, (iv) the spectrum identifier.
 
     Returns
     -------
     spectra : torch.Tensor of shape (batch_size, n_peaks, 2)
-        The mass spectra to sequence, where ``X[:, :, 0]`` are the m/z values
-        and ``X[:, :, 1]`` are their associated intensities.
+        The padded mass spectra tensor with the m/z and intensity peak values
+        for each spectrum.
     precursors : torch.Tensor of shape (batch_size, 3)
-        The precursor mass, precursor charge, and precursor m/z.
-    sequence_or_ids : list of str
-        The peptide sequence annotations in training, the spectrum identifier in de novo sequencing
+        A tensor with the precursor neutral mass, precursor charge, and
+        precursor m/z.
+    spectrum_ids : np.ndarray
+        The spectrum identifiers (during de novo sequencing) or peptide
+        sequences (during training).
     """
-    spec, mz, charge, sequence_or_ids = list(zip(*batch))
-    charge = torch.tensor(charge)
-    mz = torch.tensor(mz)
-    mass = (mz - 1.007276) * charge
-    precursors = torch.vstack([mass, charge, mz]).T.float()
-    spec = torch.nn.utils.rnn.pad_sequence(spec, batch_first=True)
-    return spec, precursors, np.array(sequence_or_ids)
+    spectra, precursor_mzs, precursor_charges, spectrum_ids = list(zip(*batch))
+    spectra = torch.nn.utils.rnn.pad_sequence(spectra, batch_first=True)
+    precursor_mzs = torch.tensor(precursor_mzs)
+    precursor_charges = torch.tensor(precursor_charges)
+    precursor_masses = (precursor_mzs - 1.007276) * precursor_charges
+    precursors = torch.vstack(
+        [precursor_masses, precursor_charges, precursor_mzs]
+    ).T.float()
+    return spectra, precursors, np.asarray(spectrum_ids)
diff --git a/casanovo/denovo/evaluate.py b/casanovo/denovo/evaluate.py
index caba1f68..28de5bb7 100644
--- a/casanovo/denovo/evaluate.py
+++ b/casanovo/denovo/evaluate.py
@@ -1,347 +1,310 @@
+"""Methods to evaluate peptide-spectrum predictions."""
 import re
-from spectrum_utils.utils import mass_diff
-import numpy as np
+from typing import Dict, List, Tuple
 
-from depthcharge.masses import PeptideMass
+import numpy as np
+from spectrum_utils.utils import mass_diff
 
 
-def best_aa_match(orig_seq, pred_seq, aa_dict):
+def aa_match_prefix(
+    peptide1: List[str],
+    peptide2: List[str],
+    aa_dict: Dict[str, float],
+    cum_mass_threshold: float = 0.5,
+    ind_mass_threshold: float = 0.1,
+) -> Tuple[np.ndarray, bool]:
     """
-    Find the matching amino acids of an original and predicted peptide sequence if either their prefix or suffix match
+    Find the matching prefix amino acids between two peptide sequences.
+
+    This is a similar evaluation criterion as used by DeepNovo.
 
     Parameters
     ----------
-    orig_seq : list
-        List of amino acids in the original peptide
-    pred_seq : list
-        List of amino acids in the predicted peptide
-    aa_dict: dict
-        Dictionary of amino acid masses
+    peptide1 : List[str]
+        The first tokenized peptide sequence to be compared.
+    peptide2 : List[str]
+        The second tokenized peptide sequence to be compared.
+    aa_dict : Dict[str, float]
+        Mapping of amino acid tokens to their mass values.
+    cum_mass_threshold : float
+        Mass threshold in Dalton to accept cumulative mass-matching amino acid
+        sequences.
+    ind_mass_threshold : float
+        Mass threshold in Dalton to accept individual mass-matching amino acids.
 
     Returns
     -------
-    aa_match: list
-        Binary list of matches over predicted peptide
-    pep_match: int
-        1 if all amino acid in two sequences match
-
+    aa_matches : np.ndarray of length max(len(peptide1), len(peptide2))
+        Boolean flag indicating whether each paired-up amino acid matches across
+        both peptide sequences.
+    pep_match : bool
+        Boolean flag to indicate whether the two peptide sequences fully match.
     """
-
-    cum_aa_threshold = 0.5
-    single_aa_threshold = 0.1
-
-    aa_match = []
-    pep_match = 0
-
-    cnt_pred = 0
-    cnt_orig = 0
-
-    orig_aa_mass_list = [aa_dict[aa] for aa in orig_seq]
-    pred_aa_mass_list = [aa_dict[aa] for aa in pred_seq]
-
-    orig_prefix_mass_list = [0] + list(np.cumsum(orig_aa_mass_list))[:-1]
-    orig_suffix_mass_list = list(
-        reversed(np.cumsum(list(reversed(orig_aa_mass_list)))[:-1])
-    ) + [0]
-
-    pred_prefix_mass_list = [0] + list(np.cumsum(pred_aa_mass_list))[:-1]
-    pred_suffix_mass_list = list(
-        reversed(np.cumsum(list(reversed(pred_aa_mass_list)))[:-1])
-    ) + [0]
-
-    while cnt_pred < len(pred_seq) and cnt_orig < len(orig_seq):
-
-        pred_aa_mass = aa_dict[pred_seq[cnt_pred]]
-        orig_aa_mass = aa_dict[orig_seq[cnt_orig]]
-
+    aa_matches = np.zeros(max(len(peptide1), len(peptide2)), np.bool_)
+    # Find longest mass-matching prefix.
+    i1, i2, cum_mass1, cum_mass2 = 0, 0, 0.0, 0.0
+    while i1 < len(peptide1) and i2 < len(peptide2):
+        aa_mass1, aa_mass2 = aa_dict[peptide1[i1]], aa_dict[peptide2[i2]]
         if (
-            abs(
-                mass_diff(
-                    pred_prefix_mass_list[cnt_pred],
-                    orig_prefix_mass_list[cnt_orig],
-                    mode_is_da=True,
-                )
-            )
-            < cum_aa_threshold
-            or abs(
-                mass_diff(
-                    pred_suffix_mass_list[cnt_pred],
-                    orig_suffix_mass_list[cnt_orig],
-                    mode_is_da=True,
-                )
-            )
-            < cum_aa_threshold
+            abs(mass_diff(cum_mass1 + aa_mass1, cum_mass2 + aa_mass2, True))
+            < cum_mass_threshold
         ):
-
-            if (
-                abs(mass_diff(pred_aa_mass, orig_aa_mass, mode_is_da=True))
-                < single_aa_threshold
-            ):
-                aa_match += [1]
-            else:
-                aa_match += [0]
-
-            cnt_pred += 1
-            cnt_orig += 1
-
-        elif (
-            mass_diff(
-                pred_prefix_mass_list[cnt_pred],
-                orig_prefix_mass_list[cnt_orig],
-                mode_is_da=True,
+            aa_matches[max(i1, i2)] = (
+                abs(mass_diff(aa_mass1, aa_mass2, True)) < ind_mass_threshold
             )
-            > 0
-        ):
-            cnt_orig += 1
-
+            i1, i2 = i1 + 1, i2 + 1
+            cum_mass1, cum_mass2 = cum_mass1 + aa_mass1, cum_mass2 + aa_mass2
+        elif cum_mass2 + aa_mass2 > cum_mass1 + aa_mass1:
+            i1, cum_mass1 = i1 + 1, cum_mass1 + aa_mass1
         else:
-            cnt_pred += 1
-            aa_match += [0]
-
-    if sum(aa_match) == len(orig_seq) and len(pred_seq) == len(orig_seq):
-        pep_match = 1
+            i2, cum_mass2 = i2 + 1, cum_mass2 + aa_mass2
+    return aa_matches, aa_matches.all()
 
-    return aa_match, pep_match
 
-
-def find_aa_match_single_pep(orig_seq, pred_seq, aa_dict):
+def aa_match_prefix_suffix(
+    peptide1: List[str],
+    peptide2: List[str],
+    aa_dict: Dict[str, float],
+    cum_mass_threshold: float = 0.5,
+    ind_mass_threshold: float = 0.1,
+) -> Tuple[np.ndarray, bool]:
     """
-    Find the matching amino acids of an original and predicted peptide sequence as described in DeepNovo.
+    Find the matching prefix and suffix amino acids between two peptide
+    sequences.
 
     Parameters
     ----------
-    orig_seq : list
-        List of amino acids in the original peptide
-    pred_seq : list
-        List of amino acids in the predicted peptide
-    aa_dict: dict
-        Dictionary of amino acid masses
+    peptide1 : List[str]
+        The first tokenized peptide sequence to be compared.
+    peptide2 : List[str]
+        The second tokenized peptide sequence to be compared.
+    aa_dict : Dict[str, float]
+        Mapping of amino acid tokens to their mass values.
+    cum_mass_threshold : float
+        Mass threshold in Dalton to accept cumulative mass-matching amino acid
+        sequences.
+    ind_mass_threshold : float
+        Mass threshold in Dalton to accept individual mass-matching amino acids.
 
     Returns
     -------
-    aa_match: list
-        Binary list of c
-    pep_match: int
-        1 if all amino acid in two sequences match
-
+    aa_matches : np.ndarray of length max(len(peptide1), len(peptide2))
+        Boolean flag indicating whether each paired-up amino acid matches across
+        both peptide sequences.
+    pep_match : bool
+        Boolean flag to indicate whether the two peptide sequences fully match.
     """
-
-    cum_aa_threshold = 0.5
-    single_aa_threshold = 0.1
-
-    pred_cum_aa_mass = 0
-    orig_cum_aa_mass = 0
-
-    aa_match = []
-    pep_match = 0
-
-    cnt_pred = 0
-    cnt_orig = 0
-
-    while cnt_pred < len(pred_seq) and cnt_orig < len(orig_seq):
-
-        pred_aa_mass = aa_dict[pred_seq[cnt_pred]]
-        orig_aa_mass = aa_dict[orig_seq[cnt_orig]]
-
+    # Find longest mass-matching prefix.
+    aa_matches, pep_match = aa_match_prefix(
+        peptide1, peptide2, aa_dict, cum_mass_threshold, ind_mass_threshold
+    )
+    # No need to evaluate the suffixes if the sequences already fully match.
+    if pep_match:
+        return aa_matches, pep_match
+    # Find longest mass-matching suffix.
+    i1, i2 = len(peptide1) - 1, len(peptide2) - 1
+    i_stop = np.argwhere(~aa_matches)[0]
+    cum_mass1, cum_mass2 = 0.0, 0.0
+    while i1 >= i_stop and i2 >= i_stop:
+        aa_mass1, aa_mass2 = aa_dict[peptide1[i1]], aa_dict[peptide2[i2]]
         if (
-            abs(mass_diff(pred_cum_aa_mass, orig_cum_aa_mass, mode_is_da=True))
-            < cum_aa_threshold
-        ):
-
-            if (
-                abs(mass_diff(pred_aa_mass, orig_aa_mass, mode_is_da=True))
-                < single_aa_threshold
-            ):
-                aa_match += [1]
-            else:
-                aa_match += [0]
-
-            cnt_pred += 1
-            cnt_orig += 1
-
-            pred_cum_aa_mass += pred_aa_mass
-            orig_cum_aa_mass += orig_aa_mass
-
-        elif (
-            mass_diff(pred_cum_aa_mass, orig_cum_aa_mass, mode_is_da=True) > 0
+            abs(mass_diff(cum_mass1 + aa_mass1, cum_mass2 + aa_mass2, True))
+            < cum_mass_threshold
         ):
-            cnt_orig += 1
-            orig_cum_aa_mass += orig_aa_mass
-
+            aa_matches[max(i1, i2)] = (
+                abs(mass_diff(aa_mass1, aa_mass2, True)) < ind_mass_threshold
+            )
+            i1, i2 = i1 - 1, i2 - 1
+            cum_mass1, cum_mass2 = cum_mass1 + aa_mass1, cum_mass2 + aa_mass2
+        elif cum_mass2 + aa_mass2 > cum_mass1 + aa_mass1:
+            i1, cum_mass1 = i1 - 1, cum_mass1 + aa_mass1
         else:
-            cnt_pred += 1
-            pred_cum_aa_mass += pred_aa_mass
-            aa_match += [0]
-
-    if sum(aa_match) == len(orig_seq) and len(pred_seq) == len(orig_seq):
-        pep_match = 1
-
-    return aa_match, pep_match
-
-
-def match_aa(orig_seq, pred_seq, aa_dict, eval_direction="best"):
+            i2, cum_mass2 = i2 - 1, cum_mass2 + aa_mass2
+    return aa_matches, aa_matches.all()
+
+
+def aa_match(
+    peptide1: List[str],
+    peptide2: List[str],
+    aa_dict: Dict[str, float],
+    cum_mass_threshold: float = 0.5,
+    ind_mass_threshold: float = 0.1,
+    mode: str = "best",
+) -> Tuple[np.ndarray, bool]:
     """
-    Find the matching amino acids of an original and predicted peptide
+    Find the matching amino acids between two peptide sequences.
 
     Parameters
     ----------
-    orig_seq : list
-        List of amino acids in the original peptide
-    pred_seq : list
-        List of amino acids in the predicted peptide
-    aa_dict: dict
-        Dictionary of amino acid masses
-    eval_direction: str, default: 'best'
-        Direction of evaluation while finding amino acid matches, e.g. 'forward', 'backward', 'best'
-
+    peptide1 : List[str]
+        The first tokenized peptide sequence to be compared.
+    peptide2 : List[str]
+        The second tokenized peptide sequence to be compared.
+    aa_dict : Dict[str, float]
+        Mapping of amino acid tokens to their mass values.
+    cum_mass_threshold : float
+        Mass threshold in Dalton to accept cumulative mass-matching amino acid
+        sequences.
+    ind_mass_threshold : float
+        Mass threshold in Dalton to accept individual mass-matching amino acids.
+    mode : {"best", "forward", "backward"}
+        The direction in which to find matching amino acids.
 
     Returns
     -------
-    aa_match: list
-        Binary list of c
-    pep_match: int
-        1 if all amino acid in two sequences match
-
+    aa_matches : np.ndarray of length max(len(peptide1), len(peptide2))
+        Boolean flag indicating whether each paired-up amino acid matches across
+        both peptide sequences.
+    pep_match : bool
+        Boolean flag to indicate whether the two peptide sequences fully match.
     """
-
-    if eval_direction == "best":
-        aa_match, pep_match = best_aa_match(orig_seq, pred_seq, aa_dict)
-        n_mismatch_aa = len(pred_seq) - len(aa_match)
-        aa_match += n_mismatch_aa * [0]
-
-    elif eval_direction == "forward":
-        aa_match, pep_match = find_aa_match_single_pep(
-            orig_seq, pred_seq, aa_dict
+    if mode == "best":
+        return aa_match_prefix_suffix(
+            peptide1, peptide2, aa_dict, cum_mass_threshold, ind_mass_threshold
         )
-
-        n_mismatch_aa = len(pred_seq) - len(aa_match)
-        aa_match += n_mismatch_aa * [0]
-
-    elif eval_direction == "backward":
-        reverse_aa_match, pep_match = find_aa_match_single_pep(
-            list(reversed(orig_seq)), list(reversed(pred_seq)), aa_dict
+    elif mode == "forward":
+        return aa_match_prefix(
+            peptide1, peptide2, aa_dict, cum_mass_threshold, ind_mass_threshold
         )
-
-        aa_match = list(reversed(reverse_aa_match))
-        n_mismatch_aa = len(pred_seq) - len(aa_match)
-        aa_match = n_mismatch_aa * [0] + aa_match
-
-    return aa_match, pep_match
-
-
-def batch_aa_match(
-    pred_pep_seqs, true_pep_seqs, aa_dict, eval_direction="best"
-):
+    elif mode == "backward":
+        aa_matches, pep_match = aa_match_prefix(
+            list(reversed(peptide1)),
+            list(reversed(peptide2)),
+            aa_dict,
+            cum_mass_threshold,
+            ind_mass_threshold,
+        )
+        return aa_matches[::-1], pep_match
+    else:
+        raise ValueError("Unknown evaluation mode")
+
+
+def aa_match_batch(
+    peptides1: List[str],
+    peptides2: List[str],
+    aa_dict: Dict[str, float],
+    cum_mass_threshold: float = 0.5,
+    ind_mass_threshold: float = 0.1,
+    mode: str = "best",
+) -> Tuple[List[Tuple[np.ndarray, bool]], int, int]:
     """
-    Find the matching amino acids of an original and predicted peptide
+    Find the matching amino acids between multiple pairs of peptide sequences.
 
     Parameters
     ----------
-    pred_pep_seqs : list
-        List of predicted peptides, i.e. list of amino acid sequences
-    true_pep_seqs : list
-        List of ground truth peptide labels
-    aa_dict: dict
-        Dictionary of amino acid masses
-    eval_direction: str, default: 'best'
-        Direction of evaluation while finding amino acid matches, e.g. 'forward', 'backward', 'best'
-
+    peptides1 : List[str]
+        The first list of (untokenized) peptide sequences to be compared.
+    peptides2 : List[str]
+        The second list of (untokenized) peptide sequences to be compared.
+    aa_dict : Dict[str, float]
+        Mapping of amino acid tokens to their mass values.
+    cum_mass_threshold : float
+        Mass threshold in Dalton to accept cumulative mass-matching amino acid
+        sequences.
+    ind_mass_threshold : float
+        Mass threshold in Dalton to accept individual mass-matching amino acids.
+    mode : {"best", "forward", "backward"}
+        The direction in which to find matching amino acids.
 
     Returns
     -------
-    all_aa_match: list
-        Binary list of lists corresponding to amino acid matches for all predicted peptides
-    orig_total_num_aa: int
-        Total number of amino acids in the ground truth peptide labels
-    pred_total_num_aa: int
-        Total number of amino acids in the predicted peptide labels
-
+    aa_matches_batch : List[Tuple[np.ndarray, bool]]
+        For each pair of peptide sequences: (i) boolean flags indicating whether
+        each paired-up amino acid matches across both peptide sequences, (ii)
+        boolean flag to indicate whether the two peptide sequences fully match.
+    n_aa1: int
+        Total number of amino acids in the first list of peptide sequences.
+    n_aa2: int
+        Total number of amino acids in the second list of peptide sequences.
     """
-
-    orig_total_num_aa = 0
-    pred_total_num_aa = 0
-    all_aa_match = []
-
-    for pred_ind in range(len(pred_pep_seqs)):
-
-        pred = re.split(r"(?<=.)(?=[A-Z])", pred_pep_seqs[pred_ind])
-        orig = re.split(r"(?<=.)(?=[A-Z])", true_pep_seqs[pred_ind])
-        orig_total_num_aa += len(orig)
-        pred_total_num_aa += len(pred)
-
-        aa_match, pep_match = match_aa(
-            orig, pred, aa_dict, eval_direction=eval_direction
+    aa_matches_batch, n_aa1, n_aa2 = [], 0, 0
+    for peptide1, peptide2 in zip(peptides1, peptides2):
+        tokens1 = re.split(r"(?<=.)(?=[A-Z])", peptide1)
+        tokens2 = re.split(r"(?<=.)(?=[A-Z])", peptide2)
+        n_aa1, n_aa2 = n_aa1 + len(tokens1), n_aa2 + len(tokens2)
+        aa_matches_batch.append(
+            aa_match(
+                tokens1,
+                tokens2,
+                aa_dict,
+                cum_mass_threshold,
+                ind_mass_threshold,
+                mode,
+            )
         )
-        all_aa_match += [(aa_match, pep_match)]
+    return aa_matches_batch, n_aa1, n_aa2
 
-    return all_aa_match, orig_total_num_aa, pred_total_num_aa
 
-
-def calc_eval_metrics(
-    aa_match_binary_list, orig_total_num_aa, pred_total_num_aa
-):
+def aa_match_metrics(
+    aa_matches_batch: List[Tuple[np.ndarray, bool]],
+    n_aa_true: int,
+    n_aa_pred: int,
+) -> Tuple[float, float, float]:
     """
-    Calculate evaluation metrics using amino acid matches
+    Calculate amino acid and peptide-level evaluation metrics.
 
     Parameters
     ----------
-    aa_match_binary_list : list of lists
-        List of amino acid matches in each predicted peptide
-    orig_total_num_aa : int
-        Number of amino acids in the original peptide sequences
-    pred_total_num_aa : int
-        Number of amino acids in the predicted peptide sequences
+    aa_matches_batch : List[Tuple[np.ndarray, bool]]
+        For each pair of peptide sequences: (i) boolean flags indicating whether
+        each paired-up amino acid matches across both peptide sequences, (ii)
+        boolean flag to indicate whether the two peptide sequences fully match.
+    n_aa_true: int
+        Total number of amino acids in the true peptide sequences.
+    n_aa_pred: int
+        Total number of amino acids in the predicted peptide sequences.
+
     Returns
     -------
     aa_precision: float
-        Number of correct aa predictions divided by all predicted aa
+        The number of correct AA predictions divided by the number of predicted
+        AAs.
     aa_recall: float
-        Number of correct aa predictions divided by all original aa
+        The number of correct AA predictions divided by the number of true AAs.
     pep_recall: float
-        Number of correct peptide predictions divided by all original peptide
+        The number of correct peptide predictions divided by the number of
+        peptides.
     """
-
-    correct_aa_count = sum(
-        [sum(pred_tuple[0]) for pred_tuple in aa_match_binary_list]
+    n_aa_correct = sum(
+        [aa_matches[0].sum() for aa_matches in aa_matches_batch]
+    )
+    aa_precision = n_aa_correct / (n_aa_pred + 1e-8)
+    aa_recall = n_aa_correct / (n_aa_true + 1e-8)
+    pep_recall = sum([aa_matches[1] for aa_matches in aa_matches_batch]) / (
+        len(aa_matches_batch) + 1e-8
     )
-    aa_recall = correct_aa_count / (orig_total_num_aa + 1e-8)
-    aa_precision = correct_aa_count / (pred_total_num_aa + 1e-8)
-    pep_recall = sum(
-        [pred_tuple[1] for pred_tuple in aa_match_binary_list]
-    ) / (len(aa_match_binary_list) + 1e-8)
-
     return aa_precision, aa_recall, pep_recall
 
 
-def aa_precision_recall_with_threshold(
-    correct_aa_confidences, all_aa_confidences, num_original_aa, threshold
-):
+def aa_precision_recall(
+    aa_scores_correct: List[float],
+    aa_scores_all: List[float],
+    n_aa_total: int,
+    threshold: float,
+) -> Tuple[float, float]:
     """
-    Calculate precision and recall for the given amino acid confidence score threshold
+    Calculate amino acid level precision and recall at a given score threshold.
 
     Parameters
     ----------
-    correct_aa_confidences : list
-        List of confidence scores for correct amino acids predictions
-    all_aa_confidences : int
-        List of confidence scores for all amino acids prediction
-    num_original_aa : int
-        Number of amino acids in the predicted peptide sequences
+    aa_scores_correct : List[float]
+        Amino acids scores for the correct amino acids predictions.
+    aa_scores_all : List[float]
+        Amino acid scores for all amino acids predictions.
+    n_aa_total : int
+        The total number of amino acids in the predicted peptide sequences.
     threshold : float
-        Amino acid confidence score threshold
+        The amino acid score threshold.
 
     Returns
     -------
     aa_precision: float
-        Number of correct aa predictions divided by all predicted aa
+        The number of correct amino acid predictions divided by the number of
+        predicted amino acids.
     aa_recall: float
-        Number of correct aa predictions divided by all original aa
+        The number of correct amino acid predictions divided by the total number
+        of amino acids.
     """
-
-    correct_aa = sum([conf >= threshold for conf in correct_aa_confidences])
-    predicted_aa = sum([conf >= threshold for conf in all_aa_confidences])
-
-    aa_precision = correct_aa / predicted_aa
-    aa_recall = correct_aa / num_original_aa
-
-    return aa_precision, aa_recall
+    n_aa_correct = sum([score > threshold for score in aa_scores_correct])
+    n_aa_predicted = sum([score > threshold for score in aa_scores_all])
+    return n_aa_correct / n_aa_predicted, n_aa_correct / n_aa_total
diff --git a/casanovo/denovo/model.py b/casanovo/denovo/model.py
index f4a4e954..d5fd3404 100644
--- a/casanovo/denovo/model.py
+++ b/casanovo/denovo/model.py
@@ -1,106 +1,105 @@
-"""A de novo peptide sequencing model"""
-import logging, time, random, os, csv
+"""A de novo peptide sequencing model."""
+import csv
+import logging
+import os
+from typing import Any, Dict, List, Optional, Tuple, Union
 
-import torch
+import depthcharge.masses
 import numpy as np
 import pytorch_lightning as pl
-from torch.utils.tensorboard import SummaryWriter
-
-import depthcharge.masses
-from depthcharge.components import SpectrumEncoder, PeptideDecoder, ModelMixin
+import torch
+from depthcharge.components import ModelMixin, PeptideDecoder, SpectrumEncoder
 from depthcharge.models.embed.model import PairedSpectrumEncoder
-from .evaluate import batch_aa_match, calc_eval_metrics
 
-LOGGER = logging.getLogger(__name__)
+from . import evaluate
+
+
+logger = logging.getLogger("casanovo")
 
 
 class Spec2Pep(pl.LightningModule, ModelMixin):
-    """A Transformer model for de novo peptide sequencing.
+    """
+    A Transformer model for de novo peptide sequencing.
 
     Use this model in conjunction with a pytorch-lightning Trainer.
 
     Parameters
     ----------
-    dim_model : int, optional
-        The latent dimensionality used by the Transformer model.
-    n_head : int, optional
+    dim_model : int
+        The latent dimensionality used by the transformer model.
+    n_head : int
         The number of attention heads in each layer. ``dim_model`` must be
         divisible by ``n_head``.
-    dim_feedforward : int, optional
-        The dimensionality of the fully connected layers in the Transformer
+    dim_feedforward : int
+        The dimensionality of the fully connected layers in the transformer
         model.
-    n_layers : int, optional
-        The number of Transformer layers.
-    dropout : float, optional
+    n_layers : int
+        The number of transformer layers.
+    dropout : float
         The dropout probability for all layers.
-    dim_intensity : int or None, optional
-        The number of features to use for encoding peak intensity.
-        The remaining (``dim_model - dim_intensity``) are reserved for
-        encoding the m/z value. If ``None``, the intensity will be projected
-        up to ``dim_model`` using a linear layer, then summed with the m/z
-        emcoding for each peak.
-    custom_encoder : SpectrumEncoder or PairedSpectrumEncoder, optional
-        A pretrained encoder to use. The ``dim_model`` of the encoder must
-        be the same as that specified by the ``dim_model`` parameter here.
-    max_length : int, optional
+    dim_intensity : Optional[int]
+        The number of features to use for encoding peak intensity. The remaining
+        (``dim_model - dim_intensity``) are reserved for encoding the m/z value.
+        If ``None``, the intensity will be projected up to ``dim_model`` using a
+        linear layer, then summed with the m/z encoding for each peak.
+    custom_encoder : Optional[Union[SpectrumEncoder, PairedSpectrumEncoder]]
+        A pretrained encoder to use. The ``dim_model`` of the encoder must be
+        the same as that specified by the ``dim_model`` parameter here.
+    max_length : int
         The maximum peptide length to decode.
-    residues: Dict or str {"massivekb", "canonical"}, optional
-        The amino acid dictionary and their masses. By default this is only
-        the 20 canonical amino acids, with cysteine carbamidomethylated. If
-        "massivekb", this dictionary will include the modifications found in
+    residues: Union[Dict[str, float], str]
+        The amino acid dictionary and their masses. By default ("canonical) this
+        is only the 20 canonical amino acids, with cysteine carbamidomethylated.
+        If "massivekb", this dictionary will include the modifications found in
         MassIVE-KB. Additionally, a dictionary can be used to specify a custom
         collection of amino acids and masses.
-    max_charge : int, optional
-        The maximum charge state to consider.
+    max_charge : int
+        The maximum precursor charge to consider.
     precursor_mass_tol : float, optional
-        The maximum allowable precursor mass tolerance (in ppm) for correct predictions.
-    n_log : int, optional
+        The maximum allowable precursor mass tolerance (in ppm) for correct
+        predictions.
+    n_log : int
         The number of epochs to wait between logging messages.
-    tb_summarywriter: torch.utils.tensorboard.SummaryWriter object or None, optional
-        Object to record performance metrics during training. If ``None``, don't use a SummarWriter
-    warmup_iters: int, optional
-        Number of warm up iterations for learning rate scheduler
-    max_iters: int, optional
-        Total number of iterations for learning rate scheduler
-    output_path: str, optional
-        Path to write csv file with denovo peptide sequences
+    tb_summarywriter: Optional[torch.utils.tensorboard.SummaryWriter]
+        Object to record performance metrics during training. If ``None``, don't
+        use a ``SummarWriter``.
+    warmup_iters: int
+        The number of warm up iterations for the learning rate scheduler.
+    max_iters: int
+        The total number of iterations for the learning rate scheduler.
+    out_filename: Optional[str]
+        The output file name for the prediction results.
     **kwargs : Dict
-        Keyword arguments passed to the Adam optimizer
+        Additional keyword arguments passed to the Adam optimizer.
     """
 
     def __init__(
         self,
-        dim_model=128,
-        n_head=8,
-        dim_feedforward=1024,
-        n_layers=1,
-        dropout=0,
-        dim_intensity=None,
-        custom_encoder=None,
-        max_length=100,
-        residues="canonical",
-        max_charge=5,
+        dim_model: int = 128,
+        n_head: int = 8,
+        dim_feedforward: int = 1024,
+        n_layers: int = 1,
+        dropout: float = 0.0,
+        dim_intensity: Optional[int] = None,
+        custom_encoder: Optional[
+            Union[SpectrumEncoder, PairedSpectrumEncoder]
+        ] = None,
+        max_length: int = 100,
+        residues: Union[Dict[str, float], str] = "canonical",
+        max_charge: int = 5,
         precursor_mass_tol=50,
-        n_log=10,
-        tb_summarywriter=None,
-        warmup_iters=100000,
-        max_iters=600000,
-        output_path=None,
-        **kwargs,
+        n_log: int = 10,
+        tb_summarywriter: Optional[
+            torch.utils.tensorboard.SummaryWriter
+        ] = None,
+        warmup_iters: int = 100_000,
+        max_iters: int = 600_000,
+        out_filename: Optional[str] = None,
+        **kwargs: Dict,
     ):
-        """Initialize a Spec2Pep model"""
         super().__init__()
 
-        # Writable
-        self.max_length = max_length
-        self.n_log = n_log
-
-        self.residues = residues
-        self.peptide_mass_calculator = depthcharge.masses.PeptideMass(
-            self.residues
-        )
-
-        # Build the model
+        # Build the model.
         if custom_encoder is not None:
             if isinstance(custom_encoder, PairedSpectrumEncoder):
                 self.encoder = custom_encoder.encoder
@@ -115,7 +114,6 @@ def __init__(
                 dropout=dropout,
                 dim_intensity=dim_intensity,
             )
-
         self.decoder = PeptideDecoder(
             dim_model=dim_model,
             n_head=n_head,
@@ -125,129 +123,108 @@ def __init__(
             residues=residues,
             max_charge=max_charge,
         )
-
         self.softmax = torch.nn.Softmax(2)
         self.celoss = torch.nn.CrossEntropyLoss(ignore_index=0)
-
-        # Things for training
-        self._history = []
-        self.opt_kwargs = kwargs
-        self.stop_token = self.decoder._aa2idx["$"]
-
-        self.tb_summarywriter = tb_summarywriter
-
+        # Optimizer settings.
         self.warmup_iters = warmup_iters
         self.max_iters = max_iters
+        self.opt_kwargs = kwargs
 
-        # Store de novo sequences to be saved
+        # Data properties.
+        self.max_length = max_length
+        self.residues = residues
         self.precursor_mass_tol = precursor_mass_tol
-        self.denovo_seqs = []
-        self.output_path = output_path if output_path is not None else "."
-        if not os.path.exists(self.output_path):
-            os.makedirs(self.output_path, exist_ok=True)
+        self.peptide_mass_calculator = depthcharge.masses.PeptideMass(
+            self.residues
+        )
+        self.stop_token = self.decoder._aa2idx["$"]
 
-    def forward(self, spectra, precursors):
-        """Sequence a batch of mass spectra.
+        # Logging.
+        self.n_log = n_log
+        self._history = []
+        self.tb_summarywriter = tb_summarywriter
 
-        Parameters
-        ----------
-        spectrum : torch.Tensor of shape (n_spectra, n_peaks, 2)
-            The spectra to embed. Axis 0 represents a mass spectrum, axis 1
-            contains the peaks in the mass spectrum, and axis 2 is essentially
-            a 2-tuple specifying the m/z-intensity pair for each peak. These
-            should be zero-padded, such that all of the spectra in the batch
-            are the same length.
-        precursors : torch.Tensor of size (n_spectra, 3)
-            The measured precursor mass (axis 0), precursor charge (axis 1),
-             and precursor m/z (axis 2) of each MS/MS spectrum.
+        # Output file during predicting.
+        if out_filename is not None:
+            self.out_filename = f"{os.path.splitext(out_filename)[0]}.csv"
+        else:
+            self.out_filename = None
 
-        Returns
-        -------
-        sequences : list or str
-            The sequence for each spectrum.
-        scores : torch.Tensor of shape (n_spectra, length, n_amino_acids)
-            The score for each amino acid.
+    def forward(
+        self, spectra: torch.Tensor, precursors: torch.Tensor
+    ) -> Tuple[List[str], torch.Tensor]:
         """
-        spectra = spectra.to(self.encoder.device)
-        precursors = precursors.to(self.decoder.device)
-        scores, tokens = self.greedy_decode(spectra, precursors)
-        sequences = [self.decoder.detokenize(t) for t in tokens]
-        return sequences, scores
-
-    def predict_step(self, batch, *args):
-        """Sequence a batch of mass spectra.
-
-        Note that this is used within the context of a pytorch-lightning
-        Trainer to generate a prediction.
+        Predict peptide sequences for a batch of MS/MS spectra.
 
         Parameters
         ----------
-        batch : tuple of torch.Tensor
-            A batch is expected to contain mass spectra (index 0) and the
-            precursor mass and charge (index 1). It may have more indices,
-            but these will be ignored.
-
+        spectra : torch.Tensor of shape (n_spectra, n_peaks, 2)
+            The spectra for which to predict peptide sequences.
+            Axis 0 represents an MS/MS spectrum, axis 1 contains the peaks in
+            the MS/MS spectrum, and axis 2 is essentially a 2-tuple specifying
+            the m/z-intensity pair for each peak. These should be zero-padded,
+            such that all of the spectra in the batch are the same length.
+        precursors : torch.Tensor of size (n_spectra, 3)
+            The measured precursor mass (axis 0), precursor charge (axis 1), and
+            precursor m/z (axis 2) of each MS/MS spectrum.
 
         Returns
         -------
-        sequences : list or str
-            The sequence for each spectrum.
-        scores : torch.Tensor of shape (n_spectra, length, n_amino_acids)
-            The score for each amino acid.
+        peptides : List[str]
+            The predicted peptide sequences for each spectrum.
+        aa_scores : torch.Tensor of shape (n_spectra, length, n_amino_acids)
+            The individual amino acid scores for each prediction.
         """
-        return self(batch[0], batch[1])
+        aa_scores, tokens = self.greedy_decode(
+            spectra.to(self.encoder.device),
+            precursors.to(self.decoder.device),
+        )
+        return [self.decoder.detokenize(t) for t in tokens], aa_scores
 
-    def greedy_decode(self, spectra, precursors):
-        """Greedy decode the spectra.
+    def greedy_decode(
+        self, spectra: torch.Tensor, precursors: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Greedy decoding of the spectrum predictions.
 
         Parameters
         ----------
-        spectrum : torch.Tensor of shape (n_spectra, n_peaks, 2)
-            The spectra to embed. Axis 0 represents a mass spectrum, axis 1
-            contains the peaks in the mass spectrum, and axis 2 is essentially
-            a 2-tuple specifying the m/z-intensity pair for each peak. These
-            should be zero-padded, such that all of the spectra in the batch
-            are the same length.
+        spectra : torch.Tensor of shape (n_spectra, n_peaks, 2)
+            The spectra for which to predict peptide sequences.
+            Axis 0 represents an MS/MS spectrum, axis 1 contains the peaks in
+            the MS/MS spectrum, and axis 2 is essentially a 2-tuple specifying
+            the m/z-intensity pair for each peak. These should be zero-padded,
+            such that all of the spectra in the batch are the same length.
         precursors : torch.Tensor of size (n_spectra, 3)
-            The measured precursor mass (axis 0), precursor charge (axis 1),
-             and precursor m/z (axis 2) of each MS/MS spectrum.
+            The measured precursor mass (axis 0), precursor charge (axis 1), and
+            precursor m/z (axis 2) of each MS/MS spectrum.
 
         Returns
         -------
-        tokens : torch.Tensor of shape (n_spectra, max_length, n_amino_acids)
-            The token sequence for each spectrum.
         scores : torch.Tensor of shape (n_spectra, max_length, n_amino_acids)
-            The score for each amino acid.
+            The individual amino acid scores for each prediction.
+        tokens : torch.Tensor of shape (n_spectra, max_length, n_amino_acids)
+            The predicted tokens for each spectrum.
         """
         memories, mem_masks = self.encoder(spectra)
-
-        # initialize scores:
+        # Initialize the scores.
         scores = torch.zeros(
-            spectra.shape[0],
-            self.max_length + 1,
-            self.decoder.vocab_size + 1,
-        )
-        scores = scores.type_as(spectra)
-
-        # The first prediction:
+            spectra.shape[0], self.max_length + 1, self.decoder.vocab_size + 1
+        ).type_as(spectra)
+        # Start with the first amino acid predictions.
         scores[:, :1, :], _ = self.decoder(
-            None,
-            precursors,
-            memories,
-            mem_masks,
+            None, precursors, memories, mem_masks
         )
-
         tokens = torch.argmax(scores, axis=2)
-
-        # Keep predicting until all have a stop token or max_length is reached.
-        # Don't count the stop token toward max_length though.
-        for idx in range(2, self.max_length + 2):
+        # Keep predicting until a stop token is predicted or max_length is
+        # reached.
+        # The stop token does not count towards max_length.
+        for i in range(2, self.max_length + 2):
             decoded = (tokens == self.stop_token).any(axis=1)
             if decoded.all():
                 break
-
-            scores[~decoded, :idx, :], _ = self.decoder(
-                tokens[~decoded, : (idx - 1)],
+            scores[~decoded, :i, :], _ = self.decoder(
+                tokens[~decoded, : (i - 1)],
                 precursors[~decoded, :],
                 memories[~decoded, :, :],
                 mem_masks[~decoded, :],
@@ -256,349 +233,297 @@ def greedy_decode(self, spectra, precursors):
 
         return self.softmax(scores), tokens
 
-    def _step(self, spectra, precursors, sequences):
-        """The forward learning step.
+    def _forward_step(
+        self,
+        spectra: torch.Tensor,
+        precursors: torch.Tensor,
+        sequences: List[str],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        The forward learning step.
 
         Parameters
         ----------
         spectra : torch.Tensor of shape (n_spectra, n_peaks, 2)
-            The spectra to embed. Axis 0 represents a mass spectrum, axis 1
-            contains the peaks in the mass spectrum, and axis 2 is essentially
-            a 2-tuple specifying the m/z-intensity pair for each peak. These
-            should be zero-padded, such that all of the spectra in the batch
-            are the same length.
+            The spectra for which to predict peptide sequences.
+            Axis 0 represents an MS/MS spectrum, axis 1 contains the peaks in
+            the MS/MS spectrum, and axis 2 is essentially a 2-tuple specifying
+            the m/z-intensity pair for each peak. These should be zero-padded,
+            such that all of the spectra in the batch are the same length.
         precursors : torch.Tensor of size (n_spectra, 3)
-            The measured precursor mass (axis 0), precursor charge (axis 1),
-             and precursor m/z (axis 3) of each MS/MS spectrum.
-        sequences : list or str of length n_spectra
+            The measured precursor mass (axis 0), precursor charge (axis 1), and
+            precursor m/z (axis 2) of each MS/MS spectrum.
+        sequences : List[str] of length n_spectra
             The partial peptide sequences to predict.
 
         Returns
         -------
         scores : torch.Tensor of shape (n_spectra, length, n_amino_acids)
-            The raw scores for each amino acid at each position.
+            The individual amino acid scores for each prediction.
         tokens : torch.Tensor of shape (n_spectra, length)
-            The best token at each sequence position
+            The predicted tokens for each spectrum.
         """
-        memory, mem_mask = self.encoder(spectra)
-        scores, tokens = self.decoder(sequences, precursors, memory, mem_mask)
-        return scores, tokens
-
-    def training_step(self, batch, *args):
-        """A single training step
+        return self.decoder(sequences, precursors, *self.encoder(spectra))
 
-        Note that this is used within the context of a pytorch-lightning
-        Trainer to generate a prediction.
+    def training_step(
+        self,
+        batch: Tuple[torch.Tensor, torch.Tensor, List[str]],
+        *args,
+        mode: str = "train",
+    ) -> torch.Tensor:
+        """
+        A single training step.
 
         Parameters
         ----------
-        batch : tuple of torch.Tensor
-            A batch is expected to contain mass spectra (index 0), the
-            precursor mass and charge (index 1), and the peptide sequence
-            (index 2)
+        batch : Tuple[torch.Tensor, torch.Tensor, List[str]]
+            A batch of (i) MS/MS spectra, (ii) precursor information, (iii)
+            peptide sequences as torch Tensors.
+        mode : str
+            Logging key to describe the current stage.
 
         Returns
         -------
         torch.Tensor
-            The loss.
+            The loss of the training step.
         """
-
-        spectra, precursors, sequences = batch
-        pred, truth = self._step(spectra, precursors, sequences)
-
+        pred, truth = self._forward_step(*batch)
         pred = pred[:, :-1, :].reshape(-1, self.decoder.vocab_size + 1)
         loss = self.celoss(pred, truth.flatten())
         self.log(
             "CELoss",
-            {"train": loss.item()},
+            {mode: loss.item()},
             on_step=False,
             on_epoch=True,
             sync_dist=True,
         )
         return loss
 
-    def validation_step(self, batch, *args):
-        """A single validation step
-
-        Note that this is used within the context of a pytorch-lightning
-        Trainer to generate a prediction.
+    def validation_step(
+        self, batch: Tuple[torch.Tensor, torch.Tensor, List[str]], *args
+    ) -> torch.Tensor:
+        """
+        A single validation step.
 
         Parameters
         ----------
-        batch : tuple of torch.Tensor
-            A batch is expected to contain mass spectra (index 0), the
-            precursor mass and charge (index 1), and the peptide sequence
-            (index 2)
+        batch : Tuple[torch.Tensor, torch.Tensor, List[str]]
+            A batch of (i) MS/MS spectra, (ii) precursor information, (iii)
+            peptide sequences.
 
         Returns
         -------
         torch.Tensor
-            The loss.
+            The loss of the validation step.
         """
-        spectra, precursors, sequences = batch
-        pred, truth = self._step(spectra, precursors, sequences)
-        pred = pred[:, :-1, :].reshape(-1, self.decoder.vocab_size + 1)
-        loss = self.celoss(pred, truth.flatten())
-        self.log(
-            "CELoss",
-            {"valid": loss.item()},
-            on_step=False,
-            on_epoch=True,
-            sync_dist=True,
-        )
-
-        # De novo sequence the batch
-        pred_seqs, scores = self.predict_step(batch)
-
-        # Temporary solution to predictions with multiple stop token '$', filter them out
-        filtered_pred_seqs = []
-        filtered_sequences = []
-        for i in range(len(pred_seqs)):
-            if len(pred_seqs[i]) > 0:
-                ps = pred_seqs[i]
-                if pred_seqs[i][0] == "$":
-                    ps = pred_seqs[i][1:]  # Remove stop token
-                if "$" not in ps and len(ps) > 0:
-                    filtered_pred_seqs += [ps]
-                    filtered_sequences += [sequences[i]]
-
-        # Find AA and peptide matches
-        all_aa_match, orig_total_num_aa, pred_total_num_aa = batch_aa_match(
-            filtered_pred_seqs,
-            filtered_sequences,
-            self.decoder._peptide_mass.masses,
-            "best",
-        )
-
-        # Calculate evaluation metrics based on matches
-        aa_precision, aa_recall, pep_recall = calc_eval_metrics(
-            all_aa_match, orig_total_num_aa, pred_total_num_aa
-        )
-
-        self.log(
-            "aa_precision",
-            {"valid": aa_precision},
-            on_step=False,
-            on_epoch=True,
-            sync_dist=True,
-        )
-
-        self.log(
-            "aa_recall",
-            {"valid": aa_recall},
-            on_step=False,
-            on_epoch=True,
-            sync_dist=True,
-        )
-
-        self.log(
-            "pep_recall",
-            {"valid": pep_recall},
-            on_step=False,
-            on_epoch=True,
-            sync_dist=True,
+        # Record the loss.
+        loss = self.training_step(batch, mode="valid")
+
+        # Calculate and log amino acid and peptide match evaluation metrics from
+        # the predicted peptides.
+        peptides_pred_raw, _ = self.forward(batch[0], batch[1])
+        # FIXME: Temporary fix to skip predictions with multiple stop tokens.
+        peptides_pred, peptides_true = [], []
+        for peptide_pred, peptide_true in zip(peptides_pred_raw, batch[2]):
+            if len(peptide_pred) > 0:
+                if peptide_pred[0] == "$":
+                    peptide_pred = peptide_pred[1:]  # Remove stop token.
+                if "$" not in peptide_pred and len(peptide_pred) > 0:
+                    peptides_pred.append(peptide_pred)
+                    peptides_true.append(peptide_true)
+        aa_precision, aa_recall, pep_recall = evaluate.aa_match_metrics(
+            *evaluate.aa_match_batch(
+                peptides_pred, peptides_true, self.decoder._peptide_mass.masses
+            )
         )
+        log_args = dict(on_step=False, on_epoch=True, sync_dist=True)
+        self.log("aa_precision", {"valid": aa_precision}, **log_args)
+        self.log("aa_recall", {"valid": aa_recall}, **log_args)
+        self.log("pep_recall", {"valid": pep_recall}, **log_args)
 
         return loss
 
-    def test_step(self, batch, *args):
-        """A single test step
-
-        Note that this is used within the context of a pytorch-lightning
-        Trainer to generate a prediction.
+    def predict_step(
+        self, batch: Tuple[torch.Tensor, torch.Tensor, torch.Tensor], *args
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[str], torch.Tensor]:
+        """
+        A single prediction step.
 
         Parameters
         ----------
-        batch : tuple of torch.Tensor
-            A batch is expected to contain mass spectra (index 0), the
-            precursor mass and charge (index 1), and the spectrum identifier
-            (index 2)
+        batch : Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
+            A batch of (i) MS/MS spectra, (ii) precursor information, (iii)
+            spectrum identifiers as torch Tensors.
 
+        Returns
+        -------
+        spectrum_idx : torch.Tensor
+            The spectrum identifiers.
+        precursors : torch.Tensor
+            Precursor information for each spectrum.
+        peptides : List[str]
+            The predicted peptide sequences for each spectrum.
+        aa_scores : torch.Tensor of shape (n_spectra, length, n_amino_acids)
+            The individual amino acid scores for each prediction.
         """
-        peptide, aa_scores = self.predict_step(batch)
-        # Spectrum ID, precursor mass and charge, predicted peptide sequence, AA scores.
-        self.denovo_seqs.append((batch[2], batch[1], peptide, aa_scores))
+        peptides, aa_scores = self.forward(batch[0], batch[1])
+        return batch[2], batch[1], peptides, aa_scores
 
-    def on_train_epoch_end(self):
-        """Log the training loss.
-
-        This is a pytorch-lightning hook.
+    def on_train_epoch_end(self) -> None:
+        """
+        Log the training loss at the end of each epoch.
         """
         train_loss = self.trainer.callback_metrics["CELoss"]["train"].item()
         self._history[-1]["train"] = train_loss
+        self._log_history()
 
-    def on_validation_epoch_end(self):
-        """Log the epoch metrics to self.history.
-
-        This is a pytorch-lightning hook.
+    def on_validation_epoch_end(self) -> None:
         """
+        Log the validation metrics at the end of each epoch.
+        """
+        callback_metrics = self.trainer.callback_metrics
         metrics = {
             "epoch": self.trainer.current_epoch,
-            "valid": self.trainer.callback_metrics["CELoss"]["valid"].item(),
-            "valid_aa_precision": self.trainer.callback_metrics[
-                "aa_precision"
-            ]["valid"].item(),
-            "valid_aa_recall": self.trainer.callback_metrics["aa_recall"][
-                "valid"
-            ].item(),
-            "valid_pep_recall": self.trainer.callback_metrics["pep_recall"][
+            "valid": callback_metrics["CELoss"]["valid"].item(),
+            "valid_aa_precision": callback_metrics["aa_precision"][
                 "valid"
             ].item(),
+            "valid_aa_recall": callback_metrics["aa_recall"]["valid"].item(),
+            "valid_pep_recall": callback_metrics["pep_recall"]["valid"].item(),
         }
         self._history.append(metrics)
+        self._log_history()
 
-    def on_test_epoch_end(self):
-        """Write de novo sequences and confidence scores to csv file.
-
-        This is a pytorch-lightning hook.
+    def on_predict_epoch_end(
+        self, results: List[List[Tuple[np.ndarray, List[str], torch.Tensor]]]
+    ) -> None:
         """
-        with open(
-            os.path.join(self.output_path, "casanovo_output.csv"), "w"
-        ) as f:
-            writer = csv.writer(f)
-            writer.writerow(
-                ["spectrum_id", "denovo_seq", "peptide_score", "aa_scores"]
-            )
-
-            for batch in self.denovo_seqs:
-                scores = batch[-1].cpu()  # transfer to cpu in case in gpu
-
-                for i in range(len(batch[0])):
-                    peptide_seq = batch[2][i][1:]
-                    _, precursor_charge, precursor_mz = batch[1][i]
-                    predicted_mz = self.peptide_mass_calculator.mass(
-                        peptide_seq, precursor_charge
-                    )
-                    delta_mass_ppm = (
-                        abs(predicted_mz - precursor_mz)
-                        / precursor_mz
-                        * 10**6
-                    )
-                    # take the score of most probable AA
-                    top_scores = torch.max(scores[i], axis=1)[0]
-                    # find the indices of positions after stop token
-                    empty_index = torch.where(top_scores == 0.04)[0]
-                    # check if decoding was stopped
-                    if len(empty_index) > 0:
-                        # select index of the last AA
-                        last_index = empty_index[0] - 1
-                        # check if peptide is at least one AA long
-                        if last_index >= 1:
-                            # omit the stop token
-                            top_scores_list = top_scores[:last_index].tolist()
-                            peptide_score = np.mean(top_scores_list)
+        Write the predicted peptide sequences and amino acid scores to the
+        output file.
+        """
+        if self.out_filename is None:
+            return
+        empty_token_score = torch.tensor(0.04)
+        with open(self.out_filename, "w") as f_out:
+            writer = csv.writer(f_out, delimiter="\t")
+            writer.writerow(["spectrum_id", "sequence", "score", "aa_scores"])
+            for batch in results:
+                for step in batch:
+                    for spectrum_i, precursor, peptide, aa_scores in zip(
+                        *step
+                    ):
+                        peptide = peptide[1:]
+                        # Compare the experimental vs calculated precursor m/z.
+                        _, precursor_charge, precursor_mz = precursor
+                        calc_mz = self.peptide_mass_calculator.mass(
+                            peptide, precursor_charge
+                        )
+                        delta_mass_ppm = (
+                            abs(calc_mz - precursor_mz)
+                            / precursor_mz
+                            * 10**6
+                        )
+                        # Take the scores of the most probable amino acids.
+                        top_aa_scores = torch.max(aa_scores, axis=1)[0]
+                        # Find the index after the first stop token to check if
+                        # decoding was stopped.
+                        empty_index = torch.argmax(
+                            torch.isclose(
+                                top_aa_scores, empty_token_score
+                            ).double()
+                        )
+                        if empty_index > 0:
+                            # Omit the stop token.
+                            top_aa_scores = top_aa_scores[: empty_index - 1]
+                            peptide_score = torch.mean(top_aa_scores).item()
                             # Subtract one if the precursor m/z tolerance is
                             # violated.
                             if delta_mass_ppm > self.precursor_mass_tol:
                                 peptide_score -= 1
-                            aa_scores = list(reversed(top_scores_list))
+                            aa_scores = ",".join(
+                                map(str, top_aa_scores.cpu().numpy()[::-1])
+                            )
                         else:
                             peptide_score, aa_scores = None, None
-                    else:
-                        peptide_score, aa_scores = None, None
-
-                    writer.writerow(
-                        [
-                            batch[0][i],
-                            peptide_seq,
-                            peptide_score,
-                            aa_scores,
-                        ]
-                    )
-
-    def on_epoch_end(self):
-        """Print log to console, if requested."""
-
-        if len(self._history) > 0:
-            # Print only if all output for the current epoch is recorded
-            if len(self._history[-1]) == 6:
-                if len(self._history) == 1:
-                    LOGGER.info(
-                        "---------------------------------------------------------------------------------------------------------"
-                    )
-                    LOGGER.info(
-                        "  Epoch |   Train Loss  |  Valid Loss | Valid AA precision | Valid AA recall | Valid Peptide recall "
-                    )
-                    LOGGER.info(
-                        "---------------------------------------------------------------------------------------------------------"
-                    )
-
-                metrics = self._history[-1]
-                if not metrics["epoch"] % self.n_log:
-                    LOGGER.info(
-                        "  %5i | %13.6f | %13.6f | %13.6f | %13.6f | %13.6f ",
-                        metrics["epoch"],
-                        metrics.get("train", np.nan),
-                        metrics.get("valid", np.nan),
-                        metrics.get("valid_aa_precision", np.nan),
-                        metrics.get("valid_aa_recall", np.nan),
-                        metrics.get("valid_pep_recall", np.nan),
-                    )
-                    # Add metrics to SummaryWriter object if provided
-                    if self.tb_summarywriter is not None:
-                        self.tb_summarywriter.add_scalar(
-                            "loss/train_crossentropy_loss",
-                            metrics.get("train", np.nan),
-                            metrics["epoch"] + 1,
-                        )
-                        self.tb_summarywriter.add_scalar(
-                            "loss/dev_crossentropy_loss",
-                            metrics.get("valid", np.nan),
-                            metrics["epoch"] + 1,
+                        writer.writerow(
+                            [spectrum_i, peptide, peptide_score, aa_scores]
                         )
 
+    def _log_history(self) -> None:
+        """
+        Write log to console, if requested.
+        """
+        # Log only if all output for the current epoch is recorded.
+        if len(self._history) > 0 and len(self._history[-1]) == 6:
+            if len(self._history) == 1:
+                logger.info(
+                    "Epoch\tTrain loss\tValid loss\tAA precision\tAA recall\t"
+                    "Peptide recall"
+                )
+            metrics = self._history[-1]
+            if metrics["epoch"] % self.n_log == 0:
+                logger.info(
+                    "%i\t%.6f\t%.6f\t%.6f\t%.6f\t%.6f",
+                    metrics["epoch"] + 1,
+                    metrics.get("train", np.nan),
+                    metrics.get("valid", np.nan),
+                    metrics.get("valid_aa_precision", np.nan),
+                    metrics.get("valid_aa_recall", np.nan),
+                    metrics.get("valid_pep_recall", np.nan),
+                )
+                if self.tb_summarywriter is not None:
+                    for descr, key in [
+                        ("loss/train_crossentropy_loss", "train"),
+                        ("loss/dev_crossentropy_loss", "valid"),
+                        ("eval/dev_aa_precision", "valid_aa_precision"),
+                        ("eval/dev_aa_recall", "valid_aa_recall"),
+                        ("eval/dev_pep_recall", "valid_pep_recall"),
+                    ]:
                         self.tb_summarywriter.add_scalar(
-                            "eval/dev_aa_precision",
-                            metrics.get("valid_aa_precision", np.nan),
-                            metrics["epoch"] + 1,
-                        )
-                        self.tb_summarywriter.add_scalar(
-                            "eval/dev_aa_recall",
-                            metrics.get("valid_aa_recall", np.nan),
-                            metrics["epoch"] + 1,
-                        )
-                        self.tb_summarywriter.add_scalar(
-                            "eval/dev_pep_recall",
-                            metrics.get("valid_pep_recall", np.nan),
+                            descr,
+                            metrics.get(key, np.nan),
                             metrics["epoch"] + 1,
                         )
 
-    def configure_optimizers(self):
-        """Initialize the optimizer.
+    def configure_optimizers(
+        self,
+    ) -> Tuple[torch.optim.Optimizer, Dict[str, Any]]:
+        """
+        Initialize the optimizer.
 
-        This is used by pytorch-lightning when preparing the model for
-        training.
+        This is used by pytorch-lightning when preparing the model for training.
 
         Returns
         -------
-        torch.optim.Adam
-            The intialized Adam optimizer.
+        Tuple[torch.optim.Optimizer, Dict[str, Any]]
+            The initialized Adam optimizer and its learning rate scheduler.
         """
         optimizer = torch.optim.Adam(self.parameters(), **self.opt_kwargs)
-
-        # Apply lr scheduler per step
+        # Apply learning rate scheduler per step.
         lr_scheduler = CosineWarmupScheduler(
             optimizer, warmup=self.warmup_iters, max_iters=self.max_iters
         )
-
-        return [optimizer], [{"scheduler": lr_scheduler, "interval": "step"}]
+        return [optimizer], {"scheduler": lr_scheduler, "interval": "step"}
 
 
 class CosineWarmupScheduler(torch.optim.lr_scheduler._LRScheduler):
-    """Learning rate scheduler with linear warm up followed by cosine shaped decay.
+    """
+    Learning rate scheduler with linear warm up followed by cosine shaped decay.
+
     Parameters
     ----------
-    optimizer :  torch.optim
-        Optimizier object
-    warmup :  int
-        Number of warm up iterations
-    max_iters :  torch.optim
-        Total number of iterations
+    optimizer : torch.optim.Optimizer
+        Optimizer object.
+    warmup : int
+        The number of warm up iterations.
+    max_iters : torch.optim
+        The total number of iterations.
     """
 
-    def __init__(self, optimizer, warmup, max_iters):
-        self.warmup = warmup
-        self.max_num_iters = max_iters
+    def __init__(
+        self, optimizer: torch.optim.Optimizer, warmup: int, max_iters: int
+    ):
+        self.warmup, self.max_iters = warmup, max_iters
         super().__init__(optimizer)
 
     def get_lr(self):
@@ -606,7 +531,7 @@ def get_lr(self):
         return [base_lr * lr_factor for base_lr in self.base_lrs]
 
     def get_lr_factor(self, epoch):
-        lr_factor = 0.5 * (1 + np.cos(np.pi * epoch / self.max_num_iters))
+        lr_factor = 0.5 * (1 + np.cos(np.pi * epoch / self.max_iters))
         if epoch <= self.warmup:
-            lr_factor *= epoch * 1.0 / self.warmup
+            lr_factor *= epoch / self.warmup
         return lr_factor
diff --git a/casanovo/denovo/model_runner.py b/casanovo/denovo/model_runner.py
new file mode 100644
index 00000000..88cb15d6
--- /dev/null
+++ b/casanovo/denovo/model_runner.py
@@ -0,0 +1,298 @@
+"""Training and testing functionality for the de novo peptide sequencing
+model."""
+import logging
+import os
+import tempfile
+import uuid
+from typing import Any, Dict, List, Optional
+
+import pytorch_lightning as pl
+from depthcharge.data import AnnotatedSpectrumIndex, SpectrumIndex
+
+from casanovo.denovo.dataloaders import DeNovoDataModule
+from casanovo.denovo.model import Spec2Pep
+
+
+logger = logging.getLogger("casanovo")
+
+
+def predict(
+    peak_dir: str,
+    model_filename: str,
+    out_filename: str,
+    config: Dict[str, Any],
+) -> None:
+    """
+    Predict peptide sequences with a trained Casanovo model.
+
+    Parameters
+    ----------
+    peak_dir : str
+        The directory with peak files for predicting peptide sequences.
+    model_filename : str
+        The file name of the model weights (.ckpt file).
+    out_filename : str
+        The output file name for the prediction results (format: .csv).
+    config : Dict[str, Any]
+        The configuration options.
+    """
+    _execute_existing(peak_dir, model_filename, config, False, out_filename)
+
+
+def evaluate(
+    peak_dir: str, model_filename: str, config: Dict[str, Any]
+) -> None:
+    """
+    Evaluate peptide sequence predictions from a trained Casanovo model.
+
+    Parameters
+    ----------
+    peak_dir : str
+        The directory with peak files for predicting peptide sequences.
+    model_filename : str
+        The file name of the model weights (.ckpt file).
+    config : Dict[str, Any]
+        The configuration options.
+    """
+    _execute_existing(peak_dir, model_filename, config, True)
+
+
+def _execute_existing(
+    peak_dir: str,
+    model_filename: str,
+    config: Dict[str, Any],
+    annotated: bool,
+    out_filename: Optional[str] = None,
+) -> None:
+    """
+    Predict peptide sequences with a trained Casanovo model with/without
+    evaluation.
+
+    Parameters
+    ----------
+    peak_dir : str
+        The directory with peak files for predicting peptide sequences.
+    model_filename : str
+        The file name of the model weights (.ckpt file).
+    config : Dict[str, Any]
+        The configuration options.
+    annotated : bool
+        Whether the input peak files are annotated (execute in evaluation mode)
+        or not (execute in prediction mode only).
+    out_filename : str
+        The output file name for the prediction results (format: .csv).
+    """
+    # Load the trained model.
+    if not os.path.isfile(model_filename):
+        logger.error(
+            "Could not find the trained model weights at file %s",
+            model_filename,
+        )
+        raise FileNotFoundError("Could not find the trained model weights")
+    model = Spec2Pep().load_from_checkpoint(
+        model_filename,
+        dim_model=config["dim_model"],
+        n_head=config["n_head"],
+        dim_feedforward=config["dim_feedforward"],
+        n_layers=config["n_layers"],
+        dropout=config["dropout"],
+        dim_intensity=config["dim_intensity"],
+        custom_encoder=config["custom_encoder"],
+        max_length=config["max_length"],
+        residues=config["residues"],
+        max_charge=config["max_charge"],
+        n_log=config["n_log"],
+        out_filename=out_filename,
+    )
+    # Read the MS/MS spectra for which to predict peptide sequences.
+    if not os.path.isdir(peak_dir):
+        logger.error(
+            "Could not find directory %s from which to read peak files",
+            peak_dir,
+        )
+        raise FileNotFoundError(
+            "Could not find the directory to read peak files"
+        )
+    peak_filenames = _get_peak_filenames(peak_dir)
+    tmp_dir = tempfile.TemporaryDirectory()
+    idx_filename = os.path.join(tmp_dir.name, f"{uuid.uuid4().hex}.hdf5")
+    if annotated:
+        index = AnnotatedSpectrumIndex(idx_filename, peak_filenames)
+    else:
+        index = SpectrumIndex(idx_filename, peak_filenames)
+    # Initialize the data loader.
+    loaders = DeNovoDataModule(
+        test_index=index,
+        n_peaks=config["n_peaks"],
+        min_mz=config["min_mz"],
+        max_mz=config["max_mz"],
+        min_intensity=config["min_intensity"],
+        remove_precursor_tol=config["remove_precursor_tol"],
+        n_workers=config["num_workers"],
+        batch_size=config["predict_batch_size"],
+    )
+    loaders.setup(stage="test", annotated=annotated)
+    # Create the Trainer object.
+    trainer = pl.Trainer(
+        accelerator="auto",
+        auto_select_gpus=True,
+        devices=-1,
+        logger=config["logger"],
+        max_epochs=config["max_epochs"],
+        num_sanity_val_steps=config["num_sanity_val_steps"],
+        strategy=config["strategy"],
+    )
+    # Run the model with/without validation.
+    if annotated:
+        trainer.validate(model, loaders.test_dataloader())
+    else:
+        trainer.predict(model, loaders.test_dataloader())
+    # Clean up temporary files.
+    tmp_dir.cleanup()
+
+
+def train(
+    peak_dir: str,
+    peak_dir_val: str,
+    model_filename: str,
+    config: Dict[str, Any],
+) -> None:
+    """
+    Train a Casanovo model.
+
+    The model can be trained from scratch or by continuing training an existing
+    model.
+
+    Parameters
+    ----------
+    peak_dir : str
+        The directory with peak files to be used as training data.
+    peak_dir_val : str
+        The directory with peak files to be used as validation data.
+    model_filename : str
+        The file name of the model weights (.ckpt file).
+    config : Dict[str, Any]
+        The configuration options.
+    """
+    # Read the MS/MS spectra to use for training and validation.
+    if not os.path.isdir(peak_dir):
+        logger.error(
+            "Could not find directory %s from which to read peak files",
+            peak_dir,
+        )
+        raise FileNotFoundError(
+            "Could not find the directory to read peak files"
+        )
+    train_filenames = _get_peak_filenames(peak_dir)
+    if peak_dir_val is None or not os.path.isdir(peak_dir_val):
+        logger.error(
+            "Could not find directory %s from which to read validation peak "
+            "files",
+            peak_dir_val,
+        )
+        raise FileNotFoundError(
+            "Could not find the directory to read validation peak files"
+        )
+    val_filenames = _get_peak_filenames(peak_dir_val)
+    tmp_dir = tempfile.TemporaryDirectory()
+    train_idx_filename = os.path.join(tmp_dir.name, f"{uuid.uuid4().hex}.hdf5")
+    val_idx_filename = os.path.join(tmp_dir.name, f"{uuid.uuid4().hex}.hdf5")
+    train_index = AnnotatedSpectrumIndex(train_idx_filename, train_filenames)
+    val_index = AnnotatedSpectrumIndex(val_idx_filename, val_filenames)
+    # Initialize the data loaders.
+    dataloader_params = dict(
+        n_peaks=config["n_peaks"],
+        min_mz=config["min_mz"],
+        max_mz=config["max_mz"],
+        min_intensity=config["min_intensity"],
+        remove_precursor_tol=config["remove_precursor_tol"],
+        batch_size=config["train_batch_size"],
+    )
+    train_loader = DeNovoDataModule(
+        train_index=train_index, **dataloader_params
+    )
+    train_loader.setup()
+    val_loader = DeNovoDataModule(valid_index=val_index, **dataloader_params)
+    val_loader.setup()
+    # Initialize the model.
+    model_params = dict(
+        dim_model=config["dim_model"],
+        n_head=config["n_head"],
+        dim_feedforward=config["dim_feedforward"],
+        n_layers=config["n_layers"],
+        dropout=config["dropout"],
+        dim_intensity=config["dim_intensity"],
+        custom_encoder=config["custom_encoder"],
+        max_length=config["max_length"],
+        residues=config["residues"],
+        max_charge=config["max_charge"],
+        n_log=config["n_log"],
+        tb_summarywriter=config["tb_summarywriter"],
+        warmup_iters=config["warmup_iters"],
+        max_iters=config["max_iters"],
+        lr=config["learning_rate"],
+        weight_decay=config["weight_decay"],
+    )
+    if config["train_from_scratch"]:
+        model = Spec2Pep(**model_params)
+    else:
+        if not os.path.isfile(model_filename):
+            logger.error(
+                "Could not find the model weights at file %s to continue "
+                "training",
+                model_filename,
+            )
+            raise FileNotFoundError(
+                "Could not find the model weights to continue training"
+            )
+        model = Spec2Pep().load_from_checkpoint(model_filename, **model_params)
+    # Create the Trainer object and (optionally) a checkpoint callback to
+    # periodically save the model.
+    if config["save_model"]:
+        callbacks = [
+            pl.callbacks.ModelCheckpoint(
+                dirpath=config["model_save_folder_path"],
+                save_top_k=-1,
+                save_weights_only=config["save_weights_only"],
+                every_n_train_steps=config["every_n_train_steps"],
+            )
+        ]
+    else:
+        callbacks = None
+    trainer = pl.Trainer(
+        accelerator="auto",
+        auto_select_gpus=True,
+        callbacks=callbacks,
+        devices=-1,
+        logger=config["logger"],
+        max_epochs=config["max_epochs"],
+        num_sanity_val_steps=config["num_sanity_val_steps"],
+        strategy=config["strategy"],
+    )
+    # Train the model.
+    trainer.fit(
+        model, train_loader.train_dataloader(), val_loader.val_dataloader()
+    )
+    # Clean up temporary files.
+    tmp_dir.cleanup()
+
+
+def _get_peak_filenames(peak_dir: str) -> List[str]:
+    """
+    Get the peak file names in the given directory.
+
+    Parameters
+    ----------
+    peak_dir : str
+        The directory in which to find peak files.
+
+    Returns
+    -------
+    List[str]
+        The peak file names from the given directory.
+    """
+    return [
+        os.path.join(peak_dir, f)
+        for f in os.listdir(peak_dir)
+        if f.lower().endswith(".mgf")
+    ]
diff --git a/casanovo/version.py b/casanovo/version.py
index 192d666d..d1b7f64e 100644
--- a/casanovo/version.py
+++ b/casanovo/version.py
@@ -1,29 +1,30 @@
-"""Get the package version"""
+"""Package version information."""
+from typing import Optional
 
 
-def _get_version():
-    """Retreive the package version.
+def _get_version() -> Optional[str]:
+    """
+    Retrieve the package version.
 
-    Using setuptools-scm dynamically sets the pacakge version depending on
-    the latest git release and commits since that point. This function
-    returns the current version number.
+    Using setuptools-scm dynamically sets the package version depending on the
+    latest git release and commits since that point. This function returns the
+    current version number.
 
     Returns
     -------
-    str or None
-        The package version number. If not version is found, returns None.
+    Optional[str]
+        The package version number. If not version is found, returns `None`.
     """
     try:
-        # Fast, but only works in Python 3.8+
+        # Fast, but only works in Python 3.8+.
         from importlib.metadata import version, PackageNotFoundError
 
         try:
             return version("casanovo")
         except PackageNotFoundError:
             return None
-
     except ImportError:
-        # Slow, but works for all Python 3+
+        # Slow, but works for all Python 3+.
         from pkg_resources import get_distribution, DistributionNotFound
 
         try:
diff --git a/sample_casanovo_output.csv b/sample_casanovo_output.csv
deleted file mode 100644
index 77edde3d..00000000
--- a/sample_casanovo_output.csv
+++ /dev/null
@@ -1,2049 +0,0 @@
-spectrum_id,denovo_seq,peptide_score,aa_scores
-0,LAHYNKR,0.9912219643592834,"[1.0, 1.0, 1.0, 0.9994825124740601, 0.9966303706169128, 0.942952036857605, 0.9994888305664062]"
-1,VKEDPDGEAHR,0.9659039594910361,"[0.9999778270721436, 0.9998947381973267, 0.9999514818191528, 0.9995558857917786, 1.0, 0.9990235567092896, 1.0, 0.9999366998672485, 0.9990146160125732, 0.6330487132072449, 0.9945400357246399]"
-2,C+57.021GHTNNLRPK,0.9913588762283325,"[0.9993884563446045, 0.9939350485801697, 0.9999914169311523, 0.9999986886978149, 0.9971713423728943, 0.9976969361305237, 0.9509690403938293, 0.9842925667762756, 0.9919559955596924, 0.9981892704963684]"
-3,VVQEQGTHPK,0.9879402279853821,"[1.0, 1.0, 0.9939390420913696, 0.9999966621398926, 0.9973711967468262, 0.999998927116394, 0.891404390335083, 0.9999926090240479, 0.9989234805107117, 0.9977759718894958]"
-4,KGKPELR,0.9913162163325718,"[0.9998069405555725, 0.9998378753662109, 0.9999715089797974, 0.9782547950744629, 0.9980762004852295, 0.995758593082428, 0.9675076007843018]"
-5,SLSHSPGK,0.9932933077216148,"[0.9999998807907104, 0.9994339346885681, 0.9999982118606567, 0.9996663331985474, 0.9999880790710449, 0.9992297887802124, 0.9982958436012268, 0.9497343897819519]"
-6,HNSFTC+57.021DATHK,0.8529938703233545,"[0.9999946355819702, 0.9084645509719849, 0.972660481929779, 0.5957528948783875, 0.26583608984947205, 0.9939258694648743, 0.6476895213127136, 0.9998811483383179, 0.9996131062507629, 0.9999020099639893, 0.9992122650146484]"
-7,HNVC+57.021LEFTAHK,0.7155281034382907,"[0.999997615814209, 0.9914761185646057, 0.9842155575752258, 0.4805639982223511, 0.364273339509964, 0.5825300216674805, 0.2940913736820221, 0.8940796256065369, 0.28478527069091797, 0.9955844283103943, 0.9992117881774902]"
-8,RHDPNYNPR,0.6686893271075355,"[0.948614239692688, 0.6637261509895325, 0.4620034694671631, 0.2485831081867218, 0.6782694458961487, 0.5741631388664246, 0.9591070413589478, 0.7565540075302124, 0.7271833419799805]"
-9,KPANVVTK,0.958288349211216,"[0.9986516833305359, 0.9999995231628418, 0.9999959468841553, 0.9944424033164978, 0.9813941717147827, 0.7045508623123169, 0.9874159693717957, 0.9998562335968018]"
-10,EKEELEK,0.9886301330157689,"[0.9967920184135437, 0.952570915222168, 0.9980810880661011, 0.9867963790893555, 0.9972942471504211, 0.9898158311843872, 0.9990604519844055]"
-11,FAEEDKK,0.9501397354262215,"[1.0, 0.999991774559021, 0.9999946355819702, 0.9999781847000122, 0.9943682551383972, 0.6600027084350586, 0.9966425895690918]"
-12,LGPHTSTR,0.78644248098135,"[0.460183709859848, 0.6111006736755371, 0.2675965130329132, 0.9999779462814331, 0.9955761432647705, 0.9869886636734009, 0.9751155376434326, 0.9950006604194641]"
-13,AHGSGVNMR,0.9260020322269864,"[0.9999964237213135, 0.9999983310699463, 1.0, 0.9583674073219299, 0.7864428162574768, 0.9286864995956421, 0.9788116812705994, 0.7153683304786682, 0.966346800327301]"
-14,KSPEPVR,0.9725440314837864,"[0.9999988079071045, 0.9480262994766235, 0.9999947547912598, 0.9983899593353271, 0.9999818801879883, 0.8618193864822388, 0.9995971322059631]"
-15,RLPSEQLAVTK,0.8176704252308066,"[0.9845380783081055, 0.4313344657421112, 0.183523491024971, 0.7692738175392151, 0.9970876574516296, 0.8148883581161499, 0.8317202925682068, 0.999713122844696, 0.9889383316040039, 0.9936650991439819, 0.9996919631958008]"
-16,LQEEERER,0.9842304214835167,"[1.0, 0.9992741942405701, 0.99998939037323, 0.9991108775138855, 0.984053373336792, 0.9999985694885254, 0.9068989753723145, 0.9845179915428162]"
-17,YREHSEMK,0.6765983067452908,"[0.5360501408576965, 0.35071641206741333, 0.4853982627391815, 0.5405501127243042, 0.9994848966598511, 0.5011968016624451, 0.9996423721313477, 0.9997474551200867]"
-18,NQGAGSPPRK,0.6183782041072845,"[0.8632534146308899, 0.26957130432128906, 0.49739041924476624, 0.7118965983390808, 0.35569408535957336, 0.3174594044685364, 1.0, 0.9561272263526917, 0.5563405156135559, 0.6560490727424622]"
-19,TESPAEGQKEEEK,0.8382585851045755,"[0.9999865293502808, 1.0, 0.9997426867485046, 1.0, 0.9999990463256836, 0.9999725818634033, 0.9999678134918213, 0.8802306652069092, 0.5113235116004944, 0.28185567259788513, 0.34364134073257446, 0.9085699915885925, 0.9720717668533325]"
-20,LVEEETKK,0.9992763698101044,"[0.9999986886978149, 0.9999998807907104, 0.9996046423912048, 0.9999836683273315, 0.9980552196502686, 0.9999936819076538, 0.9985401630401611, 0.9980350136756897]"
-21,KAETLGEK,0.9922817125916481,"[0.9999991655349731, 0.9999955892562866, 0.9999926090240479, 0.9997459053993225, 0.9999542236328125, 0.9990133047103882, 0.940726101398468, 0.998826801776886]"
-22,C+57.021LKPNETK,0.9981318116188049,"[1.0, 0.9999984502792358, 0.9999960660934448, 1.0, 0.9896292090415955, 0.9989757537841797, 0.9966967105865479, 0.9997583031654358]"
-23,GHQALER,0.9994062185287476,"[1.0, 0.9994785189628601, 0.999113142490387, 0.9998847246170044, 0.999934196472168, 0.9992223978042603, 0.9982105493545532]"
-24,TNNLRPK,0.9964359402656555,"[0.999998927116394, 0.9976056814193726, 0.99775630235672, 0.9913167953491211, 0.9999749660491943, 0.9889962673187256, 0.999402642250061]"
-25,GTTPGHALYVSGETGMR,0.8231383860111237,"[0.9989495873451233, 0.9779996275901794, 0.9758014678955078, 0.999998927116394, 0.9999325275421143, 0.9972102046012878, 0.9640188813209534, 0.5256630182266235, 0.38955995440483093, 0.40069225430488586, 0.9884377121925354, 0.8968731760978699, 0.6192965507507324, 0.9439930319786072, 0.9984625577926636, 0.31733766198158264, 0.9991254210472107]"
-26,RPVHTPK,0.8947646277291434,"[0.9992641806602478, 0.7231749296188354, 0.9853095412254333, 0.5938587784767151, 0.9976698756217957, 0.998647153377533, 0.9654279351234436]"
-27,VLEGHAEK,0.9959866628050804,"[0.999998927116394, 0.9999892711639404, 0.9998307228088379, 0.9999979734420776, 0.9999998807907104, 0.9999626874923706, 0.9820907711982727, 0.9860230684280396]"
-28,LQHEAATATQK,0.9854791868816722,"[1.0, 0.9951927661895752, 1.0, 0.9127811193466187, 0.9999983310699463, 0.9999717473983765, 0.9933866858482361, 0.9999104738235474, 0.9984533786773682, 0.9494529962539673, 0.9911235570907593]"
-29,KEAPAPPK,0.9999357834458351,"[0.9999996423721313, 0.9999822378158569, 0.999994158744812, 0.9999990463256836, 0.9999927282333374, 0.9998810291290283, 0.9997281432151794, 0.9999092817306519]"
-30,NPADELR,0.734500412430082,"[0.49399569630622864, 0.48918038606643677, 0.3771635890007019, 0.9392962455749512, 0.8510368466377258, 0.9912490844726562, 0.9995810389518738]"
-31,EVLHHTSR,0.8372023589909077,"[0.9522128701210022, 0.9974778294563293, 0.9979337453842163, 0.8720312714576721, 0.30710914731025696, 0.9961905479431152, 0.577901303768158, 0.9967621564865112]"
-32,NNTVTPNKPNK,0.913565841588107,"[0.9976600408554077, 0.9949073195457458, 0.9999220371246338, 0.9767237901687622, 0.8590580224990845, 1.0, 0.9974392652511597, 0.9357832670211792, 0.9999141693115234, 0.541760265827179, 0.746056079864502]"
-33,MQEHMR,0.9750931759675344,"[0.9999977350234985, 0.9977372884750366, 0.999402642250061, 0.9999675750732422, 0.8613871932029724, 0.9920666217803955]"
-34,HNLYGDSSTTHK,0.8119162817796072,"[1.0, 0.9864597320556641, 0.8747059106826782, 0.8165877461433411, 0.6210476160049438, 0.43453627824783325, 0.4168663024902344, 0.9941539168357849, 0.6301301717758179, 0.9930815100669861, 0.9773813486099243, 0.9980448484420776]"
-35,SAVYAAATC+57.021SGLAC+57.021STR,0.5796941150637234,"[0.7886413931846619, 0.7038338780403137, 0.35466545820236206, 0.3677065074443817, 0.29629063606262207, 0.5542182922363281, 0.9535697102546692, 0.9565771818161011, 0.854369044303894, 0.5149683356285095, 0.37934744358062744, 0.30547231435775757, 0.9977446794509888, 0.3665032386779785, 0.4070641100406647, 0.385432630777359, 0.6683951020240784]"
-36,GDDETLHK,0.9899488240480423,"[1.0, 0.9998577833175659, 0.9988671541213989, 0.9813812971115112, 0.9996992349624634, 0.9937049746513367, 0.9764373302459717, 0.9696428179740906]"
-37,NEKSEEEGASSASVK,0.982702124118805,"[0.9958420395851135, 0.999922513961792, 0.999612033367157, 0.9999991655349731, 0.9999978542327881, 0.9999974966049194, 0.9999992847442627, 0.9999713897705078, 0.7460834980010986, 0.999998927116394, 0.9999985694885254, 0.9997386336326599, 0.9999762773513794, 0.9996134638786316, 0.9997807145118713]"
-38,NNTVTGPGKPNK,0.8792637685934702,"[0.9991729855537415, 0.998418927192688, 0.9999998807907104, 0.9999990463256836, 0.9863802790641785, 1.0, 0.6531091332435608, 0.6077866554260254, 0.8414738178253174, 0.9999053478240967, 0.7078040838241577, 0.7571150660514832]"
-39,HNVQAFTYTHK,0.759482508355921,"[0.9999996423721313, 0.7766081690788269, 0.7304898500442505, 0.5816766619682312, 0.7726457715034485, 0.5617817640304565, 0.5026726722717285, 0.46306347846984863, 0.9680327773094177, 0.9983258843421936, 0.9990109205245972]"
-40,HDSLDER,0.9222563079425267,"[0.9999991655349731, 0.9862290620803833, 0.99709153175354, 0.8522517085075378, 0.800250768661499, 0.9875398874282837, 0.8324320316314697]"
-41,TGSNVGANC+57.021C+57.021NK,0.5733477597435316,"[0.693999707698822, 0.5310713052749634, 0.5175220966339111, 0.6880814433097839, 0.5409841537475586, 0.6186612248420715, 0.7000617384910583, 0.8608092665672302, 0.5391730666160583, 0.3870644271373749, 0.3675348460674286, 0.43520984053611755]"
-42,AVTKYTSSK,0.9959154393937852,"[0.9999994039535522, 0.9983537197113037, 0.9999998807907104, 0.9998414516448975, 0.9994970560073853, 0.9889110326766968, 0.9873049855232239, 0.9960892200469971, 0.9932422041893005]"
-43,TKYTSSK,0.9992796438080924,"[0.9998767375946045, 0.9977595806121826, 0.9999970197677612, 0.9997052550315857, 0.9989991784095764, 0.9988127946853638, 0.9998069405555725]"
-44,SSFSQHAR,0.9995415061712265,"[0.9999997615814209, 1.0, 0.9999822378158569, 0.9999967813491821, 0.9993321299552917, 0.9999736547470093, 0.9973082542419434, 0.9997392296791077]"
-45,DYTSNM+15.995VHK,0.7802668876118131,"[0.9385907649993896, 0.9994577765464783, 0.8957712054252625, 0.6701311469078064, 0.9598993062973022, 0.2912920415401459, 0.27257809042930603, 0.9952811598777771, 0.9994004964828491]"
-46,WEGRPNGMNDVK,0.7989266787966093,"[0.6373469829559326, 0.9634172320365906, 0.9992196559906006, 0.9981682300567627, 0.999451220035553, 0.7343735098838806, 0.999976396560669, 0.587662398815155, 0.2939552366733551, 0.38684600591659546, 0.9878690838813782, 0.9988341927528381]"
-47,EKGSM+15.995SSENDQVVK,0.7232805703367505,"[0.976375937461853, 0.7596964836120605, 0.3617808520793915, 0.5526049137115479, 0.7864323258399963, 0.3881140649318695, 0.3724137544631958, 0.958259105682373, 0.9928075075149536, 0.9996395111083984, 0.5468125939369202, 0.44499915838241577, 0.9867008328437805, 0.999290943145752]"
-48,LKEQEPQDDWSTSK,0.8842137924262455,"[0.9169140458106995, 0.8829958438873291, 0.9952141046524048, 0.635209858417511, 0.9995500445365906, 0.9997996687889099, 0.9855178594589233, 0.8312585949897766, 0.6294379830360413, 0.7764125466346741, 0.9999891519546509, 0.8344773650169373, 0.9245120286941528, 0.9677039980888367]"
-49,NKPGVYTK,0.927485466003418,"[0.9665881991386414, 0.9908066391944885, 1.0, 0.9999946355819702, 0.9899605512619019, 0.893707275390625, 0.5817421078681946, 0.9970843195915222]"
-50,GRPGMLGGY,0.7962324155701531,"[0.8444182276725769, 0.6182407140731812, 0.9953992962837219, 0.6624271869659424, 0.9683675765991211, 0.5474122762680054, 0.5834702849388123, 0.9986250400543213, 0.9477311372756958]"
-51,GHNAVC+57.021SFVVHK,0.7801535253723463,"[0.9978950023651123, 0.9445903897285461, 0.9723736047744751, 0.9152110815048218, 0.4567129909992218, 0.939210057258606, 0.6900410056114197, 0.7593300938606262, 0.3919253647327423, 0.32189932465553284, 0.9762628674507141, 0.9963905215263367]"
-52,MLDHEGK,0.9882968153272357,"[1.0, 0.9999974966049194, 0.9626772403717041, 0.9999761581420898, 0.9836212396621704, 0.9720545411109924, 0.9997510313987732]"
-53,GGM+15.995PHLSK,0.8578436300158501,"[0.9999786615371704, 0.7326479554176331, 0.626594603061676, 0.9999479055404663, 0.9972150325775146, 0.9913243055343628, 0.5161526203155518, 0.9988879561424255]"
-54,RVPAEEVK,0.8368801325559616,"[0.9999996423721313, 0.5020327568054199, 0.973760187625885, 0.7923621535301208, 0.47694528102874756, 0.9960954785346985, 0.9980443716049194, 0.9558011889457703]"
-55,FDDGNSQR,0.7490895576775074,"[0.9999970197677612, 0.9565611481666565, 0.5850563645362854, 0.9999288320541382, 0.8259735703468323, 0.35408154129981995, 0.28137528896331787, 0.9897426962852478]"
-56,DGSTDEDKNEAK,0.6575657874345779,"[0.8670092821121216, 0.36295434832572937, 0.8941389918327332, 0.9477260708808899, 0.4100567400455475, 0.9087851047515869, 0.5746687054634094, 0.39352187514305115, 0.2723146378993988, 0.2973824739456177, 0.9651026725769043, 0.9971285462379456]"
-57,KQHSLLK,0.9984437227249146,"[0.9993042945861816, 0.9959994554519653, 0.9999518394470215, 0.9999048709869385, 0.9967857599258423, 0.9974861145019531, 0.9996737241744995]"
-58,STAVSSEAAK,0.6430553063750267,"[0.9921749234199524, 0.7964262366294861, 0.6146021485328674, 0.7241789102554321, 0.3718961775302887, 0.89532470703125, 0.3332196772098541, 0.4910113513469696, 0.2233705073595047, 0.9883484244346619]"
-59,RNVVPSSGSK,0.7183170065283775,"[0.9968330264091492, 0.6435551643371582, 0.23991920053958893, 0.9989480376243591, 0.9909633994102478, 0.46065062284469604, 0.7764064073562622, 0.7383552193641663, 0.3477347195148468, 0.9898042678833008]"
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-911,M+15.995LQPPAGNPVDLM+15.995LN,0.7953609387079875,"[0.997829020023346, 0.9981624484062195, 0.9975219368934631, 0.4424609839916229, 0.8810054659843445, 0.8920279145240784, 0.6062983274459839, 0.9456780552864075, 0.47297170758247375, 0.8769082427024841, 0.7819864749908447, 0.8054987192153931, 0.9324308633804321, 0.5414624214172363, 0.7581714987754822]"
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-921,GTTTTTSESEVELLRDVEENHR,0.562256550247019,"[0.7840589880943298, 0.4144870638847351, 0.6968275904655457, 0.3180992603302002, 0.7756465673446655, 0.8170614242553711, 0.9632393717765808, 0.879152238368988, 0.9020214080810547, 0.9074816703796387, 0.5406917929649353, 0.2169286012649536, 0.3436925709247589, 0.4003206491470337, 0.6394585371017456, 0.263367235660553, 0.156332865357399, 0.2434924691915512, 0.4440346360206604, 0.40694043040275574, 0.2591298222541809, 0.9971789121627808]"
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-923,GDDLQALK,0.9201437681913376,"[1.0, 0.9531538486480713, 0.9972476363182068, 0.5363038182258606, 0.9969735145568848, 0.9952242374420166, 0.9580148458480835, 0.9242322444915771]"
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-1096,SDGALSLAEHFYADNQFGEQLQK,0.7451136837834897,"[0.9999933242797852, 0.9647625088691711, 0.6831807494163513, 0.9785799980163574, 0.5010948181152344, 0.7800768613815308, 0.9020459651947021, 0.5653560161590576, 0.4900420904159546, 0.9321841597557068, 0.9838401675224304, 0.43238499760627747, 0.4031831622123718, 0.4671683609485626, 0.5418757796287537, 0.9408293962478638, 0.43030214309692383, 0.9999748468399048, 0.8412514925003052, 0.42330944538116455, 0.9998012185096741, 0.9282078742980957, 0.9481693506240845]"
-1097,EATSLTKPGGLSDDELER,0.85777923795912,"[0.9835281372070312, 0.5025270581245422, 0.6853865385055542, 0.8240894079208374, 0.6102957725524902, 0.985937774181366, 0.9999406337738037, 0.9999921321868896, 0.99959796667099, 0.9999175071716309, 0.9346086382865906, 0.6378364562988281, 0.7839637994766235, 0.7350119948387146, 0.999294638633728, 0.9884843230247498, 0.8139852285385132, 0.9556282758712769]"
-1098,GPTPSAEAAAAALDYK,0.8230829164385796,"[0.9999954700469971, 0.7962405681610107, 0.9903438091278076, 0.9999356269836426, 0.9984316229820251, 0.6855748891830444, 0.8975585103034973, 0.999921441078186, 0.9701834917068481, 0.9999637603759766, 0.6100790500640869, 0.2789432406425476, 0.7423277497291565, 0.5229607224464417, 0.8690587878227234, 0.8078079223632812]"
-1099,ELLPGTLK,0.7353307940065861,"[0.4718499481678009, 0.48344242572784424, 0.82764732837677, 0.9999966621398926, 0.9919390082359314, 0.6883538365364075, 0.5750752687454224, 0.8443418741226196]"
-1100,LDC+57.021TADSPDTFGEGVR,0.8061762191355228,"[0.990298330783844, 0.9499971866607666, 0.9449257850646973, 0.9999701976776123, 0.9999542236328125, 0.9214856028556824, 0.9912601113319397, 0.6765913963317871, 0.8731085658073425, 0.3653110861778259, 0.41632694005966187, 0.6017098426818848, 0.42003750801086426, 0.9963029623031616, 0.7556597590446472, 0.9958800077438354]"
-1101,ALDPSLTGADAQHAK,0.8751942336559295,"[1.0, 0.9881588816642761, 0.9794663190841675, 0.9999983310699463, 0.9996637105941772, 0.9999995231628418, 0.9611412286758423, 0.48646852374076843, 0.5123136639595032, 0.7959467768669128, 0.9999988079071045, 0.7586986422538757, 0.7458066940307617, 0.9177059531211853, 0.9825464487075806]"
-1102,SDGAGVADYDQVSDAYHMAGADLQK,0.8535436689853668,"[0.9999998807907104, 0.999146580696106, 0.6097456216812134, 0.9992425441741943, 0.4723138213157654, 0.8115198612213135, 0.943560779094696, 0.4854902923107147, 0.8671950697898865, 0.885698139667511, 0.7751295566558838, 0.7309402227401733, 0.7480878233909607, 0.999816358089447, 0.7087501287460327, 0.6113789677619934, 0.9238481521606445, 0.9998985528945923, 0.9911963939666748, 0.9147035479545593, 0.8848319053649902, 0.9999345541000366, 0.9999897480010986, 0.9987598657608032, 0.9774133563041687]"
-1103,QSLPPEDDGNFDAK,0.7324105799198151,"[0.9909105896949768, 0.624476969242096, 0.9998977184295654, 0.9058577418327332, 0.32812151312828064, 0.6097860932350159, 0.863983690738678, 0.42190590500831604, 0.9876020550727844, 0.3441827893257141, 0.8166645765304565, 0.46271324157714844, 0.9020510315895081, 0.9955942034721375]"
-1104,C+57.021QQENAELLR,0.927880984544754,"[0.9999537467956543, 0.8880952000617981, 0.9966922998428345, 0.9999881982803345, 0.979146420955658, 0.514030396938324, 0.9998769760131836, 0.946295440196991, 0.9949021339416504, 0.9598290324211121]"
-1105,AADPPAENSSAPEAEQGGAE,0.9965685963630676,"[0.9999991655349731, 0.9947298765182495, 0.9999842643737793, 0.9998144507408142, 0.9990835189819336, 0.9999979734420776, 0.9999810457229614, 0.991364598274231, 0.9792872667312622, 0.9978850483894348, 0.9979168772697449, 0.9999988079071045, 1.0, 0.9999992847442627, 0.9996899366378784, 0.9952752590179443, 0.999997615814209, 0.9784604907035828, 0.9999741315841675, 0.9979323148727417]"
-1106,PATAALSPEPNESK,0.9293866945164544,"[0.9584288001060486, 0.4791392385959625, 0.9999978542327881, 0.9999767541885376, 0.998984158039093, 0.9999920129776001, 0.9999233484268188, 0.9999256134033203, 0.9998598098754883, 0.9999872446060181, 0.9917641282081604, 0.7903327941894531, 0.9857697486877441, 0.8073322176933289]"
-1107,TGASVVLAR,0.9140982230504354,"[0.9999947547912598, 0.6521041393280029, 0.9994515776634216, 0.9994513392448425, 0.9957894682884216, 0.9994463324546814, 0.9911495447158813, 0.619354784488678, 0.9701420664787292]"
-1108,DTM+15.995TPELAEGK,0.7851462878964164,"[0.4628608524799347, 0.9708205461502075, 0.9993256330490112, 0.9999886751174927, 0.9999245405197144, 0.996778666973114, 0.9984163045883179, 0.3778232932090759, 0.4504427909851074, 0.7521683573722839, 0.6280595064163208]"
-1109,ETGTTGSLAGAPEDER,0.9554988797754049,"[0.9965670108795166, 0.44392451643943787, 0.9998217225074768, 0.9563500285148621, 0.9995582699775696, 0.9736504554748535, 0.9921229481697083, 0.9999992847442627, 0.9998576641082764, 0.999993085861206, 0.9999890327453613, 0.9999812841415405, 0.9999619722366333, 0.9584049582481384, 0.9855013489723206, 0.9822984933853149]"
-1110,PATAALSPEPQDSK,0.9543154537677765,"[0.9999207258224487, 0.8824517130851746, 0.9999996423721313, 0.9995525479316711, 0.9999768733978271, 0.9999871253967285, 0.9999468326568604, 0.9999293088912964, 0.957781195640564, 1.0, 0.9982309937477112, 0.889203667640686, 0.9932597279548645, 0.640175998210907]"
-1111,GTGDDNDGPVLTDEGAK,0.9012820808326497,"[0.9982593655586243, 0.574580192565918, 0.9671167135238647, 0.31777361035346985, 0.9672346115112305, 0.8917782306671143, 0.9981217980384827, 1.0, 0.9999948740005493, 0.9666444063186646, 0.9999996423721313, 0.9999696016311646, 0.9764142036437988, 0.9999986886978149, 0.9984294772148132, 0.6656197905540466, 0.9998601675033569]"
-1112,AEEMVEAAGGFSHS,0.7903201792921338,"[0.9999973773956299, 0.9999876022338867, 0.9999972581863403, 0.32812467217445374, 0.9673274755477905, 0.9965062141418457, 0.9999994039535522, 0.9999961853027344, 0.9990009665489197, 0.8891390562057495, 0.3757626414299011, 0.6634413599967957, 0.3151644170284271, 0.5300378799438477]"
-1113,RAPM+15.995YGTGASESALK,0.8865009228388468,"[0.9127936959266663, 0.9829476475715637, 0.9369404911994934, 0.7109171748161316, 0.9271842837333679, 0.8374020457267761, 0.9769932627677917, 0.98311448097229, 0.6988263130187988, 0.8649422526359558, 0.7345248460769653, 0.9937769174575806, 0.7878484129905701, 0.9748157262802124, 0.9744862914085388]"
-1114,TAQC+57.021FLR,0.961627185344696,"[0.9999943971633911, 0.9993834495544434, 0.9976882934570312, 0.9999934434890747, 0.9974696636199951, 0.7400846481323242, 0.9967764019966125]"
-1115,VELLANDQGNR,0.9762822118672457,"[1.0, 0.9999806880950928, 1.0, 1.0, 1.0, 0.995595395565033, 0.9999996423721313, 0.994902491569519, 0.9285877346992493, 0.9811024069786072, 0.8389359712600708]"
-1116,WKPPTEAGGSLK,0.666101947426796,"[0.9999997615814209, 0.9999988079071045, 1.0, 0.9999934434890747, 0.9902395009994507, 0.37214142084121704, 0.41516977548599243, 0.5059210062026978, 0.2542778253555298, 0.19627490639686584, 0.31277570128440857, 0.9464312195777893]"
-1117,GNLGAGDAHETLR,0.6451577177414527,"[0.9989616870880127, 0.6077269315719604, 0.8525088429450989, 0.9197281002998352, 0.7064283490180969, 0.39689528942108154, 0.40774697065353394, 0.35692134499549866, 0.8246888518333435, 0.8503509759902954, 0.3484216630458832, 0.4057347774505615, 0.7109365463256836]"
-1118,HESMLTDLEER,0.9181898019530557,"[0.9999996423721313, 0.999739944934845, 0.7703789472579956, 0.8771711587905884, 0.9970809817314148, 0.9658967852592468, 0.9874842762947083, 0.9368935823440552, 0.9565920233726501, 0.8771259188652039, 0.7317245602607727]"
-1119,EVQHLATNDPK,0.729995686899532,"[0.5725464224815369, 0.47322213649749756, 0.35149189829826355, 0.9982030391693115, 0.9999923706054688, 0.9998934268951416, 0.5352506041526794, 0.7693580985069275, 0.9123157262802124, 0.913594663143158, 0.5040841698646545]"
-1120,AKSDAESEVAALNR,0.8553865807397025,"[0.9894104599952698, 0.9999021291732788, 0.5260857939720154, 0.773187518119812, 0.9999966621398926, 0.9999985694885254, 0.9989429116249084, 0.9936338663101196, 0.841010332107544, 0.6407808661460876, 0.8228302597999573, 0.992382824420929, 0.3975604772567749, 0.9996894598007202]"
-1121,QEYDESGPSLVHR,0.9992148005045377,"[0.9944666028022766, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9999948740005493, 0.9999738931655884, 0.9999827146530151, 0.9955940842628479, 0.9997802376747131]"
-1122,VPEEKDQTDDM+15.995VPVEE,0.8143512308597565,"[0.552964448928833, 0.954288899898529, 0.9941256046295166, 0.9957318902015686, 0.46172985434532166, 0.39411482214927673, 0.7540388107299805, 0.9742892384529114, 0.9691215753555298, 0.999987006187439, 0.7039138078689575, 0.7215157747268677, 0.9999997615814209, 0.9991881251335144, 0.9920157194137573, 0.5625943541526794]"
-1123,TNMPQEAPDDSGDMK,0.926728383700053,"[0.999998927116394, 0.9989444613456726, 0.9988899827003479, 1.0, 0.9801042079925537, 0.99981290102005, 0.9999579191207886, 0.9999991655349731, 0.9997662901878357, 0.9957534074783325, 0.9999942779541016, 0.955886960029602, 0.5188083648681641, 0.9873185753822327, 0.4656903147697449]"
-1124,M+15.995LQPSAGSGFRPDNF,0.9218270301818847,"[0.9999494552612305, 0.9947787523269653, 0.9630599021911621, 0.999485969543457, 0.8639819622039795, 0.9408002495765686, 0.9758847951889038, 0.7672638297080994, 0.962115466594696, 0.9999979734420776, 0.9986541271209717, 0.8336234092712402, 0.9807167649269104, 0.7934662699699402, 0.7536265254020691]"
-1125,TGAMALEPVC+57.021TLLEPN,0.7047943565994501,"[0.9923946857452393, 0.4389897286891937, 0.27789226174354553, 0.6364545226097107, 0.7049362063407898, 0.9828177690505981, 0.38743066787719727, 0.8515393733978271, 0.9912803769111633, 0.9855687022209167, 0.5343721508979797, 0.4453899562358856, 0.6054275631904602, 0.9567063450813293, 0.5886259078979492, 0.896883487701416]"
-1126,EEPVEKGEPVPEGAPC+57.021K,0.8333551813574398,"[0.9999994039535522, 0.9998206496238708, 0.9870986342430115, 0.7294648289680481, 0.9869416356086731, 0.620315670967102, 0.45832234621047974, 0.5043742060661316, 0.9957810640335083, 0.9907140731811523, 1.0, 0.9999423027038574, 0.9999984502792358, 0.9222601652145386, 0.7503716945648193, 0.8464487195014954, 0.37518423795700073]"
-1127,LLDDC+57.021HNNAR,0.882547402381897,"[0.8374572396278381, 0.9753853678703308, 0.8640888929367065, 0.9978912472724915, 0.9987969398498535, 0.9941262006759644, 0.928265392780304, 0.5224505662918091, 0.7158615589141846, 0.9911506175994873]"
-1128,HLQLALR,0.9989919577326093,"[0.9999998807907104, 0.9998108744621277, 0.9992641806602478, 0.9999439716339111, 0.999754011631012, 0.9945255517959595, 0.9996452331542969]"
-1129,QQAAELVSR,0.9320894281069437,"[0.9475790858268738, 0.9978787899017334, 0.9999914169311523, 0.999840259552002, 0.9424229860305786, 0.9819102883338928, 0.5762898325920105, 0.9434767961502075, 0.999415397644043]"
-1130,STAGDTHLGGEDFDNR,0.9997706636786461,"[1.0, 1.0, 0.9999988079071045, 0.9999997615814209, 1.0, 1.0, 1.0, 1.0, 1.0, 0.999995231628418, 1.0, 1.0, 0.9999998807907104, 0.999995231628418, 0.9993590712547302, 0.9969826340675354]"
-1131,LLQC+57.021VGSEESR,0.9565955996513367,"[0.9999994039535522, 0.9999982118606567, 0.9884917736053467, 0.9920424818992615, 0.9828113317489624, 0.9983206391334534, 0.9981180429458618, 0.9137608408927917, 0.7302385568618774, 0.9287687540054321, 0.9900015592575073]"
-1132,WDQTADQTPQTPK,0.9874970316886902,"[1.0, 0.9999977350234985, 0.9202284812927246, 0.999998927116394, 0.9999997615814209, 0.9821229577064514, 0.9966225624084473, 0.9995393753051758, 1.0, 0.9842738509178162, 0.9999880790710449, 0.9972545504570007, 0.957435131072998]"
-1133,LAADDFR,0.9717104179518563,"[0.9999997615814209, 0.9999967813491821, 0.9999958276748657, 0.9837850332260132, 0.8201013207435608, 0.9996399879455566, 0.998454213142395]"
-1134,LLASGAANFPQFLGH,0.8181459188461304,"[0.5307866334915161, 0.9979919195175171, 0.9718267321586609, 0.9169719815254211, 0.3560120463371277, 0.9179141521453857, 0.6210906505584717, 0.5935691595077515, 0.978879988193512, 0.9983454942703247, 0.989602267742157, 0.9659333825111389, 0.46144241094589233, 0.9997641444206238, 0.9720578193664551]"
-1135,SFAAGGDEPAASAAGSR,0.7960118984474855,"[0.9999990463256836, 0.9935616254806519, 0.7853266000747681, 0.9985291957855225, 0.9955955147743225, 0.928923487663269, 0.5037127733230591, 0.7864593863487244, 0.507615327835083, 0.6078608632087708, 0.9997716546058655, 0.9072206020355225, 0.8737238049507141, 0.4402106702327728, 0.5953003168106079, 0.6863084435462952, 0.9220829606056213]"
-1136,PGFEPNLK,0.647773053497076,"[0.9931349754333496, 0.9913741946220398, 0.6588317155838013, 0.4760192930698395, 0.48340973258018494, 0.3153380751609802, 0.3208555281162262, 0.9432209134101868]"
-1137,AMVSSSSNSSGSSSSLPSAEK,0.9503660911605472,"[0.9999873638153076, 0.8764172196388245, 0.8222758769989014, 0.9061028957366943, 0.9838612079620361, 0.9736453294754028, 0.9966164231300354, 0.8898448944091797, 0.9329075813293457, 0.9997848868370056, 0.7007965445518494, 0.9592445492744446, 0.9985001087188721, 0.9999361038208008, 0.9665287137031555, 0.9998257756233215, 0.9992024302482605, 0.9999927282333374, 0.9996670484542847, 0.9931097030639648, 0.9594405293464661]"
-1138,DGQVEVGFGELRAESSK,0.5111733490929884,"[0.5368124842643738, 0.9995694756507874, 0.9646759629249573, 0.27403414249420166, 0.19109877943992615, 0.6899444460868835, 0.9991641044616699, 0.33223140239715576, 0.36447227001190186, 0.3753769099712372, 0.42567893862724304, 0.27033519744873047, 0.2554216980934143, 0.19329248368740082, 0.7978528141975403, 0.4529823958873749, 0.5670034289360046]"
-1139,NGGLVLNYK,0.9778691464000278,"[0.9905974268913269, 0.9999972581863403, 0.9470697641372681, 0.8965860605239868, 0.9898945689201355, 0.9998262524604797, 0.9987039566040039, 0.9830511212348938, 0.9950959086418152]"
-1140,LMEDEETDNAYK,0.935042197505633,"[0.9999995231628418, 0.999976634979248, 0.9999330043792725, 0.9904370903968811, 0.9999910593032837, 0.9999992847442627, 0.914384663105011, 0.9696229100227356, 0.9876341819763184, 0.5000306963920593, 0.9687464237213135, 0.8897508978843689]"
-1141,TAQC+57.021FLR,0.9440069879804339,"[0.9999998807907104, 0.9999316930770874, 0.9966967105865479, 0.9994757771492004, 0.9994919300079346, 0.6274962425231934, 0.984956681728363]"
-1142,LSEEHAEN+0.984FR,0.6296799868345261,"[0.6654052138328552, 0.9630916118621826, 0.899215817451477, 0.3019265830516815, 0.5284245014190674, 0.39191871881484985, 0.22032278776168823, 0.9674445390701294, 0.840765118598938, 0.5182849764823914]"
-1143,LREEM+15.995LVDFR,0.6316052436828613,"[0.9821516275405884, 0.434773325920105, 0.37514838576316833, 0.3129729628562927, 0.3879493176937103, 0.5542541742324829, 0.7778736352920532, 0.6852154731750488, 0.8570187091827393, 0.9486948251724243]"
-1144,LLHTSVWAGQK,0.9370814074169506,"[1.0, 1.0, 1.0, 0.9999984502792358, 1.0, 0.9999653100967407, 0.9999837875366211, 0.9994146823883057, 0.9977700710296631, 0.7851393818855286, 0.5256237983703613]"
-1145,C+57.021VMQPAYEDVNNC+57.021HER,0.9379547797143459,"[0.9973065853118896, 0.9980864524841309, 0.9426368474960327, 0.5618374943733215, 1.0, 0.9999916553497314, 0.9694874286651611, 0.9999152421951294, 0.9999097585678101, 0.999996542930603, 0.9871912002563477, 0.9938740134239197, 0.772705614566803, 0.9996556043624878, 0.8036748766899109, 0.9810071587562561]"
-1146,YLNSSPATLDSR,0.8920047134160995,"[0.9957350492477417, 0.5342226028442383, 0.601302444934845, 0.9954519867897034, 0.9999991655349731, 1.0, 0.9999996423721313, 0.9998888969421387, 0.9999526739120483, 0.9648271799087524, 0.6529590487480164, 0.959717869758606]"
-1147,WKPPTEAGGSLK,0.6601328328251839,"[1.0, 0.9999997615814209, 1.0, 0.9999762773513794, 0.9701054096221924, 0.38466283679008484, 0.34911417961120605, 0.46510717272758484, 0.2446761131286621, 0.21398308873176575, 0.3151286840438843, 0.9788404703140259]"
-1148,FVPDKGSSFEDR,0.8777104367812475,"[0.9999990463256836, 0.9997336268424988, 1.0, 0.9999419450759888, 0.9119085073471069, 0.9980981945991516, 0.6594800353050232, 0.8725323677062988, 0.38867777585983276, 0.8595039248466492, 0.9694198369979858, 0.87322998046875]"
-1149,ASGNYATVLSHNPETK,0.9952789209783077,"[1.0, 0.9520254731178284, 1.0, 0.9952754974365234, 1.0, 1.0, 1.0, 0.9999955892562866, 1.0, 1.0, 0.999994158744812, 0.9988468885421753, 0.9999992847442627, 0.9911450743675232, 0.9877737164497375, 0.9994070529937744]"
-1150,SSGSPYGGGYGSGGGSGGYGSR,0.9979981644587084,"[0.9999998807907104, 0.9999991655349731, 0.9999514818191528, 0.9999254941940308, 1.0, 0.9999845027923584, 0.9999531507492065, 0.9999960660934448, 0.9999935626983643, 0.999998927116394, 1.0, 0.9999274015426636, 0.9999896287918091, 0.9999998807907104, 1.0, 0.9999890327453613, 0.9999897480010986, 0.9999352693557739, 0.9988723397254944, 0.9978759288787842, 0.9925992488861084, 0.966978907585144]"
-1151,EVVDHLLM+15.995ELN,0.7495217810977589,"[0.7500059008598328, 0.6108907461166382, 0.9794634580612183, 0.983394980430603, 0.32844191789627075, 0.6506565809249878, 0.976989209651947, 0.9836079478263855, 0.30428385734558105, 0.9840173125267029, 0.6929876804351807]"
-1152,LC+57.021DEC+57.021NYGSYQGR,0.9963240577624395,"[1.0, 1.0, 0.9999715089797974, 0.9999213218688965, 0.9999991655349731, 0.993757963180542, 0.9999991655349731, 1.0, 0.9999992847442627, 0.9999842643737793, 0.9933233857154846, 0.9836286902427673, 0.9816280007362366]"
-1153,SQC+57.021PPFSFLNYK,0.6944149434566498,"[0.7689288258552551, 0.9381482005119324, 0.38971951603889465, 0.5545749068260193, 0.5966203212738037, 0.5698306560516357, 0.28870007395744324, 0.735184907913208, 0.606652557849884, 0.9947803020477295, 0.9433799386024475, 0.9464591145515442]"
-1154,LVVDSHVQYR,0.9824102580547333,"[1.0, 0.9999990463256836, 1.0, 0.8973495960235596, 0.9999985694885254, 0.9999998807907104, 0.9998936653137207, 0.9958998560905457, 0.9611718654632568, 0.9697901010513306]"
-1155,ELEAELEDREK,0.9226463382894342,"[0.9999500513076782, 0.9999984502792358, 0.999697208404541, 0.9999985694885254, 0.9982258677482605, 0.9999997615814209, 0.9999954700469971, 0.9999911785125732, 0.9987768530845642, 0.6015600562095642, 0.5509162545204163]"
-1156,LQNMVDM+15.995EVR,0.8128873780369759,"[1.0, 0.9837726950645447, 0.9816670417785645, 0.9953014850616455, 0.8141446709632874, 0.9708659648895264, 0.44204819202423096, 0.8020334839820862, 0.2488892823457718, 0.8901509642601013]"
-1157,MANAC+57.021LPPLVDQAK,0.7902572538171496,"[0.4009198546409607, 0.9999996423721313, 0.785146951675415, 0.9995001554489136, 0.7278288006782532, 0.4893885552883148, 0.9999990463256836, 0.991801917552948, 0.7976648211479187, 0.9983468055725098, 0.9993042945861816, 0.6403734683990479, 0.39424046874046326, 0.8390867710113525]"
-1158,VVAEVYDQER,0.9821861267089844,"[0.9999994039535522, 1.0, 1.0, 0.9999884366989136, 0.9999861717224121, 0.9999997615814209, 0.9850849509239197, 0.9926347136497498, 0.9981391429901123, 0.8460286855697632]"
-1159,SDPSGSAGLSSPLQAGAR,0.5973003796405263,"[0.747985303401947, 0.8988316655158997, 0.3781934082508087, 0.7662035822868347, 0.7333328127861023, 0.999826967716217, 0.9812000393867493, 0.3847912847995758, 0.2604438364505768, 0.9995254278182983, 0.5051353573799133, 0.4704110622406006, 0.6086476445198059, 0.38860419392585754, 0.2123659998178482, 0.20888280868530273, 0.20922502875328064, 0.9978004097938538]"
-1160,QLQDELQSQR,0.9575140178203583,"[0.9885784387588501, 0.9991341233253479, 0.9977512955665588, 0.9997482895851135, 0.9991989731788635, 0.9998608827590942, 0.9988707900047302, 0.9889891743659973, 0.6040711402893066, 0.9989370703697205]"
-1161,HLQLALR,0.9978791304997036,"[0.9999998807907104, 0.999832272529602, 0.9993366599082947, 0.9999853372573853, 0.9997372031211853, 0.9866296052932739, 0.9996329545974731]"
-1162,SC+57.021QTAGLATAESGTAGAR,0.9325299163659414,"[1.0, 0.9635413289070129, 0.710830807685852, 0.9955593347549438, 0.9999923706054688, 0.9999995231628418, 0.9999802112579346, 0.9998644590377808, 0.9999998807907104, 0.9999344348907471, 0.999990701675415, 0.9994184970855713, 0.9999865293502808, 0.9488118886947632, 0.9598827362060547, 0.5091902613639832, 0.729290246963501, 0.9692652821540833]"
-1163,VESTSVGSLSPNAK,0.9585195524351937,"[0.9999998807907104, 0.9977163076400757, 1.0, 0.9595826268196106, 1.0, 0.9999167919158936, 0.9999998807907104, 0.9999978542327881, 0.9999995231628418, 0.9995341300964355, 0.9996386766433716, 0.578984260559082, 0.8992111086845398, 0.9846926927566528]"
-1164,DAAYVLEGK,0.9180472758081224,"[0.9984652996063232, 0.9561055302619934, 0.697941243648529, 0.8786100149154663, 0.9989497065544128, 0.9989425539970398, 0.930537223815918, 0.8065503239631653, 0.9963235855102539]"
-1165,LNPSLHLGNSNSSDSR,0.8212696686387062,"[0.9687963724136353, 0.7918494343757629, 0.36715051531791687, 0.8711057305335999, 0.8552770614624023, 0.9809799790382385, 0.9883908033370972, 0.9653692841529846, 0.6239531636238098, 0.999956488609314, 0.3944004476070404, 0.9933019876480103, 0.8261846303939819, 0.8253483772277832, 0.6896630525588989, 0.9985873699188232]"
-1166,VYELC+57.021VSGDLSETPVER,0.8251777712036582,"[0.6726588606834412, 0.8957741856575012, 0.6293444037437439, 0.7617266178131104, 0.9045284986495972, 0.5361821055412292, 0.7182632088661194, 0.822321355342865, 0.9911878108978271, 0.9999866485595703, 0.9999990463256836, 0.9999958276748657, 0.9999377727508545, 0.9999313354492188, 0.7114813327789307, 0.8109499216079712, 0.5737531781196594]"
-1167,SC+57.021SNTTLLGEENDGK,0.791687806447347,"[0.9956908822059631, 0.9757846593856812, 0.548402726650238, 0.6539705991744995, 0.4964337944984436, 0.6843969821929932, 0.9870945811271667, 0.9975042939186096, 0.9798414707183838, 0.7340220212936401, 0.39769548177719116, 0.9861040711402893, 0.9977218508720398, 0.6483726501464844, 0.7922810316085815]"
-1168,LLHDEDSLVTEDK,0.8894227972397437,"[0.9999940395355225, 0.9999881982803345, 0.9999536275863647, 0.9990209341049194, 0.9911531805992126, 0.6097592711448669, 0.9597655534744263, 0.5008854269981384, 0.6317441463470459, 0.9998044371604919, 0.9267024397850037, 0.9763048887252808, 0.967420220375061]"
-1169,DGQVLNETSQHHDDLE,0.9853257313370705,"[0.9995986819267273, 0.9999985694885254, 0.9606397747993469, 1.0, 1.0, 0.9871197938919067, 0.9984034895896912, 0.9999998807907104, 1.0, 0.9983762502670288, 0.9999943971633911, 0.9999816417694092, 0.9997047781944275, 0.9999407529830933, 0.9543120265007019, 0.8671416640281677]"
-1170,AEEQLLEVTK,0.618353345990181,"[0.9832004308700562, 0.9386157393455505, 0.7196911573410034, 0.5173966288566589, 0.6699603199958801, 0.4280722439289093, 0.3710043132305145, 0.36130109429359436, 0.36583244800567627, 0.8284590840339661]"
-1171,N+0.984GQVNGDC+57.021DDDQLELK,0.8441182617098093,"[0.9628706574440002, 0.9999969005584717, 0.951267421245575, 0.9637246131896973, 0.5494242310523987, 0.4736489951610565, 0.802149772644043, 0.7746019959449768, 0.5506918430328369, 0.6033678650856018, 0.9873824715614319, 0.9292006492614746, 0.9954818487167358, 0.9979861974716187, 0.9882072806358337, 0.9758894443511963]"
-1172,GAVDEVFYKPGPTGK,0.7102591395378113,"[0.9923741221427917, 0.5854184031486511, 0.9970318078994751, 0.6935095191001892, 0.31208711862564087, 0.6808099746704102, 0.3606718182563782, 0.45549893379211426, 0.9996718168258667, 0.9999979734420776, 0.8602099418640137, 0.7325199842453003, 0.7141024470329285, 0.27283382415771484, 0.9971494078636169]"
-1173,APASVDDDDEDDNDPENR,0.8653662072287666,"[0.9999964237213135, 0.999607503414154, 0.6490744352340698, 0.8756099939346313, 0.9851711392402649, 0.2613392472267151, 0.47049474716186523, 0.8871017694473267, 0.98408442735672, 0.9542871713638306, 0.536628007888794, 0.9900451302528381, 0.989285409450531, 0.999964714050293, 0.9999998807907104, 0.9999986886978149, 0.9985069632530212, 0.995396077632904]"
-1174,GVTQVVPC+57.021AGVENEVR,0.7989435717463493,"[0.990643322467804, 0.9983627200126648, 0.998314380645752, 0.9870424270629883, 0.8872116804122925, 0.9257714748382568, 0.9998282194137573, 0.7726238965988159, 0.332777738571167, 0.779008150100708, 0.36030107736587524, 0.9640553593635559, 0.5358619093894958, 0.8444169163703918, 0.4324517846107483, 0.9744260907173157]"
-1175,HLQLALR,0.9983077730451312,"[0.9999992847442627, 0.999775230884552, 0.9991517066955566, 0.9999747276306152, 0.9998041987419128, 0.9897106289863586, 0.9997386336326599]"
-1176,HALQSASAGSGSFTDVR,0.9511861520655015,"[1.0, 0.999503493309021, 1.0, 0.990770161151886, 0.9998704195022583, 0.9999902248382568, 0.9999996423721313, 0.9999849796295166, 0.9999749660491943, 0.9998137354850769, 0.9923984408378601, 0.859874427318573, 0.885872483253479, 0.8253267407417297, 0.999674916267395, 0.6176553964614868, 0.9994545578956604]"
-1177,NGLGAGDGNLQGPR,0.9980422173227582,"[0.9969209432601929, 0.9938673377037048, 0.9999980926513672, 0.9999994039535522, 1.0, 1.0, 0.9952884912490845, 1.0, 0.999933123588562, 0.9999994039535522, 0.9998070597648621, 0.9990413784980774, 0.9917469024658203, 0.9959889054298401]"
-1178,KEEELQAALAR,0.988316227089275,"[0.9999997615814209, 0.999174177646637, 0.999572217464447, 0.9998331069946289, 0.9998610019683838, 0.876081109046936, 0.9998413324356079, 0.9999972581863403, 0.9994233846664429, 0.9983426332473755, 0.9993525147438049]"
-1179,LC+57.021DEC+57.021NYGSYQGR,0.9825456279974717,"[1.0, 1.0, 0.9994045495986938, 0.999955415725708, 0.9986026883125305, 0.9877721071243286, 0.9999890327453613, 0.9999994039535522, 0.9989026784896851, 0.9995453953742981, 0.9926201701164246, 0.8996896147727966, 0.8966121077537537]"
-1180,GC+57.021DYLEESASNK,0.9593561788400015,"[1.0, 0.9756583571434021, 0.9975911378860474, 0.9999575614929199, 0.9999843835830688, 0.9991891980171204, 0.9998946189880371, 0.9999904632568359, 0.663282036781311, 0.9969471096992493, 0.9305402040481567, 0.9492390751838684]"
-1181,FM+15.995TDPLR,0.9579945547240121,"[0.999997615814209, 0.7157432436943054, 0.9999245405197144, 0.9974290728569031, 0.9999794960021973, 0.9937325119972229, 0.9991554021835327]"
-1182,VGAHAGDYQEALER,0.9278083997113364,"[0.9999756813049316, 0.9274553060531616, 0.9799878597259521, 0.9994382262229919, 0.9998937845230103, 0.6466551423072815, 0.6251962780952454, 0.9999845027923584, 0.9407256841659546, 0.9810571670532227, 0.999129593372345, 0.8946813344955444, 0.9959496259689331, 0.9991874098777771]"
-1183,VVAEVYDQER,0.998231166601181,"[0.9999969005584717, 1.0, 1.0, 0.9999865293502808, 0.9999982118606567, 0.9999996423721313, 0.9992183446884155, 0.9967382550239563, 0.9925199747085571, 0.9938538074493408]"
-1184,NNTQVLLNC+57.021R,0.9992863059043884,"[0.9989023208618164, 0.9996699094772339, 0.9999992847442627, 0.9964010715484619, 0.9985270500183105, 0.9999784231185913, 0.9999992847442627, 0.999595582485199, 0.9999948740005493, 0.9997952580451965]"
-1185,ESETAEEQALEEQNAK,0.9233167208731174,"[0.817437469959259, 0.9782472252845764, 0.6744212508201599, 0.9999053478240967, 0.9999984502792358, 0.9995063543319702, 0.9998834133148193, 0.997840404510498, 0.9999971389770508, 0.9999998807907104, 0.9999895095825195, 0.9998785257339478, 0.9972270131111145, 0.7839040756225586, 0.7020808458328247, 0.8227506279945374]"
-1186,ELFAADGQAPAVPETLGGGR,0.8119550004601479,"[0.9318035244941711, 0.8989888429641724, 0.7780268788337708, 0.983272910118103, 0.9788572788238525, 0.9827985167503357, 0.9637137651443481, 0.8202041387557983, 0.9914920926094055, 0.9999988079071045, 0.4831591844558716, 0.6614484190940857, 1.0, 0.9046334624290466, 0.8706377744674683, 0.6038627624511719, 0.8897488117218018, 0.47114136815071106, 0.3066338300704956, 0.7186776399612427]"
-1187,GQVQEVQASEETQELGNASK,0.787889039516449,"[0.9964324235916138, 0.9146777987480164, 0.9518678784370422, 0.5653660893440247, 0.2842795252799988, 0.5613842606544495, 0.607888400554657, 0.63588547706604, 0.951453447341919, 0.7102414965629578, 0.6106044054031372, 0.6792417764663696, 0.5888330936431885, 0.7475726008415222, 0.9999985694885254, 0.9999924898147583, 0.9660219550132751, 0.9999511241912842, 0.9978888630867004, 0.9881991147994995]"
-1188,NAVGGALYEDHGASSSLGNASK,0.8720430867238478,"[0.9929425716400146, 0.9884763956069946, 0.4903717339038849, 0.9821879267692566, 0.9785985946655273, 0.5364696979522705, 0.45246490836143494, 0.7942374348640442, 0.9715847969055176, 0.9839779734611511, 0.8092318177223206, 0.7517703175544739, 0.9614827036857605, 0.9990749359130859, 0.9999984502792358, 0.9933905601501465, 0.9961613416671753, 0.9999246597290039, 0.7772479057312012, 0.9829390048980713, 0.9556459784507751, 0.7867681980133057]"
-1189,EKLQEEMLQR,0.9858439028263092,"[0.9965129494667053, 0.9581977128982544, 1.0, 0.9354369044303894, 0.9931453466415405, 0.9999195337295532, 0.9998589754104614, 0.9978466033935547, 0.9879608750343323, 0.9895601272583008]"
-1190,DTELDRLEC+57.021DR,0.38618413833054627,"[0.237206369638443, 0.5833268165588379, 0.5707196593284607, 0.3672467768192291, 0.43021512031555176, 0.2047944813966751, 0.3590483069419861, 0.29463517665863037, 0.26967453956604004, 0.4251258075237274, 0.5060324668884277]"
-1191,AQDASLNVPER,0.9879723949865862,"[1.0, 0.9890882968902588, 0.9994639754295349, 0.9999998807907104, 0.9999974966049194, 1.0, 0.9879677295684814, 0.982508659362793, 0.9987459182739258, 0.9237009882926941, 0.9862233996391296]"
-1192,AQDASLTPPSGR,0.913753867149353,"[0.9999985694885254, 0.9811854958534241, 0.988591730594635, 0.9995008707046509, 0.9627721905708313, 0.9987649917602539, 0.9953179359436035, 0.5546420812606812, 0.9513965249061584, 0.994478702545166, 0.6091492772102356, 0.929248034954071]"
-1193,EAGDVC+57.021YADVQK,0.9909839083751043,"[0.999974250793457, 0.9999954700469971, 0.9997418522834778, 0.97691810131073, 0.999980092048645, 0.999934196472168, 0.9999969005584717, 0.9999626874923706, 0.9999024868011475, 0.9992716908454895, 0.9319666028022766, 0.9841625690460205]"
-1194,C+57.021LLFM+15.995GLVYEAVTK,0.6108717301062175,"[0.9957589507102966, 0.5834611654281616, 0.4032432734966278, 0.28278648853302, 0.24114474654197693, 0.3179209232330322, 0.4217277765274048, 0.9043091535568237, 0.9996339082717896, 0.7552178502082825, 0.3733746111392975, 0.5487205982208252, 0.9901930093765259, 0.734711766242981]"
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-1196,N+0.984GDPLN+0.984GAC+57.021NDGSNSK,0.8809384051710367,"[0.9342280030250549, 0.9867527484893799, 0.9999117851257324, 0.9999574422836304, 0.9999606609344482, 0.9931501746177673, 0.8460913300514221, 0.9642589092254639, 0.8809049129486084, 0.9720292091369629, 0.9937968254089355, 0.9999998807907104, 0.792578935623169, 0.47300103306770325, 0.6092095971107483, 0.6491830348968506]"
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-1388,HEALSGLGTASVEK,0.8116796123129981,"[0.9999995231628418, 0.999996542930603, 0.7769594788551331, 0.5715867877006531, 0.7009115815162659, 0.5359880924224854, 0.5475727915763855, 0.9480900764465332, 0.9209215641021729, 0.46730729937553406, 0.998964786529541, 0.9178049564361572, 0.9781538248062134, 0.9992572665214539]"
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-1392,EATTALESLQAQAQGDAVK,0.7161934383605656,"[0.5237842798233032, 0.42948833107948303, 0.23174504935741425, 0.9061646461486816, 0.9191951751708984, 0.6202788949012756, 0.7596520185470581, 0.4806957542896271, 0.46492961049079895, 0.9866395592689514, 0.9981422424316406, 0.9978275895118713, 0.9999995231628418, 0.988520085811615, 0.5522972941398621, 0.6803622245788574, 0.4162437915802002, 0.7012133002281189, 0.9504959583282471]"
-1393,YSPTSPTYSPTSPK,0.8780368098190853,"[0.9999127388000488, 0.9989664554595947, 0.9999998807907104, 0.9999920129776001, 0.9999885559082031, 1.0, 0.999839186668396, 0.46030810475349426, 0.9995827078819275, 0.9996623992919922, 0.961106538772583, 0.38410279154777527, 0.8622173070907593, 0.6268366575241089]"
-1394,DVSMASLC+57.021TTAEALK,0.782879098256429,"[0.9943040013313293, 0.9958578944206238, 0.38148924708366394, 0.7635872960090637, 0.9999725818634033, 0.9999997615814209, 0.9995265007019043, 0.979588508605957, 0.9911121726036072, 0.7604387998580933, 0.6996957063674927, 0.508563220500946, 0.3506547808647156, 0.387885183095932, 0.9305108189582825]"
-1395,YLSGLQTLGHR,0.9895001487298445,"[0.9999986886978149, 0.9996321201324463, 1.0, 1.0, 1.0, 0.9968975782394409, 0.999991774559021, 0.9999973773956299, 0.9991913437843323, 0.8916862607002258, 0.9971064925193787]"
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-1398,SLSRESDPNEGLNSPMDPK,0.770609913687957,"[0.9998247027397156, 0.6935916543006897, 0.6029716730117798, 0.33214089274406433, 0.3510197103023529, 0.25893720984458923, 0.7525849342346191, 0.7903884053230286, 0.502142608165741, 0.9781387448310852, 0.9999998807907104, 0.8957902193069458, 0.9761927723884583, 0.9793025851249695, 0.9987395405769348, 0.8779720067977905, 0.8454984426498413, 0.8602868318557739, 0.9460655450820923]"
-1399,MVTNSDDELVGVNEPADTR,0.6795112380855962,"[0.27501940727233887, 0.5100587606430054, 0.511070966720581, 0.6464568972587585, 0.7385053634643555, 0.4076637625694275, 0.507007896900177, 0.7339861989021301, 0.528914213180542, 0.9999196529388428, 0.9999587535858154, 0.8833856582641602, 0.41156265139579773, 0.6747260093688965, 0.9999996423721313, 0.5586637854576111, 0.5482137203216553, 0.9901962280273438, 0.9854039549827576]"
-1400,QLQEGLDVDYR,0.679482489824295,"[0.695148229598999, 0.5313815474510193, 0.3374171555042267, 0.9148651361465454, 0.31115666031837463, 0.3947533369064331, 0.48748621344566345, 0.9935105443000793, 0.8270918726921082, 0.9854110479354858, 0.9960856437683105]"
-1401,AGC+57.021C+57.021DC+57.021C+57.021GLGLR,0.9035220046838125,"[0.9997027516365051, 0.7384724020957947, 0.8734865188598633, 0.9999806880950928, 0.9998990297317505, 0.9999998807907104, 0.9999958276748657, 0.9996829032897949, 0.9950782060623169, 0.9090651869773865, 0.5864396691322327, 0.740460991859436]"
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-1403,LVC+57.021N+0.984GC+57.021NGPVVGTR,0.9597468503883907,"[0.9999963045120239, 0.5051761865615845, 0.9995501637458801, 0.9809442162513733, 0.9999009370803833, 0.9999867677688599, 0.9800295233726501, 1.0, 0.9992775321006775, 0.9948438405990601, 0.999775230884552, 0.9999411106109619, 0.9786959886550903, 0.9983381032943726]"
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-1443,PLPPNNEEVDEDPTGNK,0.9507387035033282,"[0.9999681711196899, 0.6319140791893005, 0.9998924732208252, 0.983628511428833, 0.6172163486480713, 0.9727687239646912, 0.9966390132904053, 0.9998214840888977, 0.999987006187439, 0.9999966621398926, 0.9999899864196777, 0.9999854564666748, 0.9999991655349731, 0.9996192455291748, 0.9910494089126587, 0.9708388447761536, 0.9992433786392212]"
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-1445,AGTATLQTLGEAQSK,0.9717475454012553,"[0.9999990463256836, 0.9461854696273804, 0.9547147750854492, 0.6802006959915161, 0.9996939897537231, 1.0, 0.9975792765617371, 1.0, 1.0, 1.0, 0.9999915361404419, 0.9999971389770508, 0.9989169836044312, 0.9991815686225891, 0.9997527003288269]"
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-1506,VEFNYC+57.021C+57.021LQGSNK,0.7355242864443705,"[0.9664739370346069, 0.5909343957901001, 0.9999839067459106, 0.9220483899116516, 0.9778087139129639, 0.4858459234237671, 0.21273501217365265, 0.32681265473365784, 0.6903806328773499, 0.9764354825019836, 0.6066929697990417, 0.8091292381286621, 0.9965344667434692]"
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-1509,LALQGPEDSSPR,0.948430115977923,"[1.0, 0.9999207258224487, 1.0, 0.9969980716705322, 0.9999998807907104, 0.9999996423721313, 0.6802051067352295, 0.7690417170524597, 0.9980106949806213, 0.9820099472999573, 0.967166543006897, 0.9878090620040894]"
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-1511,QSHVSTLK,0.435921935364604,"[0.39883413910865784, 0.34493279457092285, 0.5278465747833252, 0.26865312457084656, 0.6588436365127563, 0.21275097131729126, 0.2266126126050949, 0.848901629447937]"
-1512,VGAALSELR,0.8785770999060737,"[0.9999979734420776, 0.9922270774841309, 0.9991886019706726, 0.9982908368110657, 0.9978911280632019, 0.3942323327064514, 0.5832483768463135, 0.9560742378234863, 0.9860433340072632]"
-1513,GGSSGGGYGSGGGSSSY,0.9025196275290321,"[0.9986289739608765, 0.99661785364151, 0.9999798536300659, 0.7238042950630188, 0.9168162941932678, 0.9940934777259827, 0.9999901056289673, 0.9998719692230225, 0.8525175452232361, 0.5916134715080261, 0.9310102462768555, 0.9876077771186829, 0.9920912981033325, 0.4382966458797455, 0.9722647666931152, 0.9992460012435913, 0.948383092880249]"
-1514,SPEVLSGGEDVQR,0.9031679974152491,"[1.0, 0.9999927282333374, 0.9998689889907837, 0.9999146461486816, 0.9999998807907104, 0.9999997615814209, 1.0, 0.9902117848396301, 0.7079124450683594, 0.9894796013832092, 0.8788604736328125, 0.3871915638446808, 0.787752091884613]"
-1515,NVAAAVAATTAAPAAGAAPAK,0.8907596071561178,"[0.9479842185974121, 0.9637532830238342, 0.9666231274604797, 0.999975323677063, 0.9834611415863037, 0.5075002908706665, 0.9999867677688599, 0.999797523021698, 0.9999630451202393, 0.9955320358276367, 1.0, 1.0, 0.9999063014984131, 0.9999638795852661, 0.587918221950531, 0.9728730916976929, 0.8124327063560486, 0.835641086101532, 0.9667903780937195, 0.6340477466583252, 0.5318015813827515]"
-1516,VLSTGGVENVEAR,0.9039158110435193,"[0.9999990463256836, 0.981532096862793, 0.9999997615814209, 0.9969747066497803, 0.9982860684394836, 0.9926914572715759, 0.999998927116394, 0.9132691025733948, 0.38583019375801086, 0.7066869139671326, 0.8399338722229004, 0.9391851425170898, 0.9965182542800903]"
-1517,AAPGEASQGDLTLFR,0.7931885729233424,"[0.9065573215484619, 0.9625702500343323, 0.9999990463256836, 0.9999213218688965, 0.9970294237136841, 0.9998977184295654, 0.8524319529533386, 0.6404258012771606, 0.9988640546798706, 0.9424095749855042, 0.7648024559020996, 0.15865837037563324, 0.2945907711982727, 0.44196179509162903, 0.9377087354660034]"
-1518,TEEGPTLSYGR,0.9994816509160128,"[1.0, 0.9999984502792358, 0.9999066591262817, 0.9999988079071045, 0.9999994039535522, 0.9999866485595703, 0.9999998807907104, 0.9999990463256836, 0.9999963045120239, 0.9999721050262451, 0.9944408535957336]"
-1519,GFNC+57.021ESKPEPEETC+57.021FDK,0.9884447735898635,"[1.0, 0.9999979734420776, 0.9945996999740601, 1.0, 0.9975022673606873, 1.0, 0.9975687861442566, 0.9999995231628418, 0.9999829530715942, 0.9999988079071045, 0.8979305028915405, 0.9996346235275269, 0.9996873140335083, 0.9998929500579834, 0.999068558216095, 0.9532598853111267, 0.9644373059272766]"
-1520,TELNTAADSDQPLDK,0.9961488604545593,"[1.0, 1.0, 1.0, 0.9994211196899414, 1.0, 0.9999997615814209, 1.0, 0.9627580642700195, 0.9998769760131836, 0.9999969005584717, 0.9816465973854065, 0.9999982118606567, 0.9998955726623535, 0.9997054934501648, 0.9989342093467712]"
-1521,ATEEPGSEEVESAVK,0.8230710605780284,"[0.9919811487197876, 0.9713005423545837, 0.9999841451644897, 0.9570950865745544, 0.9896838665008545, 0.4368959367275238, 0.5875948071479797, 0.7101673483848572, 0.9937613606452942, 0.6821247935295105, 0.6605740189552307, 0.9998968839645386, 0.7067381143569946, 0.7615824937820435, 0.8966853618621826]"
-1522,GETASTTSEQTEVAAR,0.9135902803391218,"[0.9994227886199951, 0.986836314201355, 0.4203775227069855, 0.9235703945159912, 0.3991124629974365, 0.9911784529685974, 0.9999995231628418, 0.999998927116394, 0.9999476671218872, 0.9964577555656433, 0.9996557235717773, 0.9269835948944092, 0.9994698166847229, 0.9997312426567078, 0.9985249638557434, 0.9761773347854614]"
-1523,GFNC+57.021ESKPEPEETC+57.021FDK,0.9650686558555154,"[1.0, 1.0, 0.9853391647338867, 0.999998927116394, 0.8112900853157043, 1.0, 0.6271352171897888, 0.9999704360961914, 0.9919478297233582, 0.9999983310699463, 0.9999556541442871, 0.9999798536300659, 0.9981356859207153, 0.9999884366989136, 0.9999716281890869, 0.9989928603172302, 0.9934630393981934]"
-1524,LQEGLEAGTPAPGEK,0.970099937915802,"[1.0, 0.9861018657684326, 0.9999494552612305, 0.9999949932098389, 0.6427894234657288, 0.9999986886978149, 1.0, 1.0, 0.9440205097198486, 1.0, 0.9999651908874512, 1.0, 0.9937183856964111, 0.9982509016990662, 0.9867096543312073]"
-1525,EDQTEYLEER,0.9810486972332001,"[0.9978047013282776, 0.9999998807907104, 0.9765253067016602, 0.9999970197677612, 0.9988172054290771, 0.9999862909317017, 0.9668852090835571, 0.9922807812690735, 0.9618062973022461, 0.9163842797279358]"
-1526,MVDC+57.021TC+57.021LGEGDGR,0.9727395589535053,"[0.9999878406524658, 0.9999529123306274, 0.9995699524879456, 0.9999924898147583, 0.9998880624771118, 1.0, 0.9999986886978149, 0.9999998807907104, 0.6908138394355774, 0.9999933242797852, 0.9580857753753662, 0.9994651675224304, 0.9978663325309753]"
-1527,LNEELVQK,0.6001621745526791,"[0.7049771547317505, 0.5963471531867981, 0.32122278213500977, 0.5221450328826904, 0.691019594669342, 0.5962602496147156, 0.4095572531223297, 0.9597681760787964]"
-1528,TEQTPGLHFGGN,0.7254985297719637,"[0.9970857501029968, 0.8169723749160767, 0.9564222097396851, 0.599881649017334, 0.9982241988182068, 0.706802248954773, 0.6883379220962524, 0.7356808185577393, 0.4327947497367859, 0.6920289397239685, 0.23573115468025208, 0.8460203409194946]"
-1529,EDVASAVVVAGQK,0.6152400431724695,"[0.7153189182281494, 0.5095097422599792, 0.9967696666717529, 0.19355712831020355, 0.3908730447292328, 0.965272843837738, 0.7099688649177551, 0.3835274875164032, 0.9473632574081421, 0.9920444488525391, 0.42991262674331665, 0.22455647587776184, 0.5394460558891296]"
-1530,DYDESVEQDGDEPGPQR,0.9519649989464704,"[0.9902296662330627, 0.9052605032920837, 0.9805073142051697, 0.6986084580421448, 0.982842743396759, 0.9999969005584717, 0.9998610019683838, 0.9862115383148193, 0.9996657371520996, 1.0, 0.9999699592590332, 0.999998927116394, 1.0, 0.9999507665634155, 0.8873645663261414, 0.7551109790802002, 0.9978259205818176]"
-1531,KLGVDEEDEELLTESSK,0.8150016981012681,"[0.7005674242973328, 0.3259226381778717, 0.844701886177063, 0.48372283577919006, 0.37034091353416443, 0.9715793132781982, 0.9998332262039185, 0.27474913001060486, 0.933861494064331, 0.9982572197914124, 0.9999996423721313, 0.9999977350234985, 0.9980630278587341, 0.9997740387916565, 0.9805574417114258, 0.994959831237793, 0.9781410694122314]"
-1532,VLEEVC+57.021ASSGQPAAR,0.9953460256258647,"[1.0, 0.9863840937614441, 0.9999996423721313, 0.9999996423721313, 1.0, 1.0, 0.9999808073043823, 0.9999858140945435, 0.9999878406524658, 0.9825085401535034, 0.9841392636299133, 0.9998711347579956, 0.9931158423423767, 0.9935756325721741, 0.9906421303749084]"
-1533,RLEFEDLQNLLTESSK,0.7677331110462546,"[0.8411422371864319, 0.5081459283828735, 0.9822425842285156, 0.2245570570230484, 0.46692731976509094, 0.9837046265602112, 0.7408004999160767, 0.6108518838882446, 0.4852655529975891, 0.9994550347328186, 0.9572095274925232, 0.5122600197792053, 0.9987512826919556, 0.9753633737564087, 0.9989219903945923, 0.9981308579444885]"
-1534,PGFSLADK,0.9850402176380157,"[0.9997758269309998, 0.9556489586830139, 0.9999998807907104, 0.9990314245223999, 0.9983088970184326, 0.9798339009284973, 0.9498717188835144, 0.9978511333465576]"
-1535,AGEANQVGYLYPLEK,0.8504773318767548,"[0.9901583790779114, 0.750167965888977, 0.9695876836776733, 0.9997566342353821, 0.9285260438919067, 0.7856472134590149, 0.8561401963233948, 0.8815730810165405, 0.9066663980484009, 0.33903035521507263, 0.5171905755996704, 0.9999966621398926, 0.9997476935386658, 0.8344629406929016, 0.9985081553459167]"
-1536,LTAQFVAR,0.9202671349048615,"[0.9999926090240479, 0.999996542930603, 0.9999924898147583, 0.9980437755584717, 0.9999876022338867, 0.7908562421798706, 0.6150615811347961, 0.9582062363624573]"
-1537,ASASDGSSFVVAR,0.9964868930669931,"[0.9999998807907104, 0.9999998807907104, 0.9999996423721313, 0.9999984502792358, 0.9999990463256836, 1.0, 0.999997615814209, 0.9999998807907104, 0.9999998807907104, 0.9794867038726807, 0.9912132620811462, 0.9843508005142212, 0.999284565448761]"
-1538,YDNGLYVQDGDEPGPGAR,0.8651462462213304,"[0.9988492727279663, 0.9344534277915955, 0.9673418998718262, 0.3606109023094177, 0.3385222852230072, 0.8768911361694336, 0.7361325621604919, 0.977311909198761, 0.9999616146087646, 0.9999933242797852, 0.9345169067382812, 1.0, 0.9999988079071045, 0.9997476935386658, 0.9999854564666748, 0.472243994474411, 0.9789602756500244, 0.9971109628677368]"
-1539,DNLQGLTKPALR,0.9986841181914011,"[0.9999985694885254, 0.9977951049804688, 1.0, 0.9986428618431091, 1.0, 0.9999998807907104, 1.0, 1.0, 0.9991121888160706, 0.9999309778213501, 0.992210865020752, 0.9965189695358276]"
-1540,EGNGTVMGAELR,0.9987551818291346,"[1.0, 0.9999997615814209, 0.9994025230407715, 1.0, 0.9999998807907104, 0.9998701810836792, 0.9999969005584717, 1.0, 0.9999994039535522, 0.9871569275856018, 0.999566376209259, 0.9990702271461487]"
-1541,LPVQLQR,0.992415019444057,"[0.9997705817222595, 0.9994256496429443, 0.997281551361084, 0.9853455424308777, 0.99565190076828, 0.9714463353157043, 0.9979835748672485]"
-1542,LGLGENTYYK,0.6808263152837754,"[0.9016481637954712, 0.6679732203483582, 0.39135652780532837, 0.8831358551979065, 0.533405065536499, 0.9737445712089539, 0.49952027201652527, 0.3333086371421814, 0.6961500644683838, 0.9280207753181458]"
-1543,SGDGLDRVQSDMK,0.7609794804683099,"[0.9898507595062256, 0.3557121753692627, 0.331277996301651, 0.2898319363594055, 0.23043692111968994, 0.9896845817565918, 0.7482179403305054, 0.9999983310699463, 0.9912540316581726, 0.9937053322792053, 0.9982450008392334, 0.978226363658905, 0.9962918758392334]"
-1544,C+57.021TLLASGQLDDAR,0.8831467215831463,"[0.9999982118606567, 0.8923360109329224, 0.9999401569366455, 0.9999978542327881, 0.9990110397338867, 0.8951873183250427, 0.8283446431159973, 0.5149434208869934, 0.9848716855049133, 0.7519737482070923, 0.9619176983833313, 0.9913289546966553, 0.661056637763977]"
-1545,EHELAGGFLDC+57.021TC+57.021LGEGDGR,0.8297456324100494,"[0.5817086696624756, 0.36229223012924194, 0.965736448764801, 0.4183964729309082, 0.6353516578674316, 0.6623316407203674, 0.9989701509475708, 0.5378141403198242, 0.9904844760894775, 0.9999760389328003, 0.9998564720153809, 0.9517713189125061, 0.6516298055648804, 0.9999746084213257, 0.9999961853027344, 0.9998446702957153, 0.9999608993530273, 0.9693298935890198, 0.8810741901397705, 0.9884126782417297]"
-1546,QFGDSNANFPSFVM+15.995,0.8254701920918056,"[0.5729438662528992, 0.4975779056549072, 0.9839721918106079, 0.9269314408302307, 0.9995046854019165, 0.9989699125289917, 0.5518292784690857, 0.9788973331451416, 0.8054458498954773, 0.9985492825508118, 0.5605478882789612, 0.8008756637573242, 0.9997066855430603, 0.880830705165863]"
-1547,LLSGEDVGQDETQR,0.9607181165899549,"[1.0, 0.9999996423721313, 1.0, 0.9999997615814209, 0.9988275170326233, 0.9999316930770874, 0.9999998807907104, 1.0, 0.988778829574585, 0.9987291693687439, 0.9999383687973022, 0.9987853169441223, 0.4718163013458252, 0.9932471513748169]"
-1548,LQGLTKPALR,0.9185014545917511,"[1.0, 0.996780276298523, 1.0, 0.9999995231628418, 1.0, 0.9999862909317017, 0.8771810531616211, 0.853627622127533, 0.46206754446029663, 0.9953722357749939]"
-1549,GFC+57.021DWDNLQGLTGKPALR,0.8931517336103652,"[0.9994321465492249, 0.36073753237724304, 0.7355714440345764, 0.8771941661834717, 0.9664955139160156, 0.7463544607162476, 0.9815803170204163, 0.9999974966049194, 0.993125319480896, 0.9996931552886963, 0.9906745553016663, 0.9993602633476257, 0.9996485710144043, 0.9999912977218628, 0.9997804760932922, 0.9991207718849182, 0.9419116973876953, 0.48606202006340027]"
-1550,ALYQLAQK,0.955528125166893,"[0.9997144341468811, 0.7949691414833069, 0.999997615814209, 0.9948586225509644, 0.999795138835907, 0.9967671632766724, 0.9797418117523193, 0.878381073474884]"
-1551,KSLDETVFSK,0.7360019788146019,"[0.9999748468399048, 0.9997146725654602, 0.9895216226577759, 0.34986868500709534, 0.47334447503089905, 0.1992521435022354, 0.4477016031742096, 0.9957664012908936, 0.9973164200782776, 0.9075589179992676]"
-1552,QGLTKPALR,0.9932747814390395,"[0.9884507656097412, 1.0, 0.9999997615814209, 1.0, 0.9999988079071045, 0.999925971031189, 0.9998589754104614, 0.9535422921180725, 0.9976964592933655]"
-1553,EAQQYSEALASTR,0.9997674639408405,"[0.999675989151001, 1.0, 0.9994219541549683, 0.9990071654319763, 1.0, 1.0, 0.999996542930603, 0.9999971389770508, 1.0, 0.9999998807907104, 0.9999710321426392, 0.9999890327453613, 0.9989182949066162]"
-1554,YDEAASYLQSK,0.9969451590017839,"[1.0, 0.9935047626495361, 0.9999942779541016, 0.9999966621398926, 1.0, 0.9999696016311646, 0.9845135807991028, 0.9999994039535522, 0.9996121525764465, 0.9997394680976868, 0.9890668392181396]"
-1555,GQLEEQGELMEK,0.9996380805969238,"[0.9999998807907104, 0.9974210262298584, 1.0, 0.9999996423721313, 0.9999945163726807, 0.9993956089019775, 0.9999908208847046, 0.9999289512634277, 1.0, 0.999991774559021, 0.9996445178985596, 0.9992902278900146]"
-1556,KLYDLSLSK,0.8294188115331862,"[0.999941349029541, 0.9998610019683838, 0.992856502532959, 0.5699755549430847, 0.504353404045105, 0.7644098997116089, 0.9937323927879333, 0.6429368257522583, 0.9967023730278015]"
-1557,VSTDLLR,0.9504661560058594,"[0.9998169541358948, 0.9926735758781433, 0.9940733313560486, 0.9983612895011902, 0.812989354133606, 0.8564141392707825, 0.9989344477653503]"
-1558,ASASDGSSFVVAR,0.9971648683914771,"[1.0, 0.9999997615814209, 0.9999998807907104, 1.0, 1.0, 1.0, 0.9999974966049194, 0.9999998807907104, 0.9999998807907104, 0.9891713261604309, 0.9799542427062988, 0.9984216690063477, 0.9955991506576538]"
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-1614,VGSSEFYVVDKDKDNGH,0.7608148262781256,"[0.9740962386131287, 0.93752521276474, 0.42987653613090515, 0.6418497562408447, 0.7867853045463562, 0.5348426699638367, 0.47032633423805237, 0.860529363155365, 0.43900492787361145, 0.9998538494110107, 0.5321025252342224, 0.8665685653686523, 0.7027354836463928, 0.9417912364006042, 0.819149911403656, 0.9999899864196777, 0.9968241453170776]"
-1615,TYDPSGDSTLPTSC+57.021K,0.9802631258964538,"[1.0, 1.0, 0.9999997615814209, 0.9999998807907104, 0.9999961853027344, 0.9999850988388062, 0.7113937735557556, 0.9999909400939941, 0.9999754428863525, 0.9999998807907104, 1.0, 0.999983549118042, 0.9958258867263794, 0.9975818395614624, 0.9992146492004395]"
-1616,STAEQEAEELEK,0.9909113844235738,"[1.0, 0.9697014689445496, 0.9999988079071045, 0.9992642998695374, 0.9981049299240112, 0.9994271993637085, 0.9997437596321106, 0.9698725342750549, 0.9991092085838318, 0.999695897102356, 0.9921184778213501, 0.9639000296592712]"
-1617,DMNHTNSYGLLR,0.8982139130433401,"[0.9991218447685242, 0.9875232577323914, 0.7611263990402222, 0.9999997615814209, 0.9999173879623413, 0.9833179712295532, 0.9928097724914551, 0.4083660840988159, 0.9996981620788574, 0.7970845699310303, 0.8549867272377014, 0.9946150183677673]"
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-1619,EDQSLLC+57.021TGESGAGK,0.9970642765363057,"[0.9999891519546509, 0.999996542930603, 0.9904228448867798, 1.0, 0.9999997615814209, 0.9999996423721313, 0.9999935626983643, 1.0, 1.0, 0.9993680119514465, 0.9998934268951416, 0.999996542930603, 0.9999991655349731, 0.9992319345474243, 0.9670735597610474]"
-1620,MNLEGGNQEESQK,0.9213948685389298,"[0.9999958276748657, 0.994479775428772, 1.0, 0.9999765157699585, 0.9340887069702148, 0.9998846054077148, 0.7956855297088623, 0.9949312806129456, 0.9828185439109802, 0.9994401335716248, 0.9996519088745117, 0.2835330665111542, 0.9936473965644836]"
-1621,ALELPLLLETAEELQER,0.7202424775151646,"[0.5985193848609924, 0.5980646014213562, 0.49705615639686584, 0.5842039585113525, 0.547874927520752, 0.24598154425621033, 0.48574498295783997, 0.46797722578048706, 0.9997996687889099, 0.9999901056289673, 0.9999760389328003, 0.9997418522834778, 0.8300497531890869, 0.9993688464164734, 0.893363356590271, 0.8761048316955566, 0.6203048825263977]"
-1622,GAAQNLLLESALR,0.7347764166501852,"[0.9969621300697327, 0.8828681707382202, 0.9104781150817871, 0.5777100920677185, 0.4971013367176056, 0.7199800610542297, 0.7457541823387146, 0.9048763513565063, 0.9362006783485413, 0.7536987662315369, 0.5027487874031067, 0.30188024044036865, 0.8218345046043396]"
-1623,GFGFGGVSKPDPEETC+57.021FDK,0.9251404279156735,"[0.9997785687446594, 0.9999971389770508, 0.9996727705001831, 0.9712516069412231, 0.9997240900993347, 0.7572886943817139, 0.5899483561515808, 0.9998070597648621, 0.9534164667129517, 0.9999268054962158, 0.913136899471283, 0.9999827146530151, 0.9999884366989136, 0.9999927282333374, 0.9972885847091675, 0.9992771744728088, 0.9586675763130188, 0.8757619857788086, 0.5627604722976685]"
-1624,FDPYEEDSRPR,0.84646460684863,"[0.9999991655349731, 0.5164127349853516, 0.9998317956924438, 0.8739255666732788, 0.9890956282615662, 0.9752888679504395, 0.9999183416366577, 0.9999586343765259, 0.5935817360877991, 0.6870262622833252, 0.6760719418525696]"
-1625,LVAEGNLYSDK,0.9109028361060403,"[0.9999991655349731, 0.9889040589332581, 0.9999923706054688, 0.9999850988388062, 0.9999996423721313, 0.9367589354515076, 0.3739050030708313, 0.9962177872657776, 0.9994662404060364, 0.7592315077781677, 0.9654713869094849]"
-1626,TEALGLEALADELLNAPK,0.7674401236904992,"[0.9999986886978149, 0.9995855689048767, 0.999538779258728, 0.599509596824646, 0.3394039273262024, 0.8682763576507568, 0.903289258480072, 0.6534369587898254, 0.9886457920074463, 0.5885934233665466, 0.922899067401886, 0.8295660018920898, 0.24596595764160156, 0.7858829498291016, 0.5351582765579224, 0.5597361326217651, 0.9952980875968933, 0.9991374015808105]"
-1627,TEEATEGMEEAVSSK,0.9501855115095774,"[0.9999997615814209, 0.9999960660934448, 0.9999998807907104, 0.9999513626098633, 0.9985886216163635, 0.9999902248382568, 0.9999707937240601, 0.8081696629524231, 0.4632892906665802, 0.9999759197235107, 0.9999040365219116, 0.9979895353317261, 0.9925750494003296, 0.9990230798721313, 0.993359386920929]"
-1628,NYVPEDEDLK,0.9914774417877197,"[0.9962886571884155, 0.9999892711639404, 0.9999911785125732, 1.0, 0.9999990463256836, 0.9995336532592773, 0.9995380640029907, 0.9901878237724304, 0.9823670387268066, 0.9468796849250793]"
-1629,YAYLGLVDASTSSAYEQDQK,0.6895609468221664,"[0.5208812952041626, 0.5469115972518921, 0.653485119342804, 0.9991449117660522, 0.7779905200004578, 0.4263024926185608, 0.4619755744934082, 0.8879240155220032, 0.9763268232345581, 0.7477778792381287, 0.7681114673614502, 0.36605510115623474, 0.2949850559234619, 0.35147175192832947, 0.28269141912460327, 0.9992133378982544, 0.9158663749694824, 0.8438987135887146, 0.9971652626991272, 0.9730402231216431]"
-1630,NLC+57.021SDDWLER,0.8312337756156921,"[0.9532307386398315, 0.8623248338699341, 0.9999804496765137, 0.999931812286377, 0.9922183752059937, 0.6774871349334717, 0.5326259136199951, 0.9780608415603638, 0.5860504508018494, 0.7304272055625916]"
-1631,QALLQHEYLEQETK,0.7825563549995422,"[0.8396211862564087, 0.7075293064117432, 0.960357129573822, 0.9726386666297913, 0.8939080834388733, 0.8136592507362366, 0.99958735704422, 0.40733712911605835, 0.3380553722381592, 0.5077322125434875, 0.9012054800987244, 0.7849559783935547, 0.8453255295753479, 0.9838762879371643]"
-1632,VVVDSEYR,0.8575006350874901,"[0.9999680519104004, 0.9995469450950623, 0.6794067621231079, 0.5513285994529724, 0.9998986721038818, 0.9805154800415039, 0.9553875923156738, 0.6939529776573181]"
-1633,LVGLQYK,0.9551688347543988,"[0.9999972581863403, 0.9959573149681091, 0.9999995231628418, 0.990444004535675, 0.9984695315361023, 0.9764934778213501, 0.7248207330703735]"
-1634,DNLQGLTKPALR,0.9989192684491476,"[0.9999994039535522, 0.9949734210968018, 1.0, 0.9981539845466614, 0.9999940395355225, 0.9999997615814209, 1.0, 1.0, 0.9982372522354126, 0.9998998641967773, 0.997094988822937, 0.9986785054206848]"
-1635,EDLEMYNLK,0.6517589622073703,"[0.34149637818336487, 0.8791595101356506, 0.6439070105552673, 0.8003221750259399, 0.3159988224506378, 0.5601176619529724, 0.5554329752922058, 0.7869666218757629, 0.9824295043945312]"
-1636,QAVDVSPLR,0.960841629240248,"[0.989927351474762, 0.9999995231628418, 0.9999940395355225, 0.9999431371688843, 0.999976396560669, 0.9880802035331726, 0.7038870453834534, 0.9694018959999084, 0.9963650703430176]"
-1637,EQLLLSGDGDSR,0.8058986390630404,"[0.9997683167457581, 0.5391858220100403, 0.9993894100189209, 0.9990653395652771, 0.9997320771217346, 0.9194676876068115, 0.9984965324401855, 0.4253157079219818, 0.5311257839202881, 0.7026095390319824, 0.9462022185325623, 0.6104252338409424]"
-1638,QVTDVQDNSLSVR,0.9997745293837327,"[0.9983792304992676, 0.9999997615814209, 1.0, 0.9998596906661987, 1.0, 0.9994977712631226, 0.9999349117279053, 0.9997566342353821, 0.9997701048851013, 0.999996542930603, 0.9999984502792358, 0.9999675750732422, 0.9999082088470459]"
-1639,TSWLHR,0.9408904214700063,"[0.9887700080871582, 0.9999594688415527, 0.7814655303955078, 0.9988414645195007, 0.992919385433197, 0.8833866715431213]"
-1640,FADLSEAANR,0.9580122649669647,"[1.0, 0.9999055862426758, 0.9999951124191284, 0.9999997615814209, 0.9999969005584717, 0.6579516530036926, 0.9994692206382751, 0.9989035129547119, 0.9281965494155884, 0.9957043528556824]"
-1641,SNEDQSM+15.995GNWQLR,0.958028454046983,"[1.0, 0.98613041639328, 0.9997624754905701, 0.9999221563339233, 0.9695665240287781, 0.9999978542327881, 0.9999949932098389, 0.9999998807907104, 0.9959664344787598, 0.9999760389328003, 0.9502813816070557, 0.982671856880188, 0.5700998902320862]"
-1642,LQAQSLSSVGPR,0.9985347588857015,"[0.9999986886978149, 0.9954858422279358, 0.9999997615814209, 0.9957975149154663, 0.9999985694885254, 1.0, 0.9999994039535522, 1.0, 0.999994158744812, 0.9999715089797974, 0.9999380111694336, 0.9912336468696594]"
-1643,ELAEQTLNNVK,0.9636013236912814,"[0.9999971389770508, 0.9999998807907104, 1.0, 0.9999048709869385, 0.9995042085647583, 1.0, 0.9999991655349731, 0.9964643716812134, 0.9965077042579651, 0.6153373718261719, 0.991899847984314]"
-1644,VSLSEGDDKLEYR,0.9996854937993563,"[0.9999995231628418, 1.0, 1.0, 1.0, 0.9999973773956299, 1.0, 0.9998795986175537, 0.9999991655349731, 0.9999793767929077, 0.9994509816169739, 0.9993703961372375, 0.9973607659339905, 0.9998742341995239]"
-1645,STPSSGDVQVTEDAVR,0.9833124130964279,"[0.9999797344207764, 0.9410690665245056, 0.9999998807907104, 1.0, 0.999996542930603, 0.9998119473457336, 0.9999405145645142, 1.0, 0.9912842512130737, 0.9983171224594116, 0.9999984502792358, 0.9999600648880005, 0.9778501987457275, 0.9999631643295288, 0.8405107259750366, 0.9843169450759888]"
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-1647,C+57.021EWNSQPLEC+57.021,0.8499844759702683,"[0.9999632835388184, 0.7954620122909546, 0.5203043222427368, 0.8325130343437195, 0.9837586283683777, 0.37594303488731384, 0.9999933242797852, 0.9991565942764282, 0.9933722019195557, 0.9993783235549927]"
-1648,QDGHLWC+57.021STTSNYEQDQK,0.9438855979177687,"[0.5821922421455383, 0.9999094009399414, 0.9999977350234985, 0.990864098072052, 0.9654973745346069, 0.9999988079071045, 0.9999884366989136, 0.9707610011100769, 0.9999997615814209, 0.9999920129776001, 0.9999992847442627, 0.9754287004470825, 0.9999510049819946, 0.9996383190155029, 0.9909895658493042, 0.999669075012207, 0.99757319688797, 0.5174907445907593]"
-1649,EDQSLLC+57.021TGESGAGK,0.9932133595148722,"[0.9999996423721313, 1.0, 0.9823885560035706, 1.0, 1.0, 0.9999997615814209, 0.9999997615814209, 1.0, 1.0, 0.9999524354934692, 0.9999978542327881, 0.9999547004699707, 0.9999995231628418, 0.9995357990264893, 0.9163723587989807]"
-1650,GFM+15.995VQTGDPTSQR,0.953892286007221,"[0.9999998807907104, 0.9996376037597656, 0.9996166229248047, 0.9999940395355225, 0.989778459072113, 0.9999734163284302, 0.9745687246322632, 0.9999102354049683, 0.9999762773513794, 0.9982151985168457, 0.9809858202934265, 0.48143088817596436, 0.9765125513076782]"
-1651,LETLEVM+15.995EDR,0.9943707644939422,"[1.0, 0.9999945163726807, 1.0, 0.9999974966049194, 0.9999995231628418, 0.9999728202819824, 0.9997382760047913, 0.9957242012023926, 0.9682844877243042, 0.9799963235855103]"
-1652,LNSVPFR,0.9119828683989388,"[0.9973222613334656, 0.9301396012306213, 0.5052997469902039, 0.9742656946182251, 0.9998996257781982, 0.9796352982521057, 0.9973178505897522]"
-1653,LM+15.995DLLQK,0.936498897416251,"[0.9999223947525024, 0.9999871253967285, 0.9992813467979431, 0.9957044720649719, 0.9985502362251282, 0.985496461391449, 0.5765502452850342]"
-1654,EVGEETKEEFTATEEALDEETK,0.8487154787236993,"[0.6303073763847351, 0.7211224436759949, 0.4722314476966858, 0.9998890161514282, 0.8086075782775879, 0.9693241715431213, 0.7983037829399109, 0.8132257461547852, 0.9679750204086304, 0.9936544895172119, 0.8687559366226196, 0.4345206022262573, 0.7582753300666809, 0.9970664381980896, 0.9986118078231812, 0.7541696429252625, 0.9999793767929077, 0.9914078116416931, 0.9977052807807922, 0.7337623834609985, 0.9810888767242432, 0.9817559719085693]"
-1655,VLERDLVGLDDFR,0.7995321039970105,"[0.39695268869400024, 0.7164744734764099, 0.8973099589347839, 0.48453375697135925, 0.9246253371238708, 0.7806087136268616, 0.733478844165802, 0.9999513626098633, 0.9960699081420898, 0.9918310046195984, 0.841949462890625, 0.63157719373703, 0.9985546469688416]"
-1656,AFGPGLQGGNAGSPAR,0.9966940991580486,"[0.9999997615814209, 0.9999967813491821, 1.0, 1.0, 1.0, 1.0, 0.9827345013618469, 1.0, 0.9842134714126587, 0.995205283164978, 0.9999995231628418, 0.9998663663864136, 0.9860219955444336, 0.9996970891952515, 0.9997668862342834, 0.9996039271354675]"
-1657,FLTDNTVEER,0.9997023940086365,"[1.0, 0.9999978542327881, 1.0, 0.9996626377105713, 0.9985024929046631, 0.9999854564666748, 0.9999916553497314, 0.9999845027923584, 0.9997691512107849, 0.9991301894187927]"
-1658,DQVASNAFLDR,0.8846279328519647,"[0.9964753985404968, 0.9791160225868225, 1.0, 0.9999979734420776, 0.999969482421875, 0.9516028165817261, 0.9835703372955322, 0.8920893669128418, 0.9193613529205322, 0.5307903289794922, 0.47793418169021606]"
-1659,TGETQSGPTWLGEK,0.8442531909261431,"[0.9999995231628418, 0.9288010597229004, 0.9999833106994629, 0.9996002316474915, 0.4486018121242523, 0.9999998807907104, 0.9999990463256836, 0.9999607801437378, 0.9832874536514282, 0.269925981760025, 0.9059315323829651, 0.9977160692214966, 0.5406087636947632, 0.7451292276382446]"
-1660,ELVNYGANVNAQSQK,0.9350625435511272,"[0.999998927116394, 0.9999676942825317, 1.0, 0.9788628816604614, 0.9999998807907104, 0.9999967813491821, 0.9655490517616272, 0.5526734590530396, 0.9999920129776001, 0.9991922974586487, 0.9999985694885254, 0.9995321035385132, 0.9969918727874756, 0.6921716928482056, 0.8410109281539917]"
-1661,LQEPEDLNVESPPQQDPK,0.7614226357804404,"[0.9999997615814209, 0.9417157769203186, 0.999835729598999, 0.5727609395980835, 0.898649275302887, 0.9779019355773926, 0.9942611455917358, 0.46507689356803894, 0.5091624855995178, 0.9764682054519653, 0.9965420365333557, 0.5116480588912964, 0.4105004072189331, 0.33099645376205444, 0.7357462644577026, 0.5600234270095825, 0.8434411287307739, 0.9808775186538696]"
-1662,LQEQSQLLQLPQGNVDPK,0.7129557886057429,"[0.9998562335968018, 0.8282285928726196, 0.7614345550537109, 0.43619439005851746, 0.9568161368370056, 0.28695181012153625, 0.4753851294517517, 0.5886688828468323, 0.7052285671234131, 0.9223547577857971, 1.0, 0.9957098960876465, 0.8232567310333252, 0.7936075329780579, 0.25190967321395874, 0.2296529859304428, 0.9021192789077759, 0.8758290410041809]"
-1663,GYSEFEAAAASGDSHGDLH,0.8075372357117502,"[0.9999861717224121, 0.8361742496490479, 0.9991057515144348, 0.6771031022071838, 0.5568101406097412, 0.675574004650116, 0.7524211406707764, 0.9519367218017578, 0.709172785282135, 0.6821019649505615, 0.732949435710907, 0.9545737504959106, 0.969864010810852, 0.9594447612762451, 0.5525575280189514, 0.7220633029937744, 0.7566490173339844, 0.9000458121299744, 0.9546738266944885]"
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-1823,APFDLSM+15.995TSGLM+15.995R,0.8362353260700519,"[0.9558334946632385, 0.44356709718704224, 0.9272680282592773, 0.9963257908821106, 0.923376202583313, 0.924164891242981, 0.8584786653518677, 0.9964911341667175, 0.9999120235443115, 0.9999626874923706, 0.6468972563743591, 0.2004307508468628, 0.9983512163162231]"
-1824,EGN+0.984GTVMGAELR,0.9615353445212046,"[1.0, 0.9999998807907104, 0.9937933683395386, 1.0, 0.999997615814209, 0.9999558925628662, 0.9999933242797852, 0.9986259937286377, 0.9999557733535767, 0.9990299940109253, 0.5982421636581421, 0.9488301277160645]"
-1825,AEYFDGSEPVQNR,0.9955713015336257,"[1.0, 0.9964255690574646, 0.9999998807907104, 0.9999972581863403, 0.9999918937683105, 1.0, 0.9999992847442627, 0.9999953508377075, 0.999997615814209, 0.9999709129333496, 0.9977097511291504, 0.9743964076042175, 0.9739429950714111]"
-1826,DDEELLHNPSNVVSK,0.8228837807973226,"[0.5339029431343079, 0.761930525302887, 0.4870399236679077, 0.8815220594406128, 0.7700516581535339, 0.7640084028244019, 0.6979342103004456, 0.6522176265716553, 1.0, 0.9999998807907104, 0.9484431743621826, 0.9505524039268494, 0.9781888127326965, 0.9189720153808594, 0.9984930753707886]"
-1827,AETQPQQQQQPAADEAAPK,0.7211808076030329,"[0.9789772033691406, 0.24992135167121887, 0.3662530779838562, 0.9227085113525391, 0.3886326551437378, 0.563280463218689, 0.6952104568481445, 0.589884877204895, 0.8809307217597961, 0.4401978850364685, 0.9997075200080872, 0.9961649179458618, 0.6601619720458984, 0.6316138505935669, 0.9470326900482178, 0.8345430493354797, 0.5664371252059937, 0.993770182132721, 0.9970068335533142]"
-1828,TALLNSTGEEVAM+15.995R,0.9972529794488635,"[0.9999995231628418, 0.9999176263809204, 1.0, 1.0, 0.9918959736824036, 0.9999998807907104, 1.0, 0.9999992847442627, 0.9998656511306763, 0.9999799728393555, 0.9872967004776001, 0.9993128776550293, 0.9990228414535522, 0.9842513799667358]"
-1829,EMEESQPC+57.021VPGSGADGPPK,0.9051843865921623,"[0.9838479161262512, 0.9769510626792908, 0.9999619722366333, 0.9999037981033325, 0.9999961853027344, 0.7426288723945618, 1.0, 0.999990701675415, 0.9999994039535522, 1.0, 0.999629020690918, 0.9280000329017639, 0.3689550459384918, 0.5518432855606079, 0.672822892665863, 0.9998854398727417, 0.9769853353500366, 0.9984476566314697, 0.9986547231674194]"
-1830,AALVEC+57.021NAVVGAGVF,0.786587917804718,"[0.5418236255645752, 0.376614511013031, 0.7042403817176819, 0.7189774513244629, 0.9995999932289124, 0.9896166324615479, 0.5737740397453308, 0.8463141322135925, 0.9939344525337219, 0.8011572957038879, 0.9999994039535522, 0.9991176724433899, 0.9376171827316284, 0.9472266435623169, 0.3688053488731384]"
-1831,SQGELLENYER,0.6105316186493094,"[0.9991010427474976, 0.8848077058792114, 0.3820202648639679, 0.9261995553970337, 0.5727396011352539, 0.7472580075263977, 0.24422864615917206, 0.5195909738540649, 0.4331090748310089, 0.23831132054328918, 0.7684816122055054]"
-1832,VEPGLGADQASLR,0.879400046972128,"[0.99998939037323, 0.9976168870925903, 1.0, 0.9999998807907104, 0.9999996423721313, 0.9999998807907104, 0.9927393794059753, 0.9996280670166016, 0.8801069855690002, 0.622413158416748, 0.42416995763778687, 0.51567542552948, 0.9998619556427002]"
-1833,QSVEADLN+0.984GLR,0.9398143589496613,"[0.9848570823669434, 0.9897199273109436, 1.0, 0.9999992847442627, 0.9999990463256836, 0.9996598958969116, 0.9998970031738281, 0.9505208730697632, 0.9902805089950562, 0.4771324694156647, 0.9458918571472168]"
-1834,GVTC+57.021VTMNPVEQK,0.8179777952340933,"[1.0, 0.9919300079345703, 0.9990139007568359, 0.9999804496765137, 0.9982524514198303, 0.6684712171554565, 0.48867759108543396, 0.6286629438400269, 0.9999825954437256, 0.9994019269943237, 0.4247564971446991, 0.5125471949577332, 0.9220345616340637]"
-1835,DLDLSSER,0.572765314951539,"[0.9965823292732239, 0.5527243614196777, 0.6315489411354065, 0.39826783537864685, 0.17848153412342072, 0.3761371076107025, 0.4525429606437683, 0.9958374500274658]"
-1836,VQFASQELAEK,0.9952978871085427,"[1.0, 0.957956850528717, 1.0, 0.9999978542327881, 0.9999922513961792, 0.9986769556999207, 0.9998435974121094, 0.9999277591705322, 0.9989860653877258, 0.9954924583435059, 0.9974029660224915]"
-1837,C+57.021C+57.021LTYC+57.021FNKPEDK,0.942952036857605,"[1.0, 0.9999641180038452, 0.999152421951294, 0.9999597072601318, 0.9999986886978149, 0.5152764320373535, 0.9253979325294495, 0.8344314098358154, 0.999992847442627, 0.9999982118606567, 0.9990377426147461, 0.9891314506530762, 0.9960355162620544]"
-1838,N+0.984C+57.021FMAQR,0.8375603045736041,"[0.9706748723983765, 0.9631665945053101, 0.9992818236351013, 0.9997894167900085, 0.46513110399246216, 0.5021027326583862, 0.9627755880355835]"
-1839,DGALEQEELR,0.9109206765890121,"[0.8546906113624573, 0.9997162222862244, 0.9995833039283752, 1.0, 0.9974005222320557, 0.9799326658248901, 0.3611391484737396, 0.9989045858383179, 0.9849627017974854, 0.9328770041465759]"
-1840,FGGEVDASTDAK,0.7433424877623717,"[0.9999982118606567, 0.9983413219451904, 0.9954872727394104, 0.4281581938266754, 0.9421998858451843, 0.9006786346435547, 0.6176709532737732, 0.48911547660827637, 0.23045183718204498, 0.5513436794281006, 0.7727246284484863, 0.9939397573471069]"
-1841,EVFGDDSELSK,0.9998584443872626,"[0.9998753070831299, 0.9998973608016968, 1.0, 1.0, 0.9999412298202515, 0.9999650716781616, 0.999881386756897, 0.9997780919075012, 0.9999774694442749, 0.9993259906768799, 0.999800980091095]"
-1842,TAPAAVAAPVELLLEGGAEER,0.7129732498100826,"[0.7456845641136169, 0.9965659976005554, 0.9970600008964539, 0.9939706325531006, 0.4395023286342621, 0.8901721239089966, 0.9907291531562805, 0.8862472176551819, 0.8682524561882019, 0.7220368385314941, 0.8653526902198792, 0.47385334968566895, 0.8505330681800842, 0.4155847132205963, 0.5298291444778442, 0.35381457209587097, 0.4874591827392578, 0.3188778758049011, 0.6346102356910706, 0.9211913347244263, 0.5911107659339905]"
-1843,STELLLR,0.9890037860189166,"[1.0, 0.9999994039535522, 0.9995349645614624, 0.9997383952140808, 0.9993146657943726, 0.999910831451416, 0.9245282411575317]"
-1844,TGQLFLK,0.9980529035840716,"[0.9999992847442627, 1.0, 0.99460369348526, 0.9992173910140991, 0.9994354844093323, 0.994362473487854, 0.9987519979476929]"
-1845,LPEQGNWLNSASAPQGEVQTR,0.8307331935280845,"[0.7625608444213867, 0.9766408801078796, 0.9970646500587463, 0.5746034979820251, 0.3771158456802368, 0.24641959369182587, 0.5061415433883667, 0.3201025724411011, 0.8802096247673035, 0.9846400022506714, 0.9999765157699585, 0.9870510101318359, 0.8401914834976196, 0.9999982118606567, 0.9993222951889038, 1.0, 0.9999991655349731, 0.9999905824661255, 0.9993807077407837, 0.9975157976150513, 0.9964722394943237]"
-1846,LWDPTPSSHPAAAGTPGR,0.9770006736119589,"[0.9999998807907104, 0.9999969005584717, 0.9999970197677612, 0.9993274211883545, 0.9999382495880127, 0.9999984502792358, 0.9356347918510437, 0.9880875945091248, 0.9883308410644531, 1.0, 0.9998039603233337, 0.9999994039535522, 0.9999991655349731, 0.8457234501838684, 0.9999886751174927, 0.9999829530715942, 0.8454796075820923, 0.9837237596511841]"
-1847,VAPQEEDSASEAGTPQGEVQTR,0.9688510921868411,"[0.9679811596870422, 0.9867326617240906, 1.0, 0.8580959439277649, 0.9244365096092224, 0.9956329464912415, 0.9716163277626038, 0.6311475038528442, 0.9999918937683105, 0.9868638515472412, 0.9998964071273804, 0.9999996423721313, 0.9997294545173645, 0.9992790818214417, 1.0, 0.998645007610321, 0.9999995231628418, 0.9968345761299133, 0.9999575614929199, 0.9984264373779297, 0.9997608065605164, 0.9996967315673828]"
-1848,LEELDQLLMEDQNC+57.021K,0.8573510845502218,"[0.9091736674308777, 0.9999821186065674, 0.6341453790664673, 0.5403039455413818, 0.5724541544914246, 0.9720419049263, 0.9993048906326294, 0.9999998807907104, 0.9982098340988159, 0.9979497790336609, 0.9999864101409912, 0.51905757188797, 0.9920936226844788, 0.8995981812477112, 0.8259649276733398]"
-1849,EPTVEDSDEEALDEETK,0.9575922699535594,"[0.9709038138389587, 0.8504878282546997, 0.9998840093612671, 0.9907832145690918, 0.9994300007820129, 0.6398007273674011, 0.8778780102729797, 0.964302122592926, 0.9998476505279541, 0.9999995231628418, 0.9936821460723877, 1.0, 0.9948350191116333, 0.9990158081054688, 0.9997395873069763, 0.9999854564666748, 0.9984936714172363]"
-1850,KDLETNADLFLSLDMAGNGEGGAGEQGR,0.7576539580311094,"[0.9845263957977295, 0.9198704957962036, 0.892686665058136, 0.7515391707420349, 0.5836098194122314, 0.2998356819152832, 0.44928672909736633, 0.7380074858665466, 0.5976212024688721, 0.7542444467544556, 0.9735015034675598, 0.6725616455078125, 0.3495606482028961, 0.6096270680427551, 0.5329400300979614, 0.7709916830062866, 0.8062607049942017, 0.5896794199943542, 0.9457656145095825, 0.9312847852706909, 0.8514759540557861, 0.5286458134651184, 0.800390899181366, 0.9999996423721313, 0.982124388217926, 0.9586690068244934, 0.9748679399490356, 0.9647359848022461]"
-1851,C+57.021VSVQTDPTDEVPTK,0.9985074400901794,"[1.0, 0.9999094009399414, 1.0, 1.0, 0.9842934012413025, 0.9999920129776001, 0.9991463422775269, 1.0, 0.9999997615814209, 0.999744713306427, 0.9954558610916138, 0.9999985694885254, 0.9999995231628418, 0.9994996786117554, 0.9995723366737366]"
-1852,WLYETSK,0.957591678415026,"[0.9990563988685608, 0.9740471243858337, 0.9149309992790222, 0.9183693528175354, 0.9638817310333252, 0.9864192605018616, 0.946436882019043]"
-1853,DNLQGLTKPALR,0.9542569915453593,"[0.9999969005584717, 0.9819357991218567, 1.0, 0.992584228515625, 0.9999967813491821, 0.9999973773956299, 0.9999961853027344, 1.0, 0.9940206408500671, 0.9964973330497742, 0.8046733140945435, 0.681385338306427]"
-1854,VYQGC+57.021SGNGYER,0.747741661965847,"[0.996410071849823, 0.8613382577896118, 0.9629451632499695, 0.9753125905990601, 0.9842661619186401, 0.8314814567565918, 0.6979063749313354, 0.5773155093193054, 0.6647517085075378, 0.3695632219314575, 0.31167086958885193, 0.7399385571479797]"
-1855,ENLNDEPPEGMTR,0.8035116402002481,"[0.9987595081329346, 0.9917237162590027, 0.9987710118293762, 0.8348750472068787, 0.9929245710372925, 0.5550698637962341, 0.4352369010448456, 0.6348177194595337, 0.41524916887283325, 0.9999994039535522, 0.6046724915504456, 0.9844043850898743, 0.9991475343704224]"
-1856,LGDLYDEEM+15.995R,0.9723986208438873,"[0.9999992847442627, 0.9999986886978149, 0.999990701675415, 0.9999992847442627, 0.9148768186569214, 0.9958123564720154, 0.9992489218711853, 0.9876289367675781, 0.8386727571487427, 0.987758457660675]"
-1857,TTEGDSSNPSLPSGDQGPPK,0.7541007831692695,"[0.5074029564857483, 0.49536019563674927, 0.6264087557792664, 0.4142282009124756, 0.2990705668926239, 0.38967686891555786, 0.9681767821311951, 0.8643841743469238, 0.9744406938552856, 0.5344520211219788, 0.7650062441825867, 1.0, 0.9999995231628418, 0.9999607801437378, 0.9983078241348267, 0.6465070247650146, 0.9994346499443054, 0.9352768063545227, 0.9997850060462952, 0.6641365885734558]"
-1858,ELEDATETADAMNR,0.9995831208569663,"[0.9999998807907104, 1.0, 0.9999988079071045, 0.9999908208847046, 0.9999995231628418, 1.0, 0.9999901056289673, 1.0, 0.9999998807907104, 0.9999972581863403, 0.999948263168335, 0.9999771118164062, 0.9980039000511169, 0.9962581396102905]"
-1859,QLLC+57.021AYHTLGAGGGTPGR,0.7163108835617701,"[0.5223050713539124, 0.5887815356254578, 0.5372922420501709, 0.6291607618331909, 0.9957730174064636, 0.49480295181274414, 0.802996814250946, 0.4919349253177643, 0.5251855850219727, 0.6484504342079163, 0.6783372163772583, 0.6974652409553528, 0.9592295289039612, 0.4943775534629822, 0.9147706627845764, 0.9997932314872742, 0.9222671389579773, 0.9906719923019409]"
-1860,TGEDEDEEDNDALLK,0.9962238987286886,"[0.9999990463256836, 0.9999994039535522, 0.9990077614784241, 0.9962743520736694, 0.9991428852081299, 0.999984622001648, 0.9996916055679321, 0.9999940395355225, 0.9770232439041138, 0.9859021902084351, 0.9909204244613647, 0.9972230195999146, 0.9992944002151489, 0.9990174770355225, 0.9998840093612671]"
-1861,DC+57.021GSLSYGNEDALK,0.9370732605457306,"[0.9999587535858154, 0.9941781759262085, 0.99998939037323, 0.657040536403656, 0.9999997615814209, 0.9999988079071045, 1.0, 1.0, 0.9985793828964233, 0.6370015144348145, 0.850878119468689, 0.9892725944519043, 0.994889497756958, 0.9972391128540039]"
-1862,VAADQFLLDGHR,0.8228514567017555,"[0.9886661767959595, 0.7343026995658875, 0.8080773949623108, 0.8372806906700134, 0.48769411444664, 0.9971210360527039, 0.9989833235740662, 0.9831627607345581, 0.9844948649406433, 0.999302864074707, 0.5540981888771057, 0.501033365726471]"
-1863,EGTTLEQSLEANK,0.8441603733943059,"[0.9463998079299927, 0.620894730091095, 0.5101194977760315, 0.7921027541160583, 1.0, 0.9995691180229187, 0.9538926482200623, 0.3411404490470886, 0.9999626874923706, 0.9966044425964355, 0.9722514748573303, 0.8890362977981567, 0.9521109461784363]"
-1864,C+57.021C+57.021EEWVC+57.021DEPK,0.9788642904975198,"[0.9999998807907104, 0.9998735189437866, 0.9973905682563782, 0.9999988079071045, 0.9999933242797852, 0.999998927116394, 0.9999997615814209, 0.9978972673416138, 0.9997015595436096, 0.9970694184303284, 0.7755841612815857]"
-1865,DVYEELAR,0.873234286904335,"[0.9996758699417114, 0.9851996898651123, 0.999993085861206, 0.9965550899505615, 0.9568284749984741, 0.9919997453689575, 0.46583425998687744, 0.5897880792617798]"
-1866,EGAPLEPDPPSVH,0.8473056875742399,"[0.5519405603408813, 0.8301024436950684, 0.9907050728797913, 0.9999730587005615, 0.9999986886978149, 0.9998052716255188, 0.9999998807907104, 0.7330756187438965, 0.9999547004699707, 0.9955083131790161, 0.4609232544898987, 0.9962553977966309, 0.4567316770553589]"
-1867,VSVADFSAEADETR,0.90425286761352,"[0.9998014569282532, 0.9967005848884583, 0.9914621114730835, 0.9520334005355835, 0.46260371804237366, 0.9999995231628418, 0.9997574687004089, 0.9985408782958984, 0.9997225403785706, 0.9989128112792969, 0.6707043647766113, 0.9940370321273804, 0.6491649746894836, 0.9460992813110352]"
-1868,EMEESNPC+57.021VPGSDGAGPPK,0.9695241953197279,"[0.9751453399658203, 0.987236499786377, 0.9995625615119934, 0.9968735575675964, 0.9966112971305847, 0.9599704742431641, 1.0, 0.9999086856842041, 0.9998569488525391, 1.0, 0.9984791874885559, 0.6641363501548767, 0.926752507686615, 0.9999983310699463, 0.9943927526473999, 0.9737436771392822, 0.9486228227615356, 0.9998316764831543, 0.9998370409011841]"
-1869,QAQQERDELADELANSSGK,0.9899571412488034,"[0.9972551465034485, 1.0, 0.9934831261634827, 0.9858470559120178, 1.0, 0.9999974966049194, 1.0, 1.0, 0.9998283386230469, 1.0, 1.0, 1.0, 1.0, 0.9999940395355225, 0.9962083101272583, 0.9998406171798706, 0.9993683695793152, 0.9046377539634705, 0.9327254295349121]"
-1870,ALEQQVEEMR,0.9901833057403564,"[0.9999998807907104, 1.0, 0.999991774559021, 0.997027575969696, 0.998396098613739, 0.9999995231628418, 0.999909520149231, 0.9999674558639526, 0.9804201126098633, 0.9261211156845093]"
-1871,DTNFGTNC+57.021LC+57.021R,0.9931981780312278,"[0.9997676014900208, 0.9999971389770508, 0.9883684515953064, 0.9999797344207764, 0.999997615814209, 0.9999948740005493, 0.9971025586128235, 0.9998786449432373, 0.9663011431694031, 0.9984256029129028, 0.9753665924072266]"
-1872,VQEALDNELPK,0.6620754220268943,"[0.9999735355377197, 0.9280452132225037, 0.5804412961006165, 0.23240438103675842, 0.8207507133483887, 0.33528581261634827, 0.39702004194259644, 0.539342999458313, 0.9853407740592957, 0.5010205507278442, 0.9632043242454529]"
-1873,DYALELDPTK,0.7185239493846893,"[0.9993075132369995, 0.6419777274131775, 0.3245810866355896, 0.883278489112854, 0.4157446026802063, 0.5286047458648682, 0.5180277228355408, 0.999747097492218, 0.9019004106521606, 0.9720700979232788]"
-1874,GAVNTAPTAPASAAEPK,0.8254947977907517,"[0.9999507665634155, 0.598300039768219, 0.8552341461181641, 0.9739444851875305, 0.5987926125526428, 0.48090243339538574, 1.0, 1.0, 0.9998966455459595, 0.9999992847442627, 0.978394091129303, 0.8716893196105957, 0.9722176790237427, 0.43924131989479065, 0.3908316195011139, 0.8952454924583435, 0.9787716269493103]"
-1875,LLAAWSDKPGTK,0.7731273372968038,"[0.9999855756759644, 0.9993322491645813, 0.9982792139053345, 0.9907267093658447, 0.26515689492225647, 0.936622142791748, 0.5533314943313599, 0.3552282750606537, 0.9987280964851379, 0.9439871907234192, 0.6147366762161255, 0.62141352891922]"
-1876,C+57.021GFQDDVAYGK,0.9968598268248818,"[0.9999991655349731, 0.9999984502792358, 1.0, 0.9951034784317017, 0.9965434670448303, 0.9999990463256836, 0.9995648264884949, 0.9996800422668457, 0.995114803314209, 0.9983426332473755, 0.9811121821403503]"
-1877,SSGGGSFSSGGLSSGSSASSVTVTR,0.8793155169486999,"[0.997062623500824, 0.9328334331512451, 0.9994202852249146, 0.8236747980117798, 0.9485275745391846, 0.8145322799682617, 0.2731808125972748, 0.6301377415657043, 0.9248948097229004, 0.6025716066360474, 0.9879565834999084, 0.43379536271095276, 0.9681255221366882, 0.9840233325958252, 0.9999926090240479, 0.9999909400939941, 0.9999978542327881, 0.9999960660934448, 0.9999985694885254, 0.9999961853027344, 0.9999983310699463, 0.9999285936355591, 0.9802062511444092, 0.9455007314682007, 0.7365450263023376]"
-1878,GSLDKNWLNSASAPQGEVQTR,0.7777898694787707,"[0.45079830288887024, 0.41169464588165283, 0.7519376277923584, 0.9919179081916809, 0.3131163716316223, 0.3660871982574463, 0.5823519229888916, 0.40097683668136597, 0.8708081245422363, 0.3629135489463806, 0.9999042749404907, 0.9642673134803772, 0.8871644139289856, 0.9999974966049194, 0.9987678527832031, 0.9999998807907104, 0.9999990463256836, 0.9999847412109375, 0.9990831613540649, 0.9844654202461243, 0.9973511695861816]"
-1879,LWDPTPSHQHFLPSR,0.9414294441541036,"[1.0, 0.9999895095825195, 0.9999967813491821, 0.9854745864868164, 0.8574288487434387, 1.0, 0.996826171875, 0.9997016787528992, 0.87306147813797, 0.9943037629127502, 0.7559486627578735, 0.9906269311904907, 0.9996399879455566, 0.69150710105896, 0.9769361615180969]"
-1880,ASQGWEDGAGEQLR,0.9646981741700854,"[0.9999973773956299, 0.9824429750442505, 0.9786721467971802, 0.9999672174453735, 0.9999926090240479, 0.9999908208847046, 0.999561607837677, 0.9999672174453735, 0.5595243573188782, 0.999995231628418, 0.9999688863754272, 0.9959102869033813, 0.9971340894699097, 0.9926496148109436]"
-1881,YSQVLGQGLDNK,0.9799268047014872,"[0.9999998807907104, 0.9999959468841553, 0.9835802912712097, 0.9998741149902344, 0.9999992847442627, 0.9999996423721313, 0.9922868013381958, 0.999796450138092, 0.9999885559082031, 0.9781907796859741, 0.807098388671875, 0.9983115196228027]"
-1882,SDEEVYQMGDVQSK,0.9236510545015335,"[0.9662335515022278, 0.6852835416793823, 0.869521975517273, 0.9846886396408081, 0.9999988079071045, 0.9998519420623779, 0.9949240684509277, 0.9999828338623047, 0.9999898672103882, 0.9773061871528625, 0.9967357516288757, 0.4867067039012909, 0.9709370732307434, 0.9989538192749023]"
-1883,SVEVN+0.984GSQMMASGPSK,0.9515043161809444,"[0.9999998807907104, 0.9999991655349731, 0.9999996423721313, 0.9999872446060181, 0.9883042573928833, 1.0, 0.9269866347312927, 0.9861990213394165, 0.8244527578353882, 0.7678964138031006, 0.9999984502792358, 0.9999853372573853, 0.9999994039535522, 0.9979071617126465, 0.7339752912521362, 0.9983783960342407]"
-1884,LSEEDADYALK,0.9768476486206055,"[1.0, 0.9999750852584839, 0.9991735816001892, 0.9998390674591064, 0.9979575872421265, 0.9461972117424011, 0.9621357917785645, 0.9888322353363037, 0.994712769985199, 0.9578450918197632, 0.8986557126045227]"
-1885,GSSSSSYQDSEVTSPPR,0.8326602841124815,"[0.9474416971206665, 0.3352031707763672, 0.3939303755760193, 0.21688708662986755, 0.9735445380210876, 0.8320894241333008, 0.9924671053886414, 0.9722740054130554, 0.9977251887321472, 0.9555988311767578, 0.9884768724441528, 0.8815804123878479, 0.715298593044281, 0.9824967980384827, 0.9999964237213135, 0.9960579872131348, 0.9741563200950623]"
-1886,FYEDN+0.984GQLDDAR,0.9896751542886099,"[1.0, 0.9987609386444092, 0.9999969005584717, 0.9909152388572693, 0.9382269382476807, 1.0, 0.9494187235832214, 1.0, 0.9999909400939941, 0.9996682405471802, 0.999225378036499, 0.9998985528945923]"
-1887,QSVEADLN+0.984GLR,0.9992634328928861,"[0.9989629983901978, 1.0, 1.0, 0.9999926090240479, 1.0, 0.9996447563171387, 0.9999837875366211, 0.9967107772827148, 0.9998530149459839, 0.9982958436012268, 0.9984539747238159]"
-1888,LSGETVLDELEEAC+57.021EGC+57.021YR,0.6584842291317488,"[0.6267472505569458, 0.7521601915359497, 0.5290175676345825, 0.9786560535430908, 0.5315409898757935, 0.6206154227256775, 0.4435681402683258, 0.32533949613571167, 0.9979804158210754, 0.9998940229415894, 0.9319108128547668, 0.8051223754882812, 0.9664170145988464, 0.9743243455886841, 0.37382376194000244, 0.21793822944164276, 0.3913189768791199, 0.6859029531478882, 0.3589223325252533]"
-1889,WLYDTSK,0.9980570759092059,"[0.9997331500053406, 0.9999681711196899, 0.9999994039535522, 0.9967032074928284, 0.9938626289367676, 0.9962078332901001, 0.9999251365661621]"
-1890,MC+57.021TGVLSAGVPPGPELVEEESG,0.6967642862688411,"[0.4033319056034088, 0.5223270058631897, 0.9239960312843323, 0.4354199767112732, 0.7084660530090332, 0.530038595199585, 0.9880478382110596, 0.6888977289199829, 0.5106349587440491, 0.86787348985672, 0.9999682903289795, 0.9998501539230347, 0.781051754951477, 0.33328554034233093, 0.3484977185726166, 0.34114471077919006, 0.7632842063903809, 0.9958654642105103, 0.9337733387947083, 0.9638141989707947, 0.8596311807632446, 0.4296141564846039]"
-1891,NC+57.021VLDSAQSAAGSYESAVLAK,0.7232490621861958,"[0.9931689500808716, 0.9819254875183105, 0.3527888357639313, 0.7721746563911438, 0.9958900809288025, 0.5000522136688232, 0.9982175230979919, 0.41642969846725464, 0.9496510624885559, 0.7712286710739136, 0.9598710536956787, 0.8475168347358704, 0.7194007039070129, 0.6066378355026245, 0.9261109232902527, 0.21800929307937622, 0.3481996953487396, 0.3298010528087616, 0.9875388145446777, 0.5202194452285767, 0.9933974742889404]"
-1892,DTANVDNLLYDTSK,0.9086475712912423,"[0.9966559410095215, 0.9644561409950256, 0.9293808341026306, 0.6419267058372498, 0.8983938694000244, 0.969601571559906, 0.9758449196815491, 0.8414590358734131, 0.9999414682388306, 0.9999709129333496, 0.9589777588844299, 0.9998074173927307, 0.9990890026092529, 0.5455604195594788]"
-1893,DSPLETM+15.995PSEEVPTK,0.8819805800914764,"[0.8921166658401489, 0.8983966708183289, 0.9994427561759949, 0.951326310634613, 0.4408695697784424, 0.9327391386032104, 0.38645055890083313, 1.0, 0.9999502897262573, 0.8972750902175903, 0.8497415781021118, 0.9998583793640137, 0.999962568283081, 0.9953906536102295, 0.9861884713172913]"
-1894,M+15.995PELLTSVDLEEETAQEK,0.7582302565375963,"[0.907169759273529, 0.2542775273323059, 0.8464657068252563, 0.5133963823318481, 0.4421231746673584, 0.6609952449798584, 0.9311171770095825, 0.233700230717659, 0.8341584205627441, 0.5229116678237915, 0.6278635859489441, 0.9991787075996399, 0.9999043941497803, 0.9999583959579468, 0.9997196793556213, 0.908219039440155, 0.9783687591552734, 0.9886167645454407]"
-1895,AYDC+57.021EATTPPLLEGNQAGDTAQTR,0.8191636999448141,"[0.8454239964485168, 0.8007810115814209, 0.5112718939781189, 0.825904905796051, 0.5055435299873352, 0.9589677453041077, 0.3943118155002594, 0.5594462156295776, 0.8460549712181091, 0.9701046943664551, 0.4721948802471161, 0.7073206901550293, 0.8252807855606079, 0.997333288192749, 0.9958517551422119, 0.9922385811805725, 0.9999960660934448, 0.9963397979736328, 0.9999769926071167, 0.9984672665596008, 0.9752429723739624, 0.9511991143226624, 0.5733614563941956, 0.9573143720626831]"
-1896,AC+57.021LGGTNVGEDLR,0.9967963191179129,"[0.9999994039535522, 0.9999979734420776, 0.9999992847442627, 1.0, 0.9999998807907104, 0.9999563694000244, 0.9977564215660095, 0.9998414516448975, 0.9999996423721313, 0.9971143007278442, 0.9992260932922363, 0.9938961863517761, 0.970565140247345]"
-1897,C+57.021C+57.021EEWVC+57.021DEPK,0.9998043450442228,"[1.0, 0.9999997615814209, 0.9999990463256836, 0.9999990463256836, 0.9999985694885254, 0.9999866485595703, 1.0, 0.9998264908790588, 0.9999818801879883, 0.9991044402122498, 0.9989519119262695]"
-1898,EGAPLEPDPPVSH,0.9318454219744756,"[0.9998925924301147, 0.9977549910545349, 0.9981566071510315, 1.0, 0.9999998807907104, 0.9999924898147583, 0.9999984502792358, 0.5490590929985046, 0.9999994039535522, 0.9999995231628418, 0.9959174990653992, 0.886601448059082, 0.6866185069084167]"
-1899,AVANQDGSLLYK,0.7286756113171577,"[1.0, 0.9256486892700195, 0.9999934434890747, 0.5183708071708679, 0.9896999597549438, 0.6392648816108704, 0.586491584777832, 0.5110741853713989, 0.48482975363731384, 0.6901453137397766, 0.7625720500946045, 0.6360166668891907]"
-1900,LMSPTLEDQER,0.9929029399698431,"[1.0, 0.999994158744812, 1.0, 1.0, 1.0, 0.9999991655349731, 0.9999977350234985, 0.9991219639778137, 0.9237342476844788, 0.9994770884513855, 0.9996079802513123]"
-1901,GSRGGSLSGGGYGGSGGGLAGGSSGK,0.9225169179531244,"[0.9999864101409912, 0.9980227947235107, 0.7814038395881653, 0.9973083734512329, 0.9998582601547241, 0.9993371367454529, 0.9963135123252869, 0.9997217059135437, 0.693100094795227, 0.9927940964698792, 0.9983978867530823, 0.9293392300605774, 0.6344627737998962, 0.8953481316566467, 0.7834203243255615, 0.9998486042022705, 0.9898694157600403, 0.9401796460151672, 0.9999251365661621, 0.9999858140945435, 0.972198486328125, 0.47411277890205383, 0.9588673710823059, 0.9999730587005615, 0.9829383492469788, 0.968726634979248]"
-1902,SNTDGEPYAAVC+57.021LQPGR,0.6800594662918764,"[0.9380977749824524, 0.9179575443267822, 0.6042609810829163, 0.3181224465370178, 0.5407860279083252, 0.8442864418029785, 0.832443356513977, 0.5283588171005249, 0.3644384443759918, 0.34488609433174133, 0.30304673314094543, 0.6565326452255249, 0.8587002754211426, 0.5914555191993713, 0.999854564666748, 0.9777563810348511, 0.9400268793106079]"
-1903,AGQQM+15.995VELAENGK,0.8983652500005869,"[0.9998843669891357, 0.7285996079444885, 0.5302103161811829, 0.830336332321167, 0.8782736659049988, 0.9999926090240479, 0.9970738887786865, 0.9999918937683105, 0.9986059069633484, 0.9972917437553406, 0.945085346698761, 0.994046688079834, 0.7793558835983276]"
-1904,EQQLVLQSSGGLSK,0.9994462898799351,"[0.9999953508377075, 0.9992451667785645, 0.9978231191635132, 1.0, 1.0, 1.0, 0.9996360540390015, 0.9999998807907104, 1.0, 1.0, 0.9999991655349731, 0.9999257326126099, 0.9981281161308289, 0.9974954724311829]"
-1905,DTDSQVSENPPLEGDLNGDATASAR,0.8012085223197937,"[0.4656125605106354, 0.5006962418556213, 0.8342994451522827, 0.9510623812675476, 0.5258965492248535, 0.36883315443992615, 0.9988211989402771, 0.9999973773956299, 0.5978181958198547, 0.8917733430862427, 0.9708757400512695, 0.9291613698005676, 0.9938755631446838, 0.9069607853889465, 0.9327252507209778, 0.637905478477478, 0.7198693156242371, 0.9737170934677124, 0.978825032711029, 0.999885082244873, 0.5935593247413635, 0.9999936819076538, 0.9999489784240723, 0.5070891380310059, 0.7510107755661011]"
-1906,SVMQEGAAPLPR,0.9948428173859915,"[0.9999996423721313, 1.0, 0.9999990463256836, 0.9960565567016602, 0.9999996423721313, 0.9999995231628418, 0.9998563528060913, 0.9995550513267517, 1.0, 0.9999226331710815, 0.9446459412574768, 0.9980794191360474]"
-1907,QVFM+15.995GGDLAEQLPK,0.6390320147786822,"[0.4241183400154114, 0.7595193386077881, 0.8081493377685547, 0.4510141909122467, 0.5061290264129639, 0.5707421898841858, 0.6388134360313416, 0.7743555903434753, 0.9639692902565002, 0.7351621985435486, 0.47020402550697327, 0.3428073525428772, 0.50691819190979, 0.9945456981658936]"
-1908,LAN+0.984GYDYLNPPLEGNQAGDTAASAR,0.8490287089347839,"[0.5341305732727051, 0.9868277311325073, 0.3880729377269745, 0.2838888466358185, 0.9086762070655823, 0.7886103391647339, 0.7819699645042419, 0.4728667140007019, 0.9846561551094055, 0.9999288320541382, 0.5728887319564819, 0.999417781829834, 0.9615622758865356, 0.9999995231628418, 0.9983131885528564, 0.9993777275085449, 0.940946638584137, 0.8635594844818115, 0.8047413229942322, 0.9999996423721313, 0.9999978542327881, 0.9999991655349731, 0.9994660019874573, 0.980434000492096, 0.9753860831260681]"
-1909,NLSYTSSEEDLEK,0.962253057039701,"[0.9539293646812439, 0.9999823570251465, 0.9999986886978149, 0.891393780708313, 0.9845735430717468, 0.9999979734420776, 0.9999854564666748, 0.7079682946205139, 0.9998888969421387, 0.9804805517196655, 0.9991975426673889, 0.9929023385047913, 0.9989909529685974]"
-1910,LGLTAGLR,0.9299080297350883,"[0.9997081160545349, 0.9999878406524658, 0.9991612434387207, 0.9991452693939209, 0.9991938471794128, 0.5183095335960388, 0.9253551363945007, 0.9984032511711121]"
-1911,LLEGEEDVLLLTGESGAGK,0.9022932648658752,"[0.9932711720466614, 0.9932911396026611, 0.8289734125137329, 0.5654551386833191, 0.9687198400497437, 0.5908504128456116, 0.9692996740341187, 0.2763345241546631, 0.9952801465988159, 0.9999958276748657, 0.9999725818634033, 1.0, 1.0, 0.9994269609451294, 0.9999910593032837, 0.9999657869338989, 0.999893069267273, 0.9941727519035339, 0.9686785340309143]"
-1912,GYQPC+57.021DPQVNLER,0.8892536209179804,"[0.9999995231628418, 0.9999817609786987, 0.9368177652359009, 0.9999998807907104, 0.9998853206634521, 0.9998003840446472, 0.9999969005584717, 0.9357413053512573, 0.7013056874275208, 0.7030423283576965, 0.6547076106071472, 0.634770929813385, 0.9942476749420166]"
-1913,LLDLDRDDGNPDAAPGGK,0.7047367178731494,"[0.9998761415481567, 0.9369601607322693, 0.3504698872566223, 0.9108390212059021, 0.814814031124115, 0.6004910469055176, 0.5298414826393127, 0.9814666509628296, 0.41564956307411194, 0.5402510166168213, 0.3902273178100586, 0.4434443712234497, 0.9214364290237427, 0.9974531531333923, 0.9997053742408752, 0.8534989953041077, 0.5012537837028503, 0.49758249521255493]"
-1914,GEATVDAYDPPASK,0.8866418663944516,"[1.0, 0.9981531500816345, 0.9998536109924316, 0.998117208480835, 0.9998366832733154, 0.9965434670448303, 0.7389665246009827, 0.9788913130760193, 0.9703845977783203, 0.999941349029541, 0.9999716281890869, 0.6193063259124756, 0.27323201298713684, 0.8397882580757141]"
-1915,NVPAAFGQPGR,0.7795569436116652,"[0.9506349563598633, 0.5100470781326294, 0.7575414776802063, 0.9349187612533569, 0.662351667881012, 0.438843697309494, 0.9945586323738098, 0.4309106469154358, 0.9966499209403992, 0.9334818720817566, 0.965187668800354]"
-1916,SNLNSNNFLLLTGESGAGK,0.8937176591471622,"[0.94139164686203, 0.7347831130027771, 0.4449768662452698, 0.9879277348518372, 0.8961696028709412, 0.9238423705101013, 0.7891945838928223, 0.49296754598617554, 0.8267555832862854, 0.9999899864196777, 0.9998558759689331, 1.0, 1.0, 0.9993138313293457, 0.9999568462371826, 0.9992852807044983, 0.9991764426231384, 0.9829628467559814, 0.9620853662490845]"
-1917,EAELTQLEPTGR,0.9804474959770838,"[0.9999980926513672, 0.9999876022338867, 0.9999923706054688, 0.9983788728713989, 0.9990228414535522, 0.9906893968582153, 0.9881994724273682, 0.9931843876838684, 0.9999986886978149, 0.9975882768630981, 0.8263884782791138, 0.9719414710998535]"
-1918,TVAALQQALNAEENVK,0.7504000682383776,"[0.9998189806938171, 0.605397641658783, 0.9490708708763123, 0.9999942779541016, 0.9996086955070496, 0.9451240301132202, 0.9305960536003113, 0.6075361967086792, 0.7948392629623413, 0.6290205121040344, 0.9963048696517944, 0.4608071446418762, 0.6212415099143982, 0.7955479025840759, 0.16715571284294128, 0.5043374300003052]"
-1919,TVLSAESEEQVR,0.958945761124293,"[1.0, 0.999925971031189, 0.9999998807907104, 0.9996588230133057, 0.9999758005142212, 0.9998983144760132, 0.9999892711639404, 0.9889563918113708, 0.9997844099998474, 0.9370107054710388, 0.5840982794761658, 0.9980512857437134]"
-1920,QSVEADLN+0.984GLR,0.9881373914805326,"[0.9953957200050354, 0.9995660185813904, 1.0, 0.9999988079071045, 1.0, 0.9995273351669312, 0.9805542230606079, 0.9936477541923523, 0.998276948928833, 0.9060150980949402, 0.9965294003486633]"
-1921,QWQPDTEENFDSNKDVTLR,0.75914277214753,"[0.6116508841514587, 0.8622245192527771, 0.49480167031288147, 0.999367892742157, 0.5551600456237793, 0.5392552018165588, 0.7286993265151978, 0.8516526222229004, 0.5001633167266846, 0.9949445128440857, 0.8805294036865234, 0.9999995231628418, 0.8826891183853149, 0.9998931884765625, 0.32982370257377625, 0.9997106194496155, 0.9912765026092529, 0.624495267868042, 0.5773753523826599]"
-1922,ASALMSEQPQLVAR,0.8449164288384574,"[0.9946093559265137, 0.5373198390007019, 0.8351241946220398, 0.7435178160667419, 0.5091480612754822, 0.9999961853027344, 0.9988162517547607, 0.9569970369338989, 0.9999997615814209, 0.9990845918655396, 0.5993584990501404, 0.9691586494445801, 0.7203792929649353, 0.9653204679489136]"
-1923,M+15.995GPSMGPALNALER,0.8667046981198447,"[0.9948457479476929, 0.9881070852279663, 0.9980084300041199, 0.6649510860443115, 0.7586493492126465, 0.9999974966049194, 0.9999865293502808, 0.9948745369911194, 0.9922159314155579, 0.9865216612815857, 0.5043670535087585, 0.6874619126319885, 0.6013733148574829, 0.9625056385993958]"
-1924,EAPADEDLDTLK,0.9994121839602789,"[0.9999995231628418, 0.9997716546058655, 1.0, 0.9999998807907104, 0.9977601766586304, 0.9999998807907104, 0.999995231628418, 0.9999479055404663, 0.9991700649261475, 0.9984741806983948, 0.9994400143623352, 0.9983876943588257]"
-1925,SEGSLSTVNANPPLEGNQAGDTAASAR,0.8915320701069303,"[0.9662548303604126, 0.531157910823822, 0.9686981439590454, 0.389027863740921, 0.9907193779945374, 0.6172031164169312, 0.3225095272064209, 0.9209702610969543, 0.9923715591430664, 0.41481414437294006, 0.9801791906356812, 0.9999971389770508, 0.9999881982803345, 0.9999954700469971, 0.9998151659965515, 0.9999998807907104, 0.9995552897453308, 0.999129593372345, 0.9994933605194092, 0.9999874830245972, 0.9999159574508667, 0.9999998807907104, 0.9999988079071045, 0.9999996423721313, 0.9996659755706787, 0.986793577671051, 0.9931245446205139]"
-1926,ELEAELEDER,0.9979020237922669,"[0.9999685287475586, 0.9999998807907104, 0.9988646507263184, 0.9999994039535522, 0.9997486472129822, 0.9999990463256836, 0.9999630451202393, 0.9892048835754395, 0.9989733695983887, 0.9922987818717957]"
-1927,LEGGDGEEEEDLADSGR,0.9423522756380194,"[0.9855633974075317, 0.7500345706939697, 0.9974617958068848, 0.9974554181098938, 0.42977991700172424, 0.9606201648712158, 0.9947198629379272, 0.9999953508377075, 0.9999372959136963, 0.9999790191650391, 0.9999185800552368, 0.9999629259109497, 0.9612155556678772, 0.946740984916687, 0.9995977282524109, 0.9999433755874634, 0.9970627427101135]"
-1928,LLLDYGNVTSK,0.7553078478032892,"[0.9928222298622131, 0.7708740234375, 0.29321378469467163, 0.9578043818473816, 0.8034935593605042, 0.8954848051071167, 0.3259149193763733, 0.33253222703933716, 0.9980742931365967, 0.9417039155960083, 0.996468186378479]"
-1929,YLEC+57.021AEC+57.021TSLDQSC+57.021ER,0.955743882805109,"[0.9979912042617798, 0.9990455508232117, 0.9996782541275024, 0.8933613896369934, 0.9999992847442627, 0.5748627185821533, 0.9997671246528625, 0.9997221827507019, 0.9999995231628418, 0.9999884366989136, 0.9999744892120361, 0.996915340423584, 0.9998959302902222, 0.9583709836006165, 0.8863305449485779, 0.9859991669654846]"
-1930,LSSEGGTWEEPSKPDTN+0.984GSNE,0.6495434372198015,"[0.6619291305541992, 0.4316118359565735, 0.7562274932861328, 0.7291500568389893, 0.9688841700553894, 0.4584721326828003, 0.354432612657547, 0.3194219768047333, 0.7073343396186829, 0.9893321990966797, 0.9641316533088684, 0.7161074876785278, 0.4102914035320282, 0.7473735809326172, 0.42954912781715393, 0.5657703876495361, 0.8188719153404236, 0.987270712852478, 0.9259981513023376, 0.3778779208660126, 0.32037389278411865]"
-1931,ELLQQVDTSTR,0.9990374825217507,"[0.9999997615814209, 0.9999996423721313, 1.0, 0.9976188540458679, 0.9988318085670471, 0.9999843835830688, 0.9992257356643677, 0.9999990463256836, 0.999269425868988, 0.9996540546417236, 0.9948295950889587]"
-1932,GAEAANVTGPDGVPVESGR,0.9535740300228721,"[0.9999998807907104, 1.0, 0.9986987113952637, 0.6393036246299744, 1.0, 0.9106330871582031, 0.9999998807907104, 0.9999773502349854, 1.0, 0.9999992847442627, 0.999854326248169, 0.9999992847442627, 0.9993656277656555, 1.0, 0.9999308586120605, 0.9822126626968384, 0.9997217059135437, 0.6083329916000366, 0.9798772931098938]"
-1933,NTVSESWVDTWQTFR,0.7436210016409556,"[0.9824599027633667, 0.8313761949539185, 0.9354873895645142, 0.5625738501548767, 0.6002393364906311, 0.998171329498291, 0.5345007181167603, 0.9999589920043945, 0.9889705181121826, 0.9983513355255127, 0.8069854974746704, 0.5560057759284973, 0.5573958158493042, 0.18293294310569763, 0.6189054250717163]"
-1934,M+15.995SVEADLN+0.984GLR,0.9881469878283414,"[0.9999991655349731, 0.9999998807907104, 0.9999997615814209, 0.9999997615814209, 0.9999985694885254, 0.9998935461044312, 0.9999232292175293, 0.9182614684104919, 0.9988757967948914, 0.9565226435661316, 0.9961430430412292]"
-1935,VVVC+57.021DNGTGFVK,0.998278538386027,"[1.0, 1.0, 1.0, 0.9999746084213257, 0.9999997615814209, 0.9986581802368164, 0.9999793767929077, 0.9923385381698608, 0.9998735189437866, 0.9988570213317871, 0.9997921586036682, 0.9898692965507507]"
-1936,TTGLVMDSGDGVTH,0.9450572941984449,"[1.0, 1.0, 1.0, 1.0, 0.9999939203262329, 0.9106380939483643, 0.9999926090240479, 0.9999997615814209, 0.9999005794525146, 0.6155316233634949, 0.9999586343765259, 0.9443478584289551, 0.9991138577461243, 0.7613251805305481]"
-1937,LAAELAQN+0.984QVGER,0.8331454877669995,"[0.9999998807907104, 0.997072696685791, 0.9999984502792358, 0.945075273513794, 0.9999915361404419, 0.9999442100524902, 0.9852871894836426, 0.7010194659233093, 0.7997280955314636, 0.8219186663627625, 0.453940749168396, 0.3788861334323883, 0.7480289936065674]"
-1938,AGAADQQPDLDDN+0.984GLAR,0.866563197444467,"[1.0, 0.9786328673362732, 0.9870619177818298, 0.9999997615814209, 0.8357619047164917, 0.6445038318634033, 0.9981909394264221, 0.9999980926513672, 0.7257633805274963, 0.6679530739784241, 0.9051588177680969, 0.9990344047546387, 0.6606214642524719, 0.999987006187439, 0.7476798892021179, 0.6791785955429077, 0.9020484089851379]"
-1939,GNNVLYLSTQK,0.9995148344473406,"[1.0, 0.9993575215339661, 0.9997790455818176, 1.0, 0.9999910593032837, 1.0, 0.9999974966049194, 0.9999997615814209, 0.9981005787849426, 0.9979554414749146, 0.999482274055481]"
-1940,FGPYESYDSR,0.9798674881458282,"[1.0, 0.999885082244873, 1.0, 0.9999996423721313, 0.9999992847442627, 0.9998137354850769, 0.9999417066574097, 0.9622017741203308, 0.9249842166900635, 0.9118494391441345]"
-1941,GLSQQC+57.021YDDLR,0.781525194644928,"[0.9999998807907104, 0.9972359538078308, 0.6663984060287476, 0.9900569319725037, 0.8545849919319153, 0.5557896494865417, 0.5848746299743652, 0.5471495389938354, 0.9149270057678223, 0.5031512379646301, 0.9826089143753052]"
-1942,LTDLSVTDPEK,0.9995749321850863,"[1.0, 0.9999946355819702, 0.9999974966049194, 1.0, 0.999998927116394, 0.9999998807907104, 1.0, 0.9999606609344482, 0.9999979734420776, 0.9996256828308105, 0.9957489967346191]"
-1943,DSSSLSSPLNPK,0.9767478555440903,"[0.9975674152374268, 0.9979825019836426, 0.9999995231628418, 0.9999997615814209, 1.0, 0.9999908208847046, 0.9999995231628418, 1.0, 0.9998214840888977, 0.984853982925415, 0.9995550513267517, 0.7412042021751404]"
-1944,VSPVEGLPAYTK,0.7816182027260462,"[0.9644838571548462, 0.7485742568969727, 0.999281108379364, 0.2920413613319397, 0.45343053340911865, 0.5429724454879761, 0.5579099655151367, 0.9999985694885254, 0.9996248483657837, 0.8847586512565613, 0.9637476801872253, 0.9725951552391052]"
-1945,ALEEANADLEVK,0.9994004319111506,"[1.0, 0.9999997615814209, 0.9999748468399048, 0.9999996423721313, 1.0, 0.9991903901100159, 0.9999996423721313, 0.999221682548523, 0.9999802112579346, 0.998508632183075, 0.9973178505897522, 0.9986125230789185]"
-1946,ELAQNYLTDLR,0.8751858825033362,"[0.9987032413482666, 0.9999996423721313, 0.9999998807907104, 0.9932646751403809, 0.9725463390350342, 0.49657607078552246, 0.3026726543903351, 0.999757707118988, 0.9778693318367004, 0.9927000403404236, 0.8929551243782043]"
-1947,QQLQSLQQSLER,0.9980549116929373,"[0.9996048808097839, 0.9917782545089722, 0.9999998807907104, 0.9989882111549377, 0.9999998807907104, 0.9999998807907104, 0.9997971653938293, 0.9998286962509155, 0.9997257590293884, 0.9999160766601562, 0.9871009588241577, 0.9999192953109741]"
-1948,DMGLNYLN+0.984APR,0.7437287027185614,"[0.7582211494445801, 0.38597235083580017, 0.40726277232170105, 0.3824347257614136, 0.9052098989486694, 0.8387106657028198, 0.9611366987228394, 0.8958273530006409, 0.9750264883041382, 0.8874669075012207, 0.7837467193603516]"
-1949,QEEVYSELQAR,0.9966094060377642,"[0.9800729155540466, 0.9999860525131226, 0.9999947547912598, 0.9999934434890747, 1.0, 1.0, 0.9999567270278931, 0.9999815225601196, 0.9920097589492798, 0.9946433305740356, 0.9960649609565735]"
-1950,LMADNPSWDGEK,0.9980591038862864,"[0.9999027252197266, 0.9999886751174927, 1.0, 0.9998898506164551, 0.9976274371147156, 1.0, 0.9999997615814209, 0.9994351267814636, 0.9799714088439941, 0.9999985694885254, 0.9999111890792847, 0.9999845027923584]"
-1951,KVEGSGEPFEEVVEK,0.8864504834016164,"[1.0, 0.9963100552558899, 0.9499306678771973, 0.9521885514259338, 0.7652137875556946, 0.7973751425743103, 0.7143948078155518, 0.9999786615371704, 0.9951242804527283, 0.8250417113304138, 0.3146650493144989, 0.9994309544563293, 0.998108983039856, 0.9914528131484985, 0.9975417852401733]"
-1952,QTVEADVN+0.984GLR,0.9349007118831981,"[0.9860801100730896, 0.9995893836021423, 0.9999159574508667, 0.9997583031654358, 0.9999994039535522, 0.5834299921989441, 0.9685142636299133, 0.9257258176803589, 0.9760295748710632, 0.8685547709465027, 0.9763102531433105]"
-1953,TSSVVFQWESFEDVTEVNKK,0.7922800913453102,"[0.9910835027694702, 0.6562619209289551, 0.9957590699195862, 0.5230163335800171, 0.9999904632568359, 0.5988637804985046, 0.44822070002555847, 0.9985224604606628, 0.9879915118217468, 0.8105636835098267, 0.8304610848426819, 0.6622868180274963, 0.9991140961647034, 0.8820790648460388, 0.41513216495513916, 0.6933703422546387, 0.9992875456809998, 0.974643349647522, 0.6124537587165833, 0.7665001749992371]"
-1954,VAVDPVVTEQPSTSSATK,0.9973036481274499,"[1.0, 0.999936580657959, 0.9999706745147705, 0.9999581575393677, 1.0, 0.9999967813491821, 1.0, 0.9999908208847046, 0.9999991655349731, 0.9969446063041687, 1.0, 0.9999995231628418, 0.9594481587409973, 0.9999961853027344, 0.9998140931129456, 0.9997859597206116, 0.9974108338356018, 0.9982141256332397]"
-1955,FWEENASQSSLK,0.9056361094117165,"[0.8377680778503418, 0.9935348033905029, 0.999958872795105, 0.9999126195907593, 0.9622837901115417, 0.999925971031189, 0.9999227523803711, 0.7169981002807617, 0.9998725652694702, 0.9958695769309998, 0.365998238325119, 0.995587944984436]"
-1956,LGDLYEEM+15.995DR,0.8677143573760986,"[0.9999971389770508, 0.9993497729301453, 0.9852537512779236, 0.9984856247901917, 0.9684537649154663, 0.6693544983863831, 0.7574528455734253, 0.7369836568832397, 0.783417820930481, 0.7783946990966797]"
-1957,N+0.984DLEGEVTEEC+57.021GK,0.9313062521127554,"[0.9798110127449036, 0.9999903440475464, 1.0, 0.9999614953994751, 0.9999970197677612, 0.5889744162559509, 0.9999998807907104, 0.9999961853027344, 0.9996442794799805, 0.9991821646690369, 0.990361213684082, 0.9420027732849121, 0.6070604920387268]"
-1958,LM+15.995NADLALQGDK,0.9289101113875707,"[0.8503084182739258, 0.9294140934944153, 0.9224612712860107, 0.9999370574951172, 0.9999524354934692, 0.9999046325683594, 0.9999725818634033, 0.9784370064735413, 0.9686415195465088, 0.9948345422744751, 0.7907331585884094, 0.7123246192932129]"
-1959,ALGQNPTN+0.984AEVLK,0.9576637286406297,"[1.0, 0.9997108578681946, 1.0, 0.9821780323982239, 0.9952629804611206, 0.9999736547470093, 0.9999676942825317, 0.5418271422386169, 0.9961931705474854, 0.9983110427856445, 0.9819970726966858, 0.9639434814453125, 0.9902633428573608]"
-1960,GYLSGESFTLK,0.656786865808747,"[0.957226574420929, 0.943514347076416, 0.9994640946388245, 0.9991004467010498, 0.9987331032752991, 0.5599703788757324, 0.38998714089393616, 0.35051706433296204, 0.18928728997707367, 0.2217244803905487, 0.615130603313446]"
-1961,LAQTEQGDLFK,0.9808618426322937,"[1.0, 0.9999990463256836, 0.9830296635627747, 0.9999995231628418, 0.9999971389770508, 0.956464946269989, 0.9920793175697327, 0.8713439106941223, 0.9904922842979431, 0.9984017014503479, 0.9976727366447449]"
-1962,SGGTTMYPGLADR,0.9477360707062942,"[1.0, 1.0, 1.0, 0.9999994039535522, 0.9939228892326355, 0.9999984502792358, 1.0, 0.9767215251922607, 0.9999701976776123, 0.9893635511398315, 0.9844825267791748, 0.4467930793762207, 0.9293172955513]"
-1963,LLGNYNNVTSK,0.6802856000986967,"[0.9802867770195007, 0.9746819734573364, 0.47489961981773376, 0.6623252034187317, 0.7440592050552368, 0.3853990137577057, 0.3807969391345978, 0.36274096369743347, 0.9799267649650574, 0.5392791032791138, 0.9987460374832153]"
-1964,LGAPGRPM+15.995GGPPGDRPR,0.8550366601523232,"[0.4371233284473419, 0.5947011113166809, 0.750839352607727, 0.6003491282463074, 0.8329439759254456, 0.9948247671127319, 0.9841185808181763, 0.7351276278495789, 0.9999779462814331, 0.9998530149459839, 0.939822793006897, 0.8464668989181519, 0.9771134853363037, 0.9945127964019775, 0.9263566732406616, 0.9547616243362427, 0.9667301177978516]"
-1965,SGGATEELLTEK,0.8428215781847636,"[0.9996824264526367, 0.94650799036026, 0.9974657297134399, 0.5153746008872986, 0.9879619479179382, 0.7731046080589294, 0.48254328966140747, 0.6009818315505981, 0.9990705847740173, 0.9987271428108215, 0.9980151653289795, 0.8144236207008362]"
-1966,LLQNPQDGFSR,0.9270283579826355,"[0.9999969005584717, 1.0, 0.9945889711380005, 0.9863170981407166, 0.9999998807907104, 0.9918209314346313, 0.672546923160553, 0.9997734427452087, 0.9505789279937744, 0.7715767025947571, 0.8301121592521667]"
-1967,FYEDDNALDDAR,0.95597343146801,"[0.9999921321868896, 0.9808076024055481, 0.9950785636901855, 0.9948704242706299, 0.9995429515838623, 0.9951476454734802, 0.8000555634498596, 0.9999936819076538, 0.7277742028236389, 0.9971978664398193, 0.9997019171714783, 0.9815186262130737]"
-1968,SGGGGYLQPALYDPTR,0.7549115810543299,"[0.6663837432861328, 0.7127732634544373, 0.8524069786071777, 0.8495620489120483, 0.42488211393356323, 0.983572781085968, 0.7980311512947083, 0.31316110491752625, 0.4354698061943054, 0.6959534883499146, 0.871618926525116, 0.5726408362388611, 0.9981155395507812, 0.9991864562034607, 0.9809433817863464, 0.9238836765289307]"
-1969,ELYGGVTGPDGVPLDSGR,0.9613894588417478,"[0.7769208550453186, 0.6787592768669128, 0.9954289197921753, 0.9999984502792358, 0.9564922451972961, 0.999808132648468, 0.9999967813491821, 1.0, 0.9999995231628418, 0.999281108379364, 0.9999998807907104, 0.9999550580978394, 1.0, 0.9998406171798706, 0.9780444502830505, 0.9997736811637878, 0.9426085352897644, 0.9781027436256409]"
-1970,QQAAQAADAVYYC+57.021K,0.8664519190788269,"[0.5032222867012024, 0.7667316794395447, 0.995864987373352, 0.7410180568695068, 0.8916493058204651, 0.9982398748397827, 0.6212478876113892, 0.9904060959815979, 0.9987683892250061, 0.7085214853286743, 0.9978216886520386, 0.9623832702636719, 0.996289849281311, 0.9581620097160339]"
-1971,GAEMNSVMLGGR,0.9751464227835337,"[0.9999990463256836, 0.9921426773071289, 0.8716413974761963, 0.9999949932098389, 0.9916316866874695, 0.9999996423721313, 0.9999997615814209, 0.9999991655349731, 0.9953136444091797, 0.9215151071548462, 0.9720703959465027, 0.9574495553970337]"
-1972,M+15.995EDGVGC+57.021LETAEEAK,0.9985468069712321,"[0.9999998807907104, 0.9999837875366211, 0.9999970197677612, 1.0, 0.9999151229858398, 1.0, 0.9996479749679565, 0.9999713897705078, 0.9999973773956299, 0.9999998807907104, 0.9935520887374878, 0.9998995065689087, 0.9999227523803711, 0.9853545427322388, 0.9999607801437378]"
-1973,LEASGVPPPSQPY,0.718224055491961,"[0.9975201487541199, 0.9874929189682007, 0.9162582159042358, 0.3781715929508209, 0.8583299517631531, 0.48888295888900757, 0.9034749865531921, 0.4035104513168335, 0.5579962730407715, 0.827865719795227, 0.3648451566696167, 0.995739221572876, 0.6568251252174377]"
-1974,ELLQGNMETLK,0.8219710913571444,"[0.9694344997406006, 0.9999971389770508, 0.9953852295875549, 0.9891741275787354, 0.9703142046928406, 0.5910897850990295, 0.6867284774780273, 0.4769165813922882, 0.9528447389602661, 0.47522345185279846, 0.934573769569397]"
-1975,LGALDSGNVLTPQ,0.9353655026509211,"[0.9800370931625366, 0.9186059236526489, 0.9992930889129639, 0.9996896982192993, 0.9524555802345276, 0.9999963045120239, 1.0, 0.9987877011299133, 0.9889984726905823, 0.5604050755500793, 0.9611054062843323, 0.999544084072113, 0.8008331060409546]"
-1976,DFDNLYLR,0.713284008204937,"[0.43206509947776794, 0.5740742087364197, 0.3998231589794159, 0.6378268599510193, 0.9927231669425964, 0.9966782331466675, 0.673959493637085, 0.9991218447685242]"
-1977,ATQYNEFLPK,0.638856552541256,"[0.6000866889953613, 0.6934943199157715, 0.32754090428352356, 0.5639833211898804, 0.967456579208374, 0.4671160876750946, 0.2406012862920761, 0.9032263159751892, 0.6348081231117249, 0.990251898765564]"
-1978,AAPAAC+57.021FAGPPAAPAK,0.9748106561601162,"[0.999976396560669, 0.9913161993026733, 0.8992692828178406, 1.0, 0.9999948740005493, 0.8773117065429688, 0.9996656179428101, 0.9999959468841553, 0.9961575865745544, 0.9999949932098389, 0.999841570854187, 0.9995328187942505, 0.999923586845398, 0.9949254989624023, 0.8391711115837097, 0.999893307685852]"
-1979,TPPSLNAQAC+57.021LTK,0.6519699772963157,"[0.43459123373031616, 0.9914954900741577, 0.8326556086540222, 0.6052565574645996, 0.9736654758453369, 0.5008099675178528, 0.8983782529830933, 0.4046589732170105, 0.8005087971687317, 0.23493434488773346, 0.28784170746803284, 0.7659466862678528, 0.7448666095733643]"
-1980,VKPLQPDEMLFGK,0.8909373031212733,"[0.9995664954185486, 0.9966005682945251, 1.0, 0.9999973773956299, 0.8467340469360352, 0.736318051815033, 0.9937030673027039, 0.7378894090652466, 0.3333810865879059, 0.9990919828414917, 0.9999063014984131, 0.9961250424385071, 0.9428715109825134]"
-1981,QQLQSLQQSLER,0.9993994782368342,"[0.9996819496154785, 0.996634304523468, 0.9999998807907104, 0.9993846416473389, 0.9999998807907104, 0.9999990463256836, 0.9998575448989868, 0.9998730421066284, 0.9995531439781189, 0.9999018907546997, 0.9980069994926453, 0.9999014139175415]"
-1982,GDTDLESVHEEEQEEEVSEG,0.7221903130412102,"[0.890070378780365, 0.7845816612243652, 0.9903455376625061, 0.6783239245414734, 0.9907246232032776, 0.8742279410362244, 0.8210926651954651, 0.35596564412117004, 0.717442512512207, 0.743929922580719, 0.9999911785125732, 0.39824825525283813, 0.609878420829773, 0.7168581485748291, 0.4208889603614807, 0.5336717963218689, 0.9343990683555603, 0.999902606010437, 0.3436890244483948, 0.6395739912986755]"
-1983,MQM+15.995LN+0.984GDSLPK,0.7396942648020658,"[0.43425455689430237, 0.947246253490448, 0.5845239162445068, 0.9999300241470337, 0.6674898266792297, 0.9992375373840332, 0.7454043626785278, 0.4780850410461426, 0.4511432349681854, 0.837003231048584, 0.9923189282417297]"
-1984,LMADNPSAEDGEK,0.8923735618591309,"[0.9888517260551453, 0.9999232292175293, 0.9991990923881531, 0.8152347207069397, 0.9425845742225647, 0.9998708963394165, 0.9998542070388794, 0.5627565383911133, 0.7630806565284729, 0.7871676683425903, 0.9992332458496094, 0.9855595827102661, 0.7575401663780212]"
-1985,VKEEAEPFEEVVEK,0.9378346417631421,"[0.999991774559021, 0.6619066596031189, 0.9999995231628418, 0.9999771118164062, 0.9540560245513916, 0.9304419755935669, 0.9976304769515991, 0.9522350430488586, 0.9779631495475769, 0.6654257774353027, 0.9998515844345093, 0.9984001517295837, 0.993013322353363, 0.9987924098968506]"
-1986,QQQLLNEEQVR,0.9506202881986444,"[0.9957197308540344, 0.9936269521713257, 0.9890449047088623, 0.9999998807907104, 1.0, 0.9901114106178284, 0.9523830413818359, 0.9998769760131836, 0.99842369556427, 0.5771505832672119, 0.9604859948158264]"
-1987,LVMEEAAFEEK,0.894468757239255,"[0.9352281093597412, 0.8720384836196899, 0.9999929666519165, 0.9998304843902588, 0.9999977350234985, 0.9741654396057129, 0.9968114495277405, 0.9477932453155518, 0.5301888585090637, 0.6683165431022644, 0.9147930145263672]"
-1988,VLSASPEALDSAENR,0.9072406927744547,"[1.0, 0.7521324157714844, 0.7125531435012817, 0.705890953540802, 0.9990237951278687, 1.0, 0.9999998807907104, 0.999997615814209, 0.9999967813491821, 0.9985583424568176, 0.9999268054962158, 0.6841996312141418, 0.9764083027839661, 0.7810242176055908, 0.9988985061645508]"
-1989,SLYSSSPDAFDQK,0.7734600947453425,"[0.9999970197677612, 0.8634193539619446, 0.9982095956802368, 0.9956297874450684, 0.9955434203147888, 0.9697273373603821, 0.9970620274543762, 0.6342699527740479, 0.5315596461296082, 0.6693572402000427, 0.22926238179206848, 0.33397039771080017, 0.8369730710983276]"
-1990,VEPQATLSELK,0.9315642064267938,"[1.0, 0.9998915195465088, 1.0, 0.9918214678764343, 0.9999992847442627, 0.9999575614929199, 0.9845924973487854, 0.9428247809410095, 0.6978033185005188, 0.9537404775619507, 0.6765753626823425]"
-1991,QEEKENAESSDEPAEQTSES,0.6626741081476212,"[0.7856103777885437, 0.5292672514915466, 0.9849249720573425, 0.5172070264816284, 0.37391340732574463, 0.47056591510772705, 0.45540913939476013, 0.9250954985618591, 0.7014414072036743, 0.8333070874214172, 0.5500665307044983, 0.9313268661499023, 0.6124517917633057, 0.9937695860862732, 0.2951844334602356, 0.5480759143829346, 0.8833187818527222, 0.6636770963668823, 0.4894072711467743, 0.7094618082046509]"
-1992,APNFEAELFM+15.995C+57.021GNLTK,0.7541155498474836,"[0.9719274044036865, 0.9849064946174622, 0.6202499866485596, 0.739070475101471, 0.8647935390472412, 0.9451320171356201, 0.4790459871292114, 0.9715718626976013, 0.5823625922203064, 0.5345888733863831, 0.8239060044288635, 0.9998162388801575, 0.7301549911499023, 0.9394803643226624, 0.18664655089378357, 0.6921954154968262]"
-1993,TDYNASVSVPDSSGPER,0.9414231987560496,"[1.0, 0.9996731281280518, 1.0, 0.944093644618988, 0.9999997615814209, 1.0, 1.0, 0.9999064207077026, 0.8858947157859802, 0.9999451637268066, 0.9851112961769104, 0.9998458623886108, 0.9999203681945801, 0.9963929057121277, 0.7426235675811768, 0.4634437561035156, 0.9873437881469727]"
-1994,QPGEEDC+57.021ELLDEDK,0.7816881537437439,"[0.8457072377204895, 0.5476645231246948, 0.7238706350326538, 0.857089102268219, 0.4514281749725342, 0.5070585012435913, 0.9989880919456482, 0.46741628646850586, 0.5740343928337097, 0.9999799728393555, 0.9835600852966309, 0.9962266683578491, 0.9988967180252075, 0.9917137622833252]"
-1995,QLSVEPYSQEEAER,0.9881680565220969,"[0.9919397234916687, 0.9999103546142578, 1.0, 0.9999996423721313, 0.9999998807907104, 1.0, 0.8772839307785034, 1.0, 0.9806442260742188, 0.9999996423721313, 0.9999780654907227, 0.9999479055404663, 0.9937759637832642, 0.9908734560012817]"
-1996,LGGGVLSLGGVLPH,0.864037036895752,"[1.0, 0.9680591225624084, 0.8285627365112305, 0.8464769124984741, 0.8383155465126038, 0.3733212351799011, 0.9945823550224304, 0.5228168368339539, 0.963697075843811, 0.8514998555183411, 0.9291951060295105, 0.990459680557251, 0.9998385906219482, 0.9896934628486633]"
-1997,YSQVLDAGLDNK,0.9561727792024612,"[0.9999635219573975, 0.9999979734420776, 0.9866114854812622, 0.9997640252113342, 0.9999946355819702, 0.9971508383750916, 0.6815264821052551, 0.999997615814209, 0.9996857643127441, 0.9487830996513367, 0.9272258877754211, 0.9333720207214355]"
-1998,QPC+57.021PSDC+57.021ELLDEDK,0.8995451160839626,"[0.9538198113441467, 0.9999996423721313, 0.939234733581543, 0.9505748152732849, 0.4517834782600403, 0.9996283054351807, 0.9997805953025818, 0.6686040163040161, 0.6964449286460876, 0.9999587535858154, 0.9928062558174133, 0.9816933274269104, 0.9988250136375427, 0.9604779481887817]"
-1999,TC+57.021TTVAFTQVNSEDK,0.993223782380422,"[0.9999902248382568, 0.9999934434890747, 0.9999995231628418, 1.0, 0.9999998807907104, 0.9999662637710571, 1.0, 0.9999994039535522, 0.9990191459655762, 0.9999996423721313, 0.9976142644882202, 0.9998103976249695, 0.947035551071167, 0.9988481998443604, 0.9560807943344116]"
-2000,VDSLTTDHYENEELLVK,0.7741561374243568,"[0.8193013668060303, 0.7266504168510437, 0.8604966998100281, 0.5302731990814209, 0.42507532238960266, 0.6794916391372681, 0.9998972415924072, 0.5580546855926514, 0.4407762289047241, 0.9833135008811951, 0.5561317205429077, 0.6560041308403015, 0.9945840239524841, 0.9963994026184082, 0.9866031408309937, 0.9763389825820923, 0.9712626338005066]"
-2001,ASTQLVGTSTDELR,0.7753057735306876,"[0.8996933102607727, 0.49450448155403137, 0.7645885944366455, 0.864765465259552, 0.9944742321968079, 0.9996694326400757, 0.9384902715682983, 0.4251224100589752, 0.7348814010620117, 0.8176982998847961, 0.7833619713783264, 0.9031661152839661, 0.5532267689704895, 0.6806380748748779]"
-2002,LSQVSDSVSGQTVVDPK,0.9777401440283832,"[1.0, 0.9836116433143616, 0.9407040476799011, 0.9999980926513672, 0.9999256134033203, 0.9984726309776306, 0.9999665021896362, 0.9999926090240479, 0.9999998807907104, 0.9801921844482422, 0.7340916991233826, 0.9999938011169434, 0.9993906021118164, 0.9888429641723633, 0.9999071359634399, 0.9970587491989136, 0.9994342923164368]"
-2003,AQQALAGLPQSPVAVSQSK,0.8806535554559607,"[0.8567717671394348, 0.9613040089607239, 0.9944103360176086, 0.6326723098754883, 0.9619430303573608, 0.8287642002105713, 0.9546376466751099, 0.8703418374061584, 1.0, 0.9824821949005127, 0.9999676942825317, 1.0, 0.9999997615814209, 0.9999979734420776, 0.9959352016448975, 0.9999215602874756, 0.6494107842445374, 0.3460570275783539, 0.6978002190589905]"
-2004,DQTPDENLDLLEK,0.7286988886503073,"[0.999708354473114, 0.9538770914077759, 0.956709623336792, 0.9999655485153198, 0.8914315700531006, 0.9802220463752747, 0.2915281355381012, 0.5216348767280579, 0.5299387574195862, 0.6724169850349426, 0.5375924706459045, 0.5090249180793762, 0.6290351748466492]"
-2005,QAQQLVQPGASPVAVSQSK,0.9836863028375726,"[0.9970346689224243, 1.0, 0.9997443556785583, 0.9991305470466614, 0.999997615814209, 0.9998728036880493, 0.8774400949478149, 0.9999996423721313, 0.9090834856033325, 0.936187744140625, 0.9999938011169434, 0.9999858140945435, 0.9987205266952515, 0.9999572038650513, 0.9999446868896484, 0.9999982118606567, 0.9801764488220215, 0.9939597845077515, 0.9988123178482056]"
-2006,LVPDSQPATPAVNVSR,0.8754662107676268,"[1.0, 0.7113745212554932, 1.0, 0.9932659864425659, 0.9659032821655273, 0.4383225739002228, 1.0, 0.9999996423721313, 0.999988317489624, 1.0, 0.9999750852584839, 0.8079113960266113, 0.8882278800010681, 0.9130057096481323, 0.29409098625183105, 0.9953939914703369]"
-2007,EGSGSSTAVPEASPLEEPAE,0.7940960913896561,"[0.6281782984733582, 0.3166089653968811, 0.937822163105011, 0.8749154806137085, 0.9972980618476868, 0.8511685132980347, 0.9988774657249451, 0.9965919256210327, 0.9830499291419983, 0.5253578424453735, 0.8811994194984436, 0.4830535352230072, 0.29942265152931213, 0.8767784833908081, 0.9666818380355835, 0.9818520545959473, 0.551081120967865, 0.8625419735908508, 0.9997901320457458, 0.8696519732475281]"
-2008,GGSDDNVFVGAPTGSGK,0.9390337291885825,"[0.9994327425956726, 0.7060438990592957, 0.9837199449539185, 0.9974421262741089, 0.8385475873947144, 0.9913246631622314, 0.999991774559021, 0.9999998807907104, 0.9999995231628418, 0.9999995231628418, 0.9999985694885254, 0.9999994039535522, 0.9999957084655762, 0.9999295473098755, 0.9960219264030457, 0.4522593021392822, 0.9988672733306885]"
-2009,ELQAPAPTDLR,0.9952886592258107,"[0.9999040365219116, 1.0, 0.9994175434112549, 1.0, 1.0, 0.9999994039535522, 0.9999996423721313, 0.9993574023246765, 0.9728005528450012, 0.9978310465812683, 0.9788656234741211]"
-2010,QFDAGTVNYEQPTK,0.9593270250729152,"[0.9509633183479309, 0.9999661445617676, 0.9996548891067505, 0.9999994039535522, 1.0, 0.9999991655349731, 0.9999998807907104, 0.9970064759254456, 0.9999960660934448, 0.4911077618598938, 0.9995317459106445, 0.9999889135360718, 0.9996657371520996, 0.9926988482475281]"
-2011,QFEGDLDPLR,0.6058314859867096,"[0.3905762732028961, 0.260535329580307, 0.44122228026390076, 0.21645718812942505, 0.4298148453235626, 0.929408609867096, 0.9844565987586975, 0.8683175444602966, 0.8178924918174744, 0.7196336984634399]"
-2012,ATLLDVMPH,0.8328503519296646,"[0.9999083280563354, 0.8556089401245117, 0.987636148929596, 0.8262211084365845, 0.6393559575080872, 0.9837858080863953, 0.22927846014499664, 0.9999185800552368, 0.973939836025238]"
-2013,QGEEEDAELLVK,0.9930707265933355,"[0.9443367123603821, 1.0, 0.999942421913147, 0.9864335060119629, 0.999885082244873, 0.9883571267127991, 0.9999672174453735, 0.9994469285011292, 0.9999082088470459, 0.9997404217720032, 0.9993945360183716, 0.9994365572929382]"
-2014,QQLQVNVMSLER,0.6322115336855253,"[0.945867121219635, 0.5615664720535278, 0.5208469033241272, 0.9534516930580139, 0.47224166989326477, 0.4566671848297119, 0.7082166075706482, 0.2735939919948578, 0.9395177364349365, 0.6138514876365662, 0.3227710425853729, 0.8179464936256409]"
-2015,TAAVTDFC+57.021LQPGR,0.9223937300535349,"[0.9999263286590576, 0.9827447533607483, 0.6451939344406128, 0.9971516728401184, 0.999992847442627, 0.9741033911705017, 0.9334223866462708, 0.8603900671005249, 0.9265446066856384, 0.9594328999519348, 0.9178791642189026, 0.8967967629432678, 0.8975396752357483]"
-2016,NLSKDDAFDC+57.021GNVAK,0.7578348338603973,"[0.947309136390686, 0.9548977613449097, 0.753609299659729, 0.7246333360671997, 0.36321786046028137, 0.6991804242134094, 0.9294074773788452, 0.9177442193031311, 0.7771680355072021, 0.5637930631637573, 0.9948739409446716, 0.8389361500740051, 0.4984039068222046, 0.6833385229110718, 0.721009373664856]"
-2017,TYADYDSVNEFC+57.021EGVC+57.021K,0.9234321923816905,"[0.9999992847442627, 0.9999998807907104, 0.8353824615478516, 0.9965019226074219, 0.9994332194328308, 0.584863543510437, 0.692072331905365, 0.9989351630210876, 0.776846706867218, 0.9999943971633911, 0.8476207852363586, 0.967322051525116, 0.9999912977218628, 1.0, 0.9999898672103882, 0.9996317625045776, 0.9997625946998596]"
-2018,NSGGEPNVYTLSR,0.8184422758909372,"[0.989991307258606, 0.9987673759460449, 0.9962337613105774, 0.9996614456176758, 0.9751067161560059, 0.29325634241104126, 0.9541110396385193, 0.814723014831543, 0.8515902757644653, 0.47204363346099854, 0.5035446882247925, 0.8697478175163269, 0.9209721684455872]"
-2019,SLYSSSPGQYVTR,0.9987274133242093,"[1.0, 0.9998241066932678, 1.0, 1.0, 1.0, 0.9999644756317139, 1.0, 0.9999409914016724, 0.9967892169952393, 0.9982613921165466, 0.999011754989624, 0.9900083541870117, 0.999656081199646]"
-2020,C+57.021LVTTTHFR,0.6919383042388492,"[0.9999905824661255, 0.8069636225700378, 0.9433819651603699, 0.5751990675926208, 0.7564690709114075, 0.3116419315338135, 0.6100668907165527, 0.303873211145401, 0.9198583960533142]"
-2021,VTLVFHQGQPTN+0.984DLTQ,0.8874829802662134,"[0.6806351542472839, 0.3724583089351654, 0.9693232774734497, 0.9992744326591492, 0.9999814033508301, 0.9999946355819702, 0.9768142104148865, 0.999998927116394, 0.9482200741767883, 0.9927305579185486, 0.9999998807907104, 0.6153052449226379, 0.9999921321868896, 0.9997701048851013, 0.9999638795852661, 0.6452654600143433]"
-2022,GVSSMEEGSFEEAR,0.7904168516397476,"[0.9839127659797668, 0.8434303402900696, 0.6497370600700378, 0.7996155619621277, 0.9795607924461365, 0.9983934760093689, 0.9999754428863525, 0.9772034883499146, 0.6353245377540588, 0.41267940402030945, 0.5500165820121765, 0.9169014096260071, 0.6856595277786255, 0.6334255337715149]"
-2023,GGQGGENSEGSFWNLVGYNNK,0.776491356747491,"[0.9691137671470642, 0.8676490187644958, 0.3312798738479614, 0.6707940101623535, 0.4755704402923584, 0.42353641986846924, 0.9383444786071777, 0.4142409563064575, 0.7561613917350769, 0.832767128944397, 0.9938620328903198, 0.833021342754364, 0.27833589911460876, 0.9929227828979492, 0.9998939037322998, 0.9999747276306152, 0.9999995231628418, 0.9970605969429016, 0.9868648648262024, 0.9168712496757507, 0.6280540823936462]"
-2024,NYGYNLEELTFGQTK,0.8031669278939565,"[0.9618719220161438, 0.9936102032661438, 0.34429189562797546, 0.42562925815582275, 0.88441002368927, 0.6020562052726746, 0.9905791878700256, 0.9760688543319702, 0.9999043941497803, 0.9999876022338867, 0.5104979872703552, 0.999991774559021, 0.771160900592804, 0.740600049495697, 0.8468436598777771]"
-2025,VDFSLTK,0.9931340558188302,"[0.9999284744262695, 0.9634172320365906, 0.9999991655349731, 0.9999580383300781, 0.995703399181366, 0.9934176206588745, 0.9995144605636597]"
-2026,FGPALSVK,0.9942496120929718,"[0.9999992847442627, 0.9999998807907104, 0.9999412298202515, 0.9998058676719666, 0.9993546605110168, 0.999829888343811, 0.9554523825645447, 0.9996137022972107]"
-2027,TLQVLLGEGER,0.657854753461751,"[0.8552834391593933, 0.640319287776947, 0.9219539165496826, 0.22832973301410675, 0.49361518025398254, 0.4245922565460205, 0.32347193360328674, 0.9803979992866516, 0.9440786242485046, 0.5951855182647705, 0.8291743993759155]"
-2028,DVNLASSNSSSPSK,0.7344621824366706,"[0.9989724159240723, 0.9693000912666321, 0.8962954878807068, 0.8969941139221191, 0.6299808025360107, 0.79195237159729, 0.8856549859046936, 0.6609413623809814, 0.3420694172382355, 0.22521021962165833, 0.7411502599716187, 0.9770262837409973, 0.29633232951164246, 0.9705904126167297]"
-2029,ETLFSDDVEEFAPPK,0.7322338322798411,"[0.65301513671875, 0.4082372784614563, 0.3523334860801697, 0.6723242402076721, 0.79911208152771, 0.5541802644729614, 0.8082146048545837, 0.4675864279270172, 0.9923892617225647, 0.7949267029762268, 0.4969080090522766, 0.989213764667511, 0.9999901056289673, 0.998902440071106, 0.9961736798286438]"
-2030,QLSVEGGEREEFDR,0.7762274752770152,"[0.9703344106674194, 0.9999949932098389, 0.9999994039535522, 0.9999557733535767, 0.9999904632568359, 0.999950647354126, 0.9950048327445984, 0.9789093732833862, 0.6862707734107971, 0.6361998319625854, 0.40817806124687195, 0.2301388531923294, 0.2753547132015228, 0.6869025230407715]"
-2031,QVVSGVC+57.021K,0.7429332658648491,"[0.3753509819507599, 0.462177574634552, 0.8896723985671997, 0.9898862838745117, 0.48889848589897156, 0.8532377481460571, 0.9885796904563904, 0.8956629633903503]"
-2032,EELSEEGYLAK,0.9416959231550043,"[0.9999589920043945, 0.9985113739967346, 0.7754645347595215, 0.9998584985733032, 0.9855377078056335, 0.9999871253967285, 0.9997449517250061, 0.9685637950897217, 0.9598117470741272, 0.9809907674789429, 0.6902256608009338]"
-2033,VVNVETTANQEQAEDER,0.680659565855475,"[0.5190577507019043, 0.5553570985794067, 0.9899619817733765, 0.4748431146144867, 0.6960101127624512, 0.310260146856308, 0.9549157023429871, 0.4577963948249817, 0.7146207094192505, 0.571873664855957, 0.9700174331665039, 0.9462541341781616, 0.7409260272979736, 0.6748621463775635, 0.8499749302864075, 0.844878077507019, 0.2996031939983368]"
-2034,GAAVQAALLSGDK,0.9997219810119042,"[0.9999997615814209, 0.9999997615814209, 0.9999980926513672, 0.9999996423721313, 0.9982666969299316, 0.9999982118606567, 0.9999949932098389, 0.999995231628418, 0.999998927116394, 0.9999984502792358, 0.9999986886978149, 0.9994638562202454, 0.9986734390258789]"
-2035,ELNNVC+57.021EPVVTQPK,0.9842278616768974,"[0.9999959468841553, 0.9952734112739563, 0.9987140893936157, 0.9958179593086243, 0.9999996423721313, 0.9999845027923584, 0.9998016953468323, 1.0, 0.999998927116394, 0.9999327659606934, 0.9999877214431763, 0.8225147724151611, 0.9699362516403198, 0.9972323775291443]"
-2036,YPLVVC+57.021EQR,0.8026214407549964,"[0.6798282265663147, 0.8126178979873657, 0.9776234030723572, 0.9952518939971924, 0.9986622333526611, 0.9935552477836609, 0.5009790658950806, 0.34592917561531067, 0.9191458225250244]"
-2037,VQMLLSDR,0.9950085803866386,"[0.9999943971633911, 0.979278564453125, 0.9997276663780212, 0.9999895095825195, 0.9998995065689087, 0.999931812286377, 0.9853698015213013, 0.9958773851394653]"
-2038,QAAQLAGLPQSPVAVSQSK,0.8937568727292513,"[0.9300807118415833, 0.6607072353363037, 0.5163924694061279, 0.4563586711883545, 0.6386075615882874, 0.8952167630195618, 0.9496950507164001, 0.9752545356750488, 1.0, 0.9917621612548828, 0.9999967813491821, 1.0, 0.9999994039535522, 0.9999972581863403, 0.9998072981834412, 0.9999983310699463, 0.9985309839248657, 0.9955623149871826, 0.9734130501747131]"
-2039,LSQVSDSVSGQTVVDPK,0.9597321924041299,"[0.9999995231628418, 0.9999518394470215, 0.9785816073417664, 0.9997425675392151, 0.9999569654464722, 0.9957249164581299, 0.9999998807907104, 0.9999569654464722, 0.9999996423721313, 0.9999568462371826, 0.709778368473053, 0.999975323677063, 0.9999104738235474, 0.6337832808494568, 0.9999406337738037, 0.9987900853157043, 0.9993983507156372]"
-2040,ESC+57.021LMLSGESGAGK,0.9903993351118905,"[0.9978452920913696, 0.9260517954826355, 1.0, 1.0, 0.999991774559021, 1.0, 1.0, 1.0, 0.9938795566558838, 0.9999765157699585, 0.9999686479568481, 0.9806057810783386, 0.9884713888168335, 0.9787999391555786]"
-2041,QAQQLVQPQSPVAVSQSK,0.9516377846399943,"[0.9986791014671326, 0.9999997615814209, 0.9978945851325989, 0.997641921043396, 1.0, 0.9999336004257202, 0.9874765276908875, 0.9999997615814209, 0.8452211618423462, 0.999994158744812, 0.9999585151672363, 0.9977871179580688, 0.9999216794967651, 0.9998552799224854, 0.999998927116394, 0.7692112326622009, 0.5405315160751343, 0.9953752756118774]"
-2042,TYNC+57.021EFDDLEVK,0.8614370549718539,"[0.9999997615814209, 0.9998792409896851, 0.7359717488288879, 0.9832733869552612, 0.9935923218727112, 0.9999961853027344, 0.951374351978302, 0.7698615193367004, 0.6149339079856873, 0.35884222388267517, 0.9826504588127136, 0.9468695521354675]"
-2043,C+57.021LVQTTSWSQC+57.021SK,0.9825666638521048,"[1.0, 0.9999934434890747, 1.0, 0.9860700964927673, 0.9999997615814209, 0.9999998807907104, 0.9998188614845276, 0.990588903427124, 0.9999998807907104, 0.9961353540420532, 0.9537605047225952, 0.9303814768791199, 0.9166184663772583]"
-2044,VVEAYTPNDEVLVLVGYNNK,0.7431120902299881,"[0.8133500814437866, 0.7912126183509827, 0.5598524212837219, 0.39385026693344116, 0.6964821815490723, 0.5242641568183899, 0.4815507233142853, 0.7848536372184753, 0.8042800426483154, 0.3533670902252197, 0.48739132285118103, 0.641767680644989, 0.9700302481651306, 0.999897837638855, 0.9999688863754272, 0.9999995231628418, 0.9999226331710815, 0.9822549819946289, 0.6242443919181824, 0.9537010788917542]"
-2045,LLLDGDGAGDDR,0.9809706807136536,"[0.9999995231628418, 0.9999997615814209, 0.9999998807907104, 0.9999439716339111, 0.9999998807907104, 0.958479106426239, 0.998145341873169, 0.8762876391410828, 0.9999774694442749, 0.9811875224113464, 0.9947986602783203, 0.9628294110298157]"
-2046,EEAESTLQSFR,0.9964545856822621,"[0.9999479055404663, 0.9966024160385132, 0.9973141551017761, 0.9999885559082031, 0.99962317943573, 0.999923586845398, 0.9996535778045654, 0.9967467784881592, 0.9990594983100891, 0.9727284908294678, 0.9994122982025146]"
-2047,LLALGSGPNR,0.8516732960939407,"[0.9999507665634155, 0.9999912977218628, 0.9999823570251465, 0.9999864101409912, 0.9995914101600647, 0.9917094111442566, 0.9997405409812927, 0.7843351364135742, 0.2872640788555145, 0.4541815519332886]"
diff --git a/setup.cfg b/setup.cfg
index 49269bc6..bca0a29b 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,7 +1,7 @@
 [metadata]
-name = casanovo
+name = Casanovo
 author = Melih Yilmaz
-author_email = melih@cs.washington.edu 
+author_email = melih@cs.washington.edu
 description = De Novo Mass Spectrometry Peptide Sequencing with a Transformer Model
 long_description = file: README.md
 long_description_content_type = text/markdown
@@ -21,27 +21,30 @@ classifiers =
 packages = find:
 python_requires = >=3.7
 install_requires =
+    click
     depthcharge-ms @ git+https://github.com/wfondrie/depthcharge@75fad7df9869572290ec79ec6a67d5d329b7a6c7
     numpy
     pandas
+    pytorch-lightning
     scikit-learn
-    torch>=1.9 
-    pytorch-lightning==1.5.10
     spectrum_utils
-    click
-    pytest
+    torch>=1.9
 
 [options.extras_require]
 docs =
+    ipykernel>=5.3.0
+    nbsphinx>=0.7.1
     numpydoc>=1.0.0
-    sphinx-argparse>=0.2.5
     pydata-sphinx-theme>=0.4.3
-    nbsphinx>=0.7.1
-    ipykernel>=5.3.0
     recommonmark>=0.5.0
+    sphinx-argparse>=0.2.5
 dev =
-    pre-commit>=2.7.1
     black>=19.10b0
+    pre-commit>=2.7.1
+    ppx
+    pyteomics
+    pytest
+    requests
 
 [options.entry_points]
 console_scripts =
diff --git a/setup.py b/setup.py
index 10d9469e..c00a9928 100644
--- a/setup.py
+++ b/setup.py
@@ -1,4 +1,4 @@
-"""Setup ppx"""
+"""Setup Casanovo."""
 import setuptools
 
 setuptools.setup()


Test patch:
diff --git a/casanovo/denovo/train_test.py b/casanovo/denovo/train_test.py
deleted file mode 100644
index a9deac96..00000000
--- a/casanovo/denovo/train_test.py
+++ /dev/null
@@ -1,295 +0,0 @@
-"""Training and testing functionality for the de novo peptide sequencing model"""
-import pytest
-import logging, importlib, os
-from pathlib import Path
-import pytorch_lightning as pl
-from depthcharge.data import AnnotatedSpectrumIndex, SpectrumIndex
-from casanovo.denovo import DeNovoDataModule, Spec2Pep
-import yaml
-
-
-def train(train_data_path, val_data_path, model_path, config):
-    """Train a Casanovo model with options specified in config.py."""
-
-    # Set random seed across PyTorch, numpy and python.random
-    pl.utilities.seed.seed_everything(seed=config["random_seed"], workers=True)
-
-    # Index training and validation data
-    train_data_path = Path(train_data_path)
-    if train_data_path.is_file():
-        raise FileNotFoundError(
-            f"train_data_path expects directory path but file path was provided instead"
-        )
-    train_mgf_files = [
-        train_data_path / f
-        for f in os.listdir(train_data_path)
-        if (train_data_path / f).suffix.lower() == ".mgf"
-    ]
-    val_data_path = Path(val_data_path)
-    if val_data_path.is_file():
-        raise FileNotFoundError(
-            f"val_data_path expects directory path but file path was provided instead"
-        )
-    val_mgf_files = [
-        val_data_path / f
-        for f in os.listdir(val_data_path)
-        if (val_data_path / f).suffix.lower() == ".mgf"
-    ]
-
-    train_index = AnnotatedSpectrumIndex(
-        os.path.join(os.getcwd(), config["train_annot_spec_idx_path"]),
-        train_mgf_files,
-        overwrite=config["train_spec_idx_overwrite"],
-    )
-    val_index = AnnotatedSpectrumIndex(
-        os.path.join(os.getcwd(), config["val_annot_spec_idx_path"]),
-        val_mgf_files,
-        overwrite=config["val_spec_idx_overwrite"],
-    )
-
-    # Initialize data loaders
-    train_loader = DeNovoDataModule(
-        train_index=train_index,
-        n_peaks=config["n_peaks"],
-        min_mz=config["min_mz"],
-        max_mz=config["max_mz"],
-        min_intensity=config["min_intensity"],
-        fragment_tol_mass=config["fragment_tol_mass"],
-        preprocess_spec=config["preprocess_spec"],
-        num_workers=config["num_workers"],
-        batch_size=config["train_batch_size"],
-    )
-
-    val_loader = DeNovoDataModule(
-        valid_index=val_index,
-        n_peaks=config["n_peaks"],
-        min_mz=config["min_mz"],
-        max_mz=config["max_mz"],
-        min_intensity=config["min_intensity"],
-        fragment_tol_mass=config["fragment_tol_mass"],
-        preprocess_spec=config["preprocess_spec"],
-        num_workers=config["num_workers"],
-        batch_size=config["val_batch_size"],
-    )
-
-    train_loader.setup()
-    val_loader.setup()
-
-    # Initialize the model
-    if config["train_from_scratch"] == True:
-        model = Spec2Pep(
-            dim_model=config["dim_model"],
-            n_head=config["n_head"],
-            dim_feedforward=config["dim_feedforward"],
-            n_layers=config["n_layers"],
-            dropout=config["dropout"],
-            dim_intensity=config["dim_intensity"],
-            custom_encoder=config["custom_encoder"],
-            max_length=config["max_length"],
-            residues=config["residues"],
-            max_charge=config["max_charge"],
-            n_log=config["n_log"],
-            tb_summarywriter=config["tb_summarywriter"],
-            warmup_iters=config["warmup_iters"],
-            max_iters=config["max_iters"],
-            lr=config["learning_rate"],
-            weight_decay=config["weight_decay"],
-        )
-
-    else:
-        model_path = Path(model_path)
-        if model_path.is_dir():
-            raise FileNotFoundError(
-                f"model_path expects file path but directory path was provided instead"
-            )
-        model = Spec2Pep().load_from_checkpoint(
-            model_path,
-            dim_model=config["dim_model"],
-            n_head=config["n_head"],
-            dim_feedforward=config["dim_feedforward"],
-            n_layers=config["n_layers"],
-            dropout=config["dropout"],
-            dim_intensity=config["dim_intensity"],
-            custom_encoder=config["custom_encoder"],
-            max_length=config["max_length"],
-            residues=config["residues"],
-            max_charge=config["max_charge"],
-            n_log=config["n_log"],
-            tb_summarywriter=config["tb_summarywriter"],
-            warmup_iters=config["warmup_iters"],
-            max_iters=config["max_iters"],
-            lr=config["learning_rate"],
-            weight_decay=config["weight_decay"],
-        )
-
-    # Create Trainer object and checkpoint callback to save model
-    if config["save_model"] == True:
-
-        checkpoint_callback = pl.callbacks.ModelCheckpoint(
-            dirpath=config["model_save_folder_path"],
-            save_weights_only=config["save_weights_only"],
-            filename="{epoch}",
-            every_n_epochs=config["every_n_epochs"],
-            save_top_k=-1,
-        )
-
-        trainer = pl.Trainer(
-            accelerator=config["accelerator"],
-            logger=config["logger"],
-            gpus=config["gpus"],
-            max_epochs=config["max_epochs"],
-            num_sanity_val_steps=config["num_sanity_val_steps"],
-            callbacks=[checkpoint_callback],
-        )
-
-    else:
-
-        trainer = pl.Trainer(
-            accelerator=config["accelerator"],
-            logger=config["logger"],
-            gpus=config["gpus"],
-            max_epochs=config["max_epochs"],
-            num_sanity_val_steps=config["num_sanity_val_steps"],
-        )
-
-    # Train the model
-    trainer.fit(
-        model, train_loader.train_dataloader(), val_loader.val_dataloader()
-    )
-
-
-def evaluate(test_data_path, model_path, config):
-    """Run inference a pre-trained Casanovo model with evaluation and using options specified in config.py."""
-
-    # Initialize the pre-trained model
-    model_path = Path(model_path)
-    if model_path.is_dir():
-        raise FileNotFoundError(
-            f"model_path expects file path but directory path was provided instead"
-        )
-    model_trained = Spec2Pep().load_from_checkpoint(
-        model_path,
-        dim_model=config["dim_model"],
-        n_head=config["n_head"],
-        dim_feedforward=config["dim_feedforward"],
-        n_layers=config["n_layers"],
-        dropout=config["dropout"],
-        dim_intensity=config["dim_intensity"],
-        custom_encoder=config["custom_encoder"],
-        max_length=config["max_length"],
-        residues=config["residues"],
-        max_charge=config["max_charge"],
-        n_log=config["n_log"],
-    )
-    # Index test data
-    test_data_path = Path(test_data_path)
-    if test_data_path.is_file():
-        raise FileNotFoundError(
-            f"test_data_path expects directory path but file path was provided instead"
-        )
-    mgf_files = [
-        test_data_path / f
-        for f in os.listdir(test_data_path)
-        if (test_data_path / f).suffix.lower() == ".mgf"
-    ]
-    index = AnnotatedSpectrumIndex(
-        os.path.join(os.getcwd(), config["test_annot_spec_idx_path"]),
-        mgf_files,
-        overwrite=config["test_spec_idx_overwrite"],
-    )
-
-    # Initialize the data loader
-    loaders = DeNovoDataModule(
-        test_index=index,
-        n_peaks=config["n_peaks"],
-        min_mz=config["min_mz"],
-        max_mz=config["max_mz"],
-        min_intensity=config["min_intensity"],
-        fragment_tol_mass=config["fragment_tol_mass"],
-        preprocess_spec=config["preprocess_spec"],
-        num_workers=config["num_workers"],
-        batch_size=config["test_batch_size"],
-    )
-
-    loaders.setup(stage="test", annotated=True)
-
-    # Create Trainer object
-    trainer = pl.Trainer(
-        accelerator=config["accelerator"],
-        logger=config["logger"],
-        gpus=config["gpus"],
-        max_epochs=config["max_epochs"],
-        num_sanity_val_steps=config["num_sanity_val_steps"],
-    )
-
-    # Run test
-    trainer.validate(model_trained, loaders.test_dataloader())
-
-
-def denovo(test_data_path, model_path, config, output_path):
-    """Run inference with a pre-trained Casanovo model without evaluation and using options specified in config.py."""
-
-    # Initialize the pre-trained model
-    model_path = Path(model_path)
-    if model_path.is_dir():
-        raise FileNotFoundError(
-            f"model_path expects file path but directory path was provided instead"
-        )
-    model_trained = Spec2Pep().load_from_checkpoint(
-        model_path,
-        dim_model=config["dim_model"],
-        n_head=config["n_head"],
-        dim_feedforward=config["dim_feedforward"],
-        n_layers=config["n_layers"],
-        dropout=config["dropout"],
-        dim_intensity=config["dim_intensity"],
-        custom_encoder=config["custom_encoder"],
-        max_length=config["max_length"],
-        residues=config["residues"],
-        max_charge=config["max_charge"],
-        n_log=config["n_log"],
-        output_path=output_path,
-    )
-    # Index test data
-    test_data_path = Path(test_data_path)
-    if test_data_path.is_file():
-        raise FileNotFoundError(
-            f"test_data_path expects directory path but file path was provided instead"
-        )
-    mgf_files = [
-        test_data_path / f
-        for f in os.listdir(test_data_path)
-        if (test_data_path / f).suffix.lower() == ".mgf"
-    ]
-    index = SpectrumIndex(
-        os.path.join(os.getcwd(), config["test_annot_spec_idx_path"]),
-        mgf_files,
-        overwrite=config["test_spec_idx_overwrite"],
-    )
-
-    # Initialize the data loader
-    loaders = DeNovoDataModule(
-        test_index=index,
-        n_peaks=config["n_peaks"],
-        min_mz=config["min_mz"],
-        max_mz=config["max_mz"],
-        min_intensity=config["min_intensity"],
-        fragment_tol_mass=config["fragment_tol_mass"],
-        preprocess_spec=config["preprocess_spec"],
-        num_workers=config["num_workers"],
-        batch_size=config["test_batch_size"],
-    )
-
-    loaders.setup(stage="test", annotated=False)
-
-    # Create Trainer object
-    trainer = pl.Trainer(
-        accelerator=config["accelerator"],
-        logger=config["logger"],
-        gpus=config["gpus"],
-        max_epochs=config["max_epochs"],
-        num_sanity_val_steps=config["num_sanity_val_steps"],
-    )
-
-    # Run model without evaluation
-    trainer.test(model_trained, loaders.test_dataloader())
diff --git a/tests/system_tests/test_program.py b/tests/system_tests/test_program.py
index 937f30c6..bdc5b77c 100644
--- a/tests/system_tests/test_program.py
+++ b/tests/system_tests/test_program.py
@@ -18,7 +18,6 @@ def test_denovo_casanovo(mgf_small):
         config = yaml.safe_load(f)
 
     """Ensure that the test doesn't raise any system overdraw issues in terms of resources"""
-    config["gpus"] = []
     config["num_workers"] = 0
     output_path = os.path.dirname(os.path.abspath(__file__))
 
@@ -40,7 +39,7 @@ def test_denovo_casanovo(mgf_small):
         max_iters=config["max_iters"],
         lr=config["learning_rate"],
         weight_decay=config["weight_decay"],
-        output_path=output_path,
+        out_filename=output_path,
     )
     from pathlib import Path
 
@@ -62,21 +61,22 @@ def test_denovo_casanovo(mgf_small):
         min_mz=config["min_mz"],
         max_mz=config["max_mz"],
         min_intensity=config["min_intensity"],
-        fragment_tol_mass=config["fragment_tol_mass"],
-        preprocess_spec=config["preprocess_spec"],
-        num_workers=config["num_workers"],
-        batch_size=config["test_batch_size"],
+        remove_precursor_tol=config["remove_precursor_tol"],
+        n_workers=config["num_workers"],
+        batch_size=config["predict_batch_size"],
     )
 
     loaders.setup(stage="test", annotated=False)
 
     # Create Trainer object
     trainer = pl.Trainer(
-        accelerator=config["accelerator"],
+        accelerator="auto",
+        auto_select_gpus=True,
+        devices=-1,
         logger=config["logger"],
-        gpus=config["gpus"],
         max_epochs=config["max_epochs"],
         num_sanity_val_steps=config["num_sanity_val_steps"],
+        strategy=config["strategy"],
     )
 
     trainer.test(tabula_rasa_model, loaders.test_dataloader())