Datasets:
language: en
license: mit
source_datasets: https://doi.org/10.1016/j.cell.2024.03.027
task_categories:
- tabular-classification
tags:
- drug_discovery
- cysteine
- chemistry
- biology
pretty_name: Cysteine Structure Database
dataset_summary: >-
structural data regarding ligandabale and non ligandable cysteins in ~6000
proteins along with probe interaction read outs. PROBE indicates one of three
probes KB02, KB03, or KB05
citation: |-
@article{
Takahashi_et_al_2024,
author={Takahashi, Chong, Harrison, Bar-Peled, et al},
doi={10.1016/j.cell.2024.03.027},
journal={Cell},
number={10},
month={May}
title={DrugMap: A quantitative pan-cancer analysis of Cysteine ligandability},
volume={187},
year={2024}
url = {https://www.biorxiv.org/content/10.1101/2023.10.20.563287v1}
}
size_categories:
- 1K<n<10K
config_names:
- KB02
- KB03
- KB05
configs:
- config_name: KB02
data_files:
- split: test
path: KB02_data/structure.test_KB02.csv
- split: train
path: KB02_data/structure.train_KB02.csv
- split: validation
path: KB02_data/structure.validation_KB02.csv
- config_name: KB03
data_files:
- split: test
path: KB03_data/structure.test_KB03.csv
- split: train
path: KB03_data/structure.train_KB03.csv
- split: validation
path: KB03_data/structure.validation_KB03.csv
- config_name: KB05
data_files:
- split: test
path: KB05_data/structure.test_KB05.csv
- split: train
path: KB05_data/structure.train_KB05.csv
- split: validation
path: KB05_data/structure.validation_KB05.csv
dataset_info:
- config_name: KB02
features:
- name: uniprot_accession
dtype: string
description: Uniprot ID of Protein
- name: pdb_id
dtype: string
description: PDB Structure ID of Protein
- name: gene_names
dtype: string
description: Gene Names Associated with Uniprot ID of Protein
- name: entry_name
dtype: string
description: Entry Name of Protein Associated with Uniprot ID
- name: protein_names
dtype: string
description: Protein Names Associated with Uniprot ID of Protein
- name: depth
dtype: float64
description: Depth of a Cystein in Protein Pocket (Å)
- name: absolute_sasa
dtype: float64
description: Absolute Solvent Accessible Surface Area (Å^2)
- name: hse_up
dtype: int64
description: Half-Sphere Exposure (up)
- name: hse_down
dtype: int64
description: Half-Sphere Exposure (down)
- name: coord_number
dtype: int64
description: coordination number
- name: rsa
dtype: float64
description: Relative Solvent Accessible Surface Area (Å^2)
- name: h_nho1
dtype: float64
description: >-
Estimated h_nho1 energy (from database of secondary structure
assignments in proteins-- DSSP)
- name: h_ohn1
dtype: float64
description: Estimated h_ohn1 energy (DSSP)
- name: h_nho2
dtype: float64
description: Estimated h_nho2 energy (DSSP)
- name: h_ohn2
dtype: float64
description: Estimated h_ohn2 energy (DSSP)
- name: tco
dtype: float64
description: TCO (DSSP)
- name: kappa
dtype: float64
description: Kappa (DSSP)
- name: alpha
dtype: float64
description: Alpha (DSSP)
- name: phi
dtype: float64
description: Phi (DSSP)
- name: psi
dtype: float64
description: Psi (DSSP)
- name: pocket
dtype: float64
description: Pocket Volume (Å^3)
- name: interface
dtype: bool
description: Cysteine Presence in Protein Interface (TRUE or FALSE)
- name: basic
dtype: float64
description: Local Basic Content (Fraction of Local Neighbors)
- name: acidic
dtype: float64
description: Local Acidic Content (Fraction of Local Neighbors)
- name: polar
dtype: float64
description: Local Polar Content (Fraction of Local Neighbors)
- name: cysteine
dtype: float64
description: Local Cysteine Content (Fraction of Local Neighbors)
- name: structural
dtype: float64
description: Local Structural Content (Fraction of Local Neighbors)
- name: aliphatic
dtype: float64
description: Local Aliphatic Content (Fraction of Local Neighbors)
- name: aromatic
dtype: float64
description: Local Aromatic Content (Fraction of Local Neighbors)
- name: is_active
dtype: bool
description: >-
Probe Interaction Fraction Thresholded to Classify Cysteines as Active
or Not (TRUE or FALSE)
- name: struct_motif_B
dtype: bool
description: Presense of Structural Motif B (DSSP)
- name: struct_motif_E
dtype: bool
description: Presense of Structural Motif E (DSSP)
- name: struct_motif_G
dtype: bool
description: Presense of Structural Motif G (DSSP)
- name: struct_motif_H
dtype: bool
description: Presense of Structural Motif H (DSSP)
- name: struct_motif_I
dtype: bool
description: Presense of Structural Motif I (DSSP)
- name: struct_motif_P
dtype: bool
description: Presense of Structural Motif P (DSSP)
- name: struct_motif_S
dtype: bool
description: Presense of Structural Motif S (DSSP)
- name: struct_motif_T
dtype: bool
description: Presense of Structural Motif T (DSSP)
- name: ligand_name
dtype: string
description: Name of Probe Interacting with Cysteine
- name: residue_number
dtype: int64
description: Residue Number of Cysteine
- name: ligand_smiles
dtype: string
description: SMILES depiction of Probe Interacting with Cysteine
splits:
- name: train
num_bytes: 979293
num_examples: 4685
- name: test
num_bytes: 136187
num_examples: 651
- name: validation
num_bytes: 245076
num_examples: 1172
Cysteine Structure Database
The [Cysteine Structure Database] is a dataset compiled of strucutral data for 6515 cysteine sites in hundreds of proteins. This dataset was published in Cell and is also available at the official DrugMap Github repo.
For each cysteine site, this database includes numerical values for Solvent Accessible Surface Area (SASA), Cysteine Depth, etc. Additionally, each cysteine site has a probe engagement score derived from isotopic tandem orthogonal proteolysis-activity-based protein profiling (isoTOP-ABPP) that is represented as True or False in this dataset for three probes: KB02, KB03, KB05.
Probes
KB02
SMILES: COC1=CC=C2C(CCCN2C(CCl)=O)=C1 Depiction:
KB03
SMILES: FC(F)(F)C1=CC(C(F)(F)F)=CC(NC(CCl)=O)=C1 Depiction:
KB05
SMILES: O=C(C=C)N(C1=CC=C(Br)C=C1)C2=CC=CC=C2 Depiction:
Quickstart Usage
Load a Dataset in Python
Each subset can be loaded into python using the Huggingface datasets library.
Install the datasets library
$ pip install datasets
then, in Python, load the datasets library
>>> import datasets
and load one of the Cysteine Structure Database datasets, e.g.,
>>> KB03_data = datasets.load_dataset('ymanasa2000/DrugMap_Ligandability', name='KB03')
Downloading readme: 100%|████████████████████████| 4.04k/4.04k [00:00<00:00, 281kB/s]
Downloading data: 100%|████████████████████████| 30.5k/30.5k [00:00<00:00, 470kB/s]
Downloading data: 100%|████████████████████████| 218k/218k [00:00<00:00, 2.55MB/s]
Downloading data: 100%|████████████████████████| 54.8k/54.8k [00:00<00:00, 915kB/s]
Generating test split: 100%|████████████████████████| 143/0 [00:00<00:00, 4939.35 examples/s]
Generating train split: 100%|████████████████████████| 1029/0 [00:00<00:00, 40692.99 examples/s]
Generating validation split: 100%|████████████████████████| 258/0 [00:00<00:00, 22803.78 examples/s]
Then, inspect the loaded dataset
>>> KB03_data
DatasetDict({
test: Dataset({
features: ['Unnamed: 0', 'site', 'depth', 'absolute_sasa', 'hse_up', 'hse_down', 'coord_number', 'rsa', 'h_nho1', 'h_ohn1', 'h_nho2', 'h_ohn2', 'tco', 'kappa', 'alpha', 'phi', 'psi', 'pocket', 'interface', 'basic', 'acidic', 'polar', 'cysteine', 'structural', 'aliphatic', 'aromatic', 'KB03', 'struct_motif_B', 'struct_motif_E', 'struct_motif_G', 'struct_motif_H', 'struct_motif_I', 'struct_motif_P', 'struct_motif_S', 'struct_motif_T'],
num_rows: 143
})
train: Dataset({
features: ['Unnamed: 0', 'site', 'depth', 'absolute_sasa', 'hse_up', 'hse_down', 'coord_number', 'rsa', 'h_nho1', 'h_ohn1', 'h_nho2', 'h_ohn2', 'tco', 'kappa', 'alpha', 'phi', 'psi', 'pocket', 'interface', 'basic', 'acidic', 'polar', 'cysteine', 'structural', 'aliphatic', 'aromatic', 'KB03', 'struct_motif_B', 'struct_motif_E', 'struct_motif_G', 'struct_motif_H', 'struct_motif_I', 'struct_motif_P', 'struct_motif_S', 'struct_motif_T'],
num_rows: 1029
})
validation: Dataset({
features: ['Unnamed: 0', 'site', 'depth', 'absolute_sasa', 'hse_up', 'hse_down', 'coord_number', 'rsa', 'h_nho1', 'h_ohn1', 'h_nho2', 'h_ohn2', 'tco', 'kappa', 'alpha', 'phi', 'psi', 'pocket', 'interface', 'basic', 'acidic', 'polar', 'cysteine', 'structural', 'aliphatic', 'aromatic', 'KB03', 'struct_motif_B', 'struct_motif_E', 'struct_motif_G', 'struct_motif_H', 'struct_motif_I', 'struct_motif_P', 'struct_motif_S', 'struct_motif_T'],
num_rows: 258
})
})
Use a Dataset to Train a Model
One way to use the dataset is by training a Baseline Random Forest Classifier to predict intereaction of a cysteine with one of the three probes (KB02, KB03, KB05). In this example, we will train and test on KB03 data.
First, install scikit-learn
>>> pip install scikit-learn
then load, split, featurize, fit and evaluate the Random Forest model
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
KB03_data = datasets.load_dataset('ymanasa2000/DrugMap_Ligandability', name='KB03')
# split into train and test
KB03_train = KB03_data['train']
KB03_test = KB03_data['test']
train_set = pd.DataFrame(KB03_train)
test_set = pd.DataFrame(KB03_test)
# featurize
X_train = train_set.drop(columns=['site', 'KB03'])
y_train = train_set['KB03']
X_test = test_set.drop(columns=['site', 'KB03'])
y_test = test_set['KB03']
# fit
model_1 = RandomForestClassifier()
model_1.fit(X_train, y_train)
# evaluate
print(model_1.score(X_test, y_test)) # output: 0.5944
About the Cysteine Structure Database
Features of the DB
This DB features a csv with structural data for ~6,500 bindable cysteines in hundreds of protein active sites. Each cysteine has structural data such as,
numerical values for Solvent Accessible Surface Area (SASA), Cysteine Depth, etc.
Additionally, this DB contains probe read-outs from an experiment described in Takahashi_et_al_2024. They integrated the isotopic tandem orthogonal
proteolysis-activity-based protein profiling (isoTOP-ABPP) platform with tandem mass tag (TMT)-based mass spectrometry quantification (iso-TMT)
to measure cysteine reactivity. In this approach, cell lysates are first treated with cysteine-reactive “scout” compounds or vehicle control,
allowing reactive cysteines a chance to form covalent adducts, and then this is followed by a chase with a pan-cysteine-reactive probe
(iodoacetamide-desthiobiotin DBIA), which reacts with all remaining free cysteine thiolate groups.
Crucially, cysteines that reacted with the scout compound will escape being tagged by DBIA.
Ligandable cysteines are defined as those that are engaged (ε-value) >60% by cysteine-reactive compounds.
Data splits
The authors of this dataset suggested using a Stratified Split via the train_test_split() method which was used to produce the datasets in this Hugging Face DB.
Citation
Please use the following citation in any publication using our Cysteine Structure Dataset:
@article{
Takahashi_et_al_2024,
author={Takahashi, Chong, Harrison, Bar-Peled, et al},
doi={10.1016/j.cell.2024.03.027},
journal={Cell},
number={10},
month={May}
title={DrugMap: A quantitative pan-cancer analysis of Cysteine ligandability},
volume={187},
year={2024}
url = {https://www.biorxiv.org/content/10.1101/2023.10.20.563287v1}
}