README.md

---
language: en
license: cc-by-4.0
size_categories:
- 100k<n<1M
pretty_name: 'Microbiome Immunity Project: Protein Universe'
config_names:
- rosetta_high_quality_models
- rosetta_low_quality_models
- dmpfold_high_quality_models
- dmpfold_low_quality_models
- rosetta_high_quality_function_predictions
- rosetta_low_quality_function_predictions
- dmpfold_high_quality_function_predictions
- dmpfold_low_quality_function_predictions
tags:
- chemistry
- biology
dataset_summary: ~200,000 predicted structures for diverse protein sequences from
  1,003 representative genomes across the microbial tree of life and annotate them
  functionally on a per-residue basis.
dataset_description: Large-scale structure prediction on representative protein domains
  from the Genomic Encyclopedia of Bacteria and Archaea (GEBA1003) reference genome
  database across the microbial tree of life. From a non-redundant GEBA1003 gene catalog
  protein sequences without matches to any structural databases and which produced
  multiple-sequence alignments of N_eff > 16 and all putative novel domains between
  40 and 200 residues were extracted. For each sequence 20,000 Rosetta de novo models
  and up to 5 DMPfold models were generated. The initial output dataset (MIP_raw)
  of about 240,000 models were curated to high-quality models comprising about 75%
  of the original dataset (MIP_curated). Functional annotations of the entire dataset
  were created using structure-based Graph Convolutional Network embeddings from DeepFRI.
acknowledgements: We kindly acknowledge the support of the IBM World Community Grid
  team (Caitlin Larkin, Juan A Hindo, Al Seippel, Erika Tuttle, Jonathan D Armstrong,
  Kevin Reed, Ray Johnson, and Viktors Berstis), and the community of 790,000 volunteers
  who donated 140,661 computational years since Aug 2017 of their computer time over
  the course of the project. This research was also supported in part by PLGrid Infrastructure
  (to PS). The authors thank Hera Vlamakis and Damian Plichta from the Broad Institute
  for helpful discussions. The work was supported by the Flatiron Institute as part
  of the Simons Foundation to J.K.L., P.D.R., V.G., D.B., C.C., A.P., N.C., I.F.,
  and R.B. This research was also supported by grants NAWA PPN/PPO/2018/1/00014 to
  P.S. and T.K., PLGrid to P.S., and NIH - DK043351 to T.V. and R.J.X.
repo: https://github.com/microbiome-immunity-project/protein_universe
citation_bibtex: "@article{KoehlerLeman2023,\n  title = {Sequence-structure-function\
  \ relationships in the microbial protein universe},\n  volume = {14},\n  ISSN =\
  \ {2041-1723},\n  url = {http://dx.doi.org/10.1038/s41467-023-37896-w},\n  DOI =\
  \ {10.1038/s41467-023-37896-w},\n  number = {1},\n  journal = {Nature Communications},\n\
  \  publisher = {Springer Science and Business Media LLC},\n  author = {Koehler Leman,\
  \  Julia and Szczerbiak,  Pawel and Renfrew,  P. Douglas and Gligorijevic,  Vladimir\
  \ and Berenberg,  Daniel and Vatanen,  Tommi and Taylor,  Bryn C. and Chandler,\
  \  Chris and Janssen,  Stefan and Pataki,  Andras and Carriero,  Nick and Fisk,\
  \  Ian and Xavier,  Ramnik J. and Knight,  Rob and Bonneau,  Richard and Kosciolek,\
  \  Tomasz},\n  year = {2023},\n  month = apr\n}"
citation_apa: Koehler Leman, J., Szczerbiak, P., Renfrew, P. D., Gligorijevic, V.,
  Berenberg, D., Vatanen, T., Taylor, B. C., Janssen, S., Pataki, A., Carriero, N.,
  Fisk, I., Xavier, R. J., Knight, R., Bonneau, R., Kosciolek, T. (2023). Sequence-structure-function
  relationships in the microbial protein universe. Nature Communications, 14(1), 2351.
  doi:10.1038/s41467-023-37896-w
configs:
- config_name: dmpfold_high_quality_function_predictions
  data_files:
  - split: train
    path: dmpfold_high_quality_function_predictions/data/train-*
- config_name: dmpfold_high_quality_models
  data_files:
  - split: train
    path: dmpfold_high_quality_models/data/train-*
- config_name: dmpfold_low_quality_function_predictions
  data_files:
  - split: train
    path: dmpfold_low_quality_function_predictions/data/train-*
- config_name: dmpfold_low_quality_models
  data_files:
  - split: train
    path: dmpfold_low_quality_models/data/train-*
- config_name: rosetta_high_quality_function_predictions
  data_files:
  - split: train
    path: rosetta_high_quality_function_predictions/data/train-*
- config_name: rosetta_high_quality_models
  data_files:
  - split: train
    path: rosetta_high_quality_models/data/train-*
- config_name: rosetta_low_quality_function_predictions
  data_files:
  - split: train
    path: rosetta_low_quality_function_predictions/data/train-*
- config_name: rosetta_low_quality_models
  data_files:
  - split: train
    path: rosetta_low_quality_models/data/train-*
dataset_info:
- config_name: dmpfold_high_quality_function_predictions
  features:
  - name: id
    dtype: large_string
  - name: term_id
    dtype: large_string
  - name: term_name
    dtype: large_string
  - name: Y_hat
    dtype: float64
  splits:
  - name: train
    num_bytes: 105506959131
    num_examples: 1287483255
  download_size: 37331993547
  dataset_size: 105506959131
- config_name: dmpfold_high_quality_models
  splits:
  - name: train
- config_name: dmpfold_low_quality_function_predictions
  features:
  - name: id
    dtype: large_string
  - name: term_id
    dtype: large_string
  - name: term_name
    dtype: large_string
  - name: Y_hat
    dtype: float64
  splits:
  - name: train
    num_bytes: 19642861371
    num_examples: 239698455
  download_size: 6947138509
  dataset_size: 19642861371
- config_name: dmpfold_low_quality_models
  splits:
  - name: train
- config_name: rosetta_high_quality_function_predictions
  features:
  - name: id
    dtype: large_string
  - name: term_id
    dtype: large_string
  - name: term_name
    dtype: large_string
  - name: Y_hat
    dtype: float64
  splits:
  - name: train
    num_bytes: 109228840707
    num_examples: 1332900735
  download_size: 38646102125
  dataset_size: 109228840707
- config_name: rosetta_high_quality_models
  splits:
  - name: train
- config_name: rosetta_low_quality_function_predictions
  features:
  - name: id
    dtype: large_string
  - name: term_id
    dtype: string
  - name: term_name
    dtype: large_string
  - name: Y_hat
    dtype: float64
  splits:
  - name: train
    num_bytes: 16920360882
    num_examples: 217071810
  download_size: 6294592566
  dataset_size: 16920360882
- config_name: rosetta_low_quality_models
  splits:
  - name: train
---
# Microbiome Immunity Project: Protein Universe
~200,000 predicted structures for diverse protein sequences from 1,003
representative genomes across the microbial tree of life and annotate
them functionally on a per-residue basis.


## Quickstart Usage

Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
First, from the command line install the `datasets` library

    $ pip install datasets

Optionally set the cache directory, e.g.

    $ HF_HOME=${HOME}/.cache/huggingface/
    $ export HF_HOME

then, from within python load the datasets library

    >>> import datasets
   
and load one of the `MPI` model, e.g.,

    >>> dataset_tag = "rosetta_high_quality"
    >>> dataset_models = datasets.load_dataset(
      path = "RosettaCommons/MIP",
      name = f"{dataset_tag}_models",
      data_dir = f"{dataset_tag}_models")
    Resolving data files: 100%|█████████████████████████████████████████| 54/54 [00:00<00:00, 441.70it/s]
    Downloading data: 100%|███████████████████████████████████████████| 54/54 [01:34<00:00,  1.74s/files]
    Generating train split: 100%|███████████████████████| 211069/211069 [01:41<00:00, 2085.54 examples/s]
    Loading dataset shards: 100%|███████████████████████████████████████| 48/48 [00:00<00:00, 211.74it/s]

and inspecting the loaded dataset

    >>> dataset_models
    DatasetDict({
        train: Dataset({
            features: ['id', 'pdb', 'Filter_Stage2_aBefore', 'Filter_Stage2_bQuarter', 'Filter_Stage2_cHalf', 'Filter_Stage2_dEnd', 'clashes_bb', 'clashes_total', 'score', 'silent_score', 'time'],
            num_rows: 211069
        })
    })

many structure-based pipelines expect a `.pdb` file as input. For example, `frame2seq` takes in a structure
and generates a sequence for the backbone. The `frame2seq` can be installed using `pip` from the command line:

    $ pip install frame2seq

Then used from within python:

    >>> from frame2seq import Frame2seqRunner
    >>> runner = Frame2seqRunner()
    >>> runner.design(
      pdb_file = "target.pdb",
      chain_id = "A",
      temperature = 1,
      num_samples = 5000)

To run `frame2seq` on each MIP target, 

    >>> for pdb in dataset_models.data['train'].column('pdb'):
      pdb.str
      print(f"Predicting sequences for id = {row$id}")
      pdb = row$pdb
      

    >>> dataset_function_prediction = datasets.load_dataset(
      path = "RosettaCommons/MIP",
      name = f"{dataset_tag}_function_predictions",
      data_dir = f"{dataset_tag}_function_predictions")
    Downloading readme: 100%|████████████████████████████████████████| 15.4k/15.4k [00:00<00:00, 264kB/s]
    Resolving data files: 100%|██████████████████████████████████████| 219/219 [00:00<00:00, 1375.51it/s]
    Downloading data: 100%|█████████████████████████████████████████| 219/219 [13:04<00:00,  3.58s/files]
    Generating train split: 100%|████████████| 1332900735/1332900735 [13:11<00:00, 1684288.89 examples/s]
    Loading dataset shards: 100%|██████████████████████████████████████| 219/219 [01:22<00:00,  2.66it/s]

this loads the `>1.3B` function predictions (xxx targets x yyyy terms from the GO and EC ontologies).
The predictions are stored in long format, but can be easily converted to a wide format using pandas:

    >>> dataset_function_prediction

    >>> import pandas
    >>> dataset_function_prediction_wide = pandas.pivot(
      dataset_function_prediction.data['train'].select(['id', 'term_id', 'Y_hat']).to_pandas()
      columns = "term_id",
      index = "id",
      values = "Y_hat")
    >>> dataset_function_prediction_wide[1:3, 1:3]

## Dataset Details

### Dataset Description
Large-scale structure prediction on representative protein domains from
the Genomic Encyclopedia of Bacteria and Archaea (GEBA1003) reference
genome database across the microbial tree of life. From a non-redundant
GEBA1003 gene catalog protein sequences without matches to any structural databases
and which produced multiple-sequence alignments of N_eff > 16 and all
putative novel domains between 40 and 200 residues were extracted.
For each sequence 20,000 Rosetta de novo models and up to 5 DMPfold models
were generated. The initial output dataset (MIP_raw) of about 240,000
models were curated to high-quality models comprising about 75% of the
original dataset (MIP_curated): Models were filtered out if (1) Rosetta
models had >60% coil content or DMPFold models with >80% coil content,
(2) the averaging the pairwise TM-scores of the 10 lowest-scoring models
was less than 0.4, and (3) if the Rosetta and DMPfold models had TM-score
less than 0.5. Functional annotations of the entire dataset were
created using structure-based Graph Convolutional Network
embeddings from DeepFRI.

- **Acknowledgements:**
  We kindly acknowledge the support of the IBM World Community Grid team
  (Caitlin Larkin, Juan A Hindo, Al Seippel, Erika Tuttle, Jonathan D Armstrong,
  Kevin Reed, Ray Johnson, and Viktors Berstis), and the community of 790,000
  volunteers who donated 140,661 computational years since Aug 2017 of their
  computer time over the course of the project. This research was also
  supported in part by PLGrid Infrastructure (to PS). The authors thank Hera
  Vlamakis and Damian Plichta from the Broad Institute for helpful discussions.
  The work was supported by the Flatiron Institute as part of the Simons Foundation
  to J.K.L., P.D.R., V.G., D.B., C.C., A.P., N.C., I.F., and R.B. This research
  was also supported by grants NAWA PPN/PPO/2018/1/00014 to P.S. and T.K.,
  PLGrid to P.S., and NIH - DK043351 to T.V. and R.J.X.

- **License:** cc-by-4.0

### Dataset Sources
- **Repository:** https://github.com/microbiome-immunity-project/protein_universe
- **Paper:**
  Koehler Leman, J., Szczerbiak, P., Renfrew, P. D., Gligorijevic, V., Berenberg,
  D., Vatanen, T., … Kosciolek, T. (2023). Sequence-structure-function relationships
  in the microbial protein universe. Nature Communications, 14(1), 2351.
  doi:10.1038/s41467-023-37896-w
- **Zenodo Repository:** https://doi.org/10.5281/zenodo.6611431


## Uses
Exploration of sequence-structure-function relationship in naturally ocurring proteins.
The MIP database is complementary to and distinct from the other large-scale predicted
protein structure databases such as the EBI AlphaFold database because it consists of
proteins from Archaea and Bacteria, whose protein sequences are generally shorter
than Eukaryotic.

### Direct Use
This dataset could be used to train representation models of protein structure

-


### Out-of-Scope Use
While this dataset has been curated for quality, in some cases the predicted structures
may not represent physically realistic conformations. Thus caution much be used when using
it as training data for protein structure prediction and design.

## Dataset Structure
    microbiome_immunity_project_dataset
      dataset
        dmpfold_high_quality_function_predictions
          DeepFRI_MIP_<chunk-index>_<gene-ontology-prefix>_pred_scores.json.gz
        dmpfold_high_quality_models
          MIP_<MIP-ID>.pdb.gz.pdb.gz


### Source Data

Sequences were obtained from the Genomic Encyclopedia of Bacteria and Archaea
([GEBA1003](https://genome.jgi.doe.gov/portal/geba1003/geba1003.info.html)) reference
genome database across the microbial tree of life:

> **1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life**
> We present 1,003 reference genomes that were sequenced as part of the Genomic Encyclopedia of Bacteria
> and Archaea (GEBA) initiative, selected to maximize sequence coverage of phylogenetic space.
> These genomes double the number of existing type strains and expand their overall phylogenetic
> diversity by 25%. Comparative analyses with previously available finished and draft genomes reveal
> a 10.5% increase in novel protein families as a function of phylogenetic diversity. The GEBA genomes
> recruit 25 million previously unassigned metagenomic proteins from 4,650 samples, improving their
> phylogenetic and functional interpretation. We identify numerous biosynthetic clusters and experimentally
> validate a divergent phenazine cluster with potential new chemical structure and antimicrobial activity.
> This Resource is the largest single release of reference genomes to date. Bacterial and archaeal isolate
> sequence space is still far from saturated, and future endeavors in this direction will continue to be a
> valuable resource for scientific discovery.

#### Data Collection and Processing

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#### Who are the source data producers?

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## Bias, Risks, and Limitations

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### Recommendations


    


<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

{{ bias_recommendations | default("Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.", true)}}

## Citation

    @article{KoehlerLeman2023,
      title = {Sequence-structure-function relationships in the microbial protein universe},
      volume = {14},
      ISSN = {2041-1723},
      url = {http://dx.doi.org/10.1038/s41467-023-37896-w},
      DOI = {10.1038/s41467-023-37896-w},
      number = {1},
      journal = {Nature Communications},
      publisher = {Springer Science and Business Media LLC},
      author = {Koehler Leman,  Julia and Szczerbiak,  Pawel and Renfrew,  P. Douglas and Gligorijevic,  Vladimir and Berenberg,  Daniel and Vatanen,  Tommi and Taylor,  Bryn C. and Chandler,  Chris and Janssen,  Stefan and Pataki,  Andras and Carriero,  Nick and Fisk,  Ian and Xavier,  Ramnik J. and Knight,  Rob and Bonneau,  Richard and Kosciolek,  Tomasz},
      year = {2023},
      month = apr
    }



## Dataset Card Authors
Matthew O'Meara (maom@umich.edu)