ProteinMPNN/group_mpnn

Curated ProteinMPNN training dataset

The multi-chain training data for ProteinMPNN

Quickstart Usage

Install HuggingFace Datasets package

Each subset can be loaded into python using the Huggingface datasets 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

Load model datasets

To load one of the group_mpnn model datasets, use datasets.load_dataset(...):

>>> dataset_tag = "train"
>>> dataset_models = datasets.load_dataset(
  path = "leebecca/group_mpnn",
  name = f"{dataset_tag}_models",
  data_dir = f"{dataset_tag}")['train']

and the dataset is loaded as a datasets.arrow_dataset.Dataset

>>> dataset_models
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
})

which is a column oriented format that can be accessed directly, converted in to a pandas.DataFrame, or parquet format, e.g.

>>> dataset_models.data.column('pdb')
>>> dataset_models.to_pandas()
>>> dataset_models.to_parquet("dataset.parquet")

Dataset Details

Dataset Description

This dataset contains metadata and per-chain sequence and tensorized coorindates for the multi-chain training data for ProteinMPNN

• Acknowledgements: We kindly acknowledge the ProteinMPNN team, RosettaCommons, and the following institutions: University of California, Los Angeles; University of Maryland; University of Oregon; University of Michigan; University of Pennsylvania; and the Wistar Institute.

• License: rosetta-license-1.0

Dataset Sources

• Repository: https://github.com/dauparas/ProteinMPNN/tree/main/training
• Paper: Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., … Baker, D. (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science (New York, N.Y.), 378(6615), 49–56. doi:10.1126/science.add2187

Uses

Exploration of sequence-structure relationships, not limited to inverse folding models.

Out-of-Scope Use

This dataset has been curated with restraints imposed by the ProteinMPNN team. Thus caution much be used when using it as training data for protein structure prediction.

Source Data

A set of 19,700 high-resolution single-chain structures from the Protein Data Bank (PDB) were split into train, validation, and test sets (80/10/10) based on the CATH protein classification database. This set contains protein assemblies in the PDB (as of 2 August 2021) determined by x-ray crystallography or cryo–electron microscopy (cryo-EM) to better than 3.5-Å resolution and with fewer than 10,000 residues.

Citation

@article{Dauparas2022,
title = {Robust deep learning–based protein sequence design using ProteinMPNN},
volume = {378},
ISSN = {1095-9203},
url = {http://dx.doi.org/10.1126/science.add2187},
DOI = {10.1126/science.add2187},
number = {6615},
journal = {Science},
publisher = {American Association for the Advancement of Science (AAAS)},
author = {Dauparas,  J. and Anishchenko,  I. and Bennett,  N. and Bai,  H. and Ragotte,  R. J. and Milles,  L. F. and Wicky,  B. I. M. and Courbet,  A. and de Haas,  R. J. and Bethel,  N. and Leung,  P. J. Y. and Huddy,  T. F. and Pellock,  S. and Tischer,  D. and Chan,  F. and Koepnick,  B. and Nguyen,  H. and Kang,  A. and Sankaran,  B. and Bera,  A. K. and King,  N. P. and Baker,  D.},
year = {2022},
month = oct,
pages = {49–56}
}

Dataset Card Authors

Miranda Simpson (miranda13nicoles@gmail.com), Becca Lee (beccalee5@g.ucla.edu), Nathaniel Felbinger (nfelbing@umd.edu), Pratyush Dhal (pdhal@umich.edu), Colby Agostino (colby.agostino@pennmedicine.upenn.edu)