--- language: en license: cc-by-4.0 size_categories: - 100k 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 features: - name: id dtype: string - name: pdb dtype: string splits: - name: train num_bytes: 11207993089 num_examples: 203878 download_size: 4371437931 dataset_size: 11207993089 - 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 features: - name: id dtype: string - name: pdb dtype: string splits: - name: train num_bytes: 1587078782 num_examples: 37957 download_size: 618815244 dataset_size: 1587078782 - 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 features: - name: id dtype: string - name: pdb dtype: string - name: Filter_Stage2_aBefore dtype: float64 - name: Filter_Stage2_bQuarter dtype: float64 - name: Filter_Stage2_cHalf dtype: float64 - name: Filter_Stage2_dEnd dtype: float64 - name: clashes_bb dtype: float64 - name: clashes_total dtype: float64 - name: score dtype: float64 - name: silent_score dtype: float64 - name: time dtype: float64 splits: - name: train num_bytes: 26605117078 num_examples: 211069 download_size: 9111917125 dataset_size: 26605117078 - 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 features: - name: id dtype: string - name: pdb dtype: string - name: Filter_Stage2_aBefore dtype: float64 - name: Filter_Stage2_bQuarter dtype: float64 - name: Filter_Stage2_cHalf dtype: float64 - name: Filter_Stage2_dEnd dtype: float64 - name: clashes_bb dtype: float64 - name: clashes_total dtype: float64 - name: score dtype: float64 - name: silent_score dtype: float64 - name: time dtype: float64 splits: - name: train num_bytes: 5140214262 num_examples: 34374 download_size: 1763765951 dataset_size: 5140214262 --- # 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 ### Install HuggingFace Datasets package 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 ### Load model datasets To load one of the `MIP` model datasets, use `datasets.load_dataset(...)`: >>> dataset_tag = "rosetta_high_quality" >>> dataset_models = datasets.load_dataset( path = "RosettaCommons/MIP", name = f"{dataset_tag}_models", data_dir = f"{dataset_tag}_models")['train'] 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 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") ### Load Function Predictions Function predictions are generated using `DeepFRI` across >>> dataset_function_prediction = datasets.load_dataset( path = "RosettaCommons/MIP", name = f"{dataset_tag}_function_predictions", data_dir = f"{dataset_tag}_function_predictions")['train'] 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 for all `211069` targets across `6315` GO and EC ontology terms. The predictions are stored in long format, but can be easily converted to a wide format using pandas: >>> import pandas >>> dataset_function_prediction_wide = pandas.pivot( dataset_function_prediction.data.select(['id', 'term_id', 'Y_hat']).to_pandas(), columns = "term_id", index = "id", values = "Y_hat") >>> dataset_function_prediction_wide.shape (211069, 6315) ## 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. *The highest quality structure for each sequence for both Rosetta and DMPFold, is included in this dataset; the entire dataset of more than 5 billion Rosetta models and 1 million DMPFold models is available upon request.* - **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. ### 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. ### 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. ## 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)