---
language: en
license: cc-by-4.0
tags:
- chemistry
- biology
pretty_name: "Microbiome Immunity Project: Protein Universe"
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,
    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 
  }
citation_apa: >-
  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
size_categories:
- 100k<n<1M
---
# 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.

## 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). 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 [optional]

<!-- Provide the basic links for the dataset. -->

- **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


## Uses

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### Direct Use

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### Out-of-Scope Use

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## Dataset Structure

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## Dataset Creation

### Curation Rationale

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### Source Data

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#### Data Collection and Processing

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### Annotations [optional]

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#### Annotation process

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#### Personal and Sensitive Information

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## Bias, Risks, and Limitations

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### Recommendations

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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