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README.md
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- chemistry
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- biology
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pretty_name: "Microbiome Immunity Project Protein Universe"
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~200,000 predicted structures for diverse protein sequences from 1,003
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representative genomes across the microbial tree of life and annotate
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them functionally on a per-residue basis.
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Large-scale structure prediction on representative protein domains from
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the Genomic Encyclopedia of Bacteria and Archaea (GEBA1003) reference
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genome database across the microbial tree of life. From a non-redundant
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original dataset (MIP_curated). Functional annotations of the entire
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dataset were created using structure-based Graph Convolutional Network
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embeddings from DeepFRI.
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---
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# Dataset Card for {{ pretty_name }}
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{{ dataset_summary}}
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## Dataset Details
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<!-- Provide a longer summary of what this dataset is. -->
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{{ dataset_description
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- **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
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- **License:** {{ license | default("[More Information Needed]", true)}}
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### Dataset Sources [optional]
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<!-- Provide the basic links for the dataset. -->
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- **Repository:** {{ repo | default("[More Information Needed]", true)}}
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- **Paper [optional]:** {{
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- **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}
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## Uses
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- chemistry
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- biology
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pretty_name: "Microbiome Immunity Project Protein Universe"
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dataset_summary: >-
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~200,000 predicted structures for diverse protein sequences from 1,003
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representative genomes across the microbial tree of life and annotate
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them functionally on a per-residue basis.
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dataset_description: >-
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Large-scale structure prediction on representative protein domains from
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the Genomic Encyclopedia of Bacteria and Archaea (GEBA1003) reference
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genome database across the microbial tree of life. From a non-redundant
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original dataset (MIP_curated). Functional annotations of the entire
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dataset were created using structure-based Graph Convolutional Network
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embeddings from DeepFRI.
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acknowledgements: >-
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We kindly acknowledge the support of the IBM World Community Grid team
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(Caitlin Larkin, Juan A Hindo, Al Seippel, Erika Tuttle, Jonathan D Armstrong,
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Kevin Reed, Ray Johnson, and Viktors Berstis), and the community of 790,000
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volunteers who donated 140,661 computational years since Aug 2017 of their
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computer time over the course of the project. This research was also
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supported in part by PLGrid Infrastructure (to PS). The authors thank Hera
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Vlamakis and Damian Plichta from the Broad Institute for helpful discussions.
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The work was supported by the Flatiron Institute as part of the Simons Foundation
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to J.K.L., P.D.R., V.G., D.B., C.C., A.P., N.C., I.F., and R.B. This research
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was also supported by grants NAWA PPN/PPO/2018/1/00014 to P.S. and T.K.,
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PLGrid to P.S., and NIH - DK043351 to T.V. and R.J.X.
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repo: https://github.com/microbiome-immunity-project/protein_universe
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citation_bibtex: >-
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@article{KoehlerLeman2023,
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title = {Sequence-structure-function relationships in the microbial protein universe},
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volume = {14},
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ISSN = {2041-1723},
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url = {http://dx.doi.org/10.1038/s41467-023-37896-w},
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DOI = {10.1038/s41467-023-37896-w},
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number = {1},
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journal = {Nature Communications},
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publisher = {Springer Science and Business Media LLC},
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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},
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year = {2023},
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month = apr
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}
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citation_apa: >-
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Koehler Leman, J., Szczerbiak, P., Renfrew, P. D., Gligorijevic, V., Berenberg,
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D., Vatanen, T., … Kosciolek, T. (2023). Sequence-structure-function relationships
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in the microbial protein universe. Nature Communications, 14(1), 2351.
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doi:10.1038/s41467-023-37896-w
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---
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# Dataset Card for {{ pretty_name }}
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{{ dataset_summary }}
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## Dataset Details
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<!-- Provide a longer summary of what this dataset is. -->
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{{ dataset_description }}
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- **Acknowledgements:** {{ acknowledgements })}
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- **Language(s) (NLP):** {{ language }}
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- **License:** {{ license }}
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### Dataset Sources [optional]
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<!-- Provide the basic links for the dataset. -->
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- **Repository:** {{ repo | default("[More Information Needed]", true)}}
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- **Paper [optional]:** {{ citation_apa | default("[More Information Needed]", true)}}
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## Uses
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