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README.md
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tags:
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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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size_categories:
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- 100k<n<1M
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---
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#
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## Dataset Details
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### Dataset Description
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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:**
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- **Paper
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## Uses
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## Dataset Card Contact
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{{ dataset_card_contact | default("[More Information Needed]", true)}}
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tags:
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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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size_categories:
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- 100k<n<1M
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---
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# 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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## Dataset Details
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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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GEBA1003 gene catalog protein sequences without matches to any structural databases
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and which produced multiple-sequence alignments of N_eff > 16 and all
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putative novel domains between 40 and 200 residues were extracted.
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For each sequence 20,000 Rosetta de novo models and up to 5 DMPfold models
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were generated. The initial output dataset (MIP_raw) of about 240,000
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models were curated to high-quality models comprising about 75% of the
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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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- **License:** cc-by-4.0
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### Dataset Sources [optional]
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<!-- Provide the basic links for the dataset. -->
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- **Repository:** https://github.com/microbiome-immunity-project/protein_universe
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- **Paper:**
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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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## Uses
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## Dataset Card Contact
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{{ dataset_card_contact | default("[More Information Needed]", true)}}
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-->
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