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Deep Semantic Protein Representation for Annotation, Discovery, and Engineering

Ariel S Schwartz, Gregory J Hannum, Zach R Dwiel, Michael E Smoot, Ana R Grant, Jason M Knight, Scott A Becker, Jonathan R Eads, Matthew C LaFave, Harini Eavani, Yinyin Liu, Arjun K Bansal, Toby H Richardson
doi: https://doi.org/10.1101/365965
Ariel S Schwartz
1Synthetic Genomics Inc., La Jolla, California, USA
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Gregory J Hannum
1Synthetic Genomics Inc., La Jolla, California, USA
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Zach R Dwiel
2Intel AI Lab, San Diego, California, USA
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Michael E Smoot
1Synthetic Genomics Inc., La Jolla, California, USA
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Ana R Grant
1Synthetic Genomics Inc., La Jolla, California, USA
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Jason M Knight
2Intel AI Lab, San Diego, California, USA
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Scott A Becker
1Synthetic Genomics Inc., La Jolla, California, USA
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Jonathan R Eads
1Synthetic Genomics Inc., La Jolla, California, USA
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Matthew C LaFave
1Synthetic Genomics Inc., La Jolla, California, USA
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Harini Eavani
2Intel AI Lab, San Diego, California, USA
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Yinyin Liu
2Intel AI Lab, San Diego, California, USA
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Arjun K Bansal
2Intel AI Lab, San Diego, California, USA
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Toby H Richardson
1Synthetic Genomics Inc., La Jolla, California, USA
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Abstract

Computational assignment of function to proteins with no known homologs is still an unsolved problem. We have created a novel, function-based approach to protein annotation and discovery called D-SPACE (Deep Semantic Protein Annotation Classification and Exploration), comprised of a multi-task, multi-label deep neural network trained on over 70 million proteins. Distinct from homology and motif-based methods, D-SPACE encodes proteins in high-dimensional representations (embeddings), allowing the accurate assignment of over 180,000 labels for 13 distinct tasks. The embedding representation enables fast searches for functionally related proteins, including homologs undetectable by traditional approaches. D-SPACE annotates all 109 million proteins in UniProt in under 35 hours on a single computer and searches the entirety of these in seconds. D-SPACE further quantifies the relative functional effect of mutations, facilitating rapid in silico mutagenesis for protein engineering applications. D-SPACE incorporates protein annotation, search, and other exploratory efforts into a single cohesive model.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted July 10, 2018.
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Deep Semantic Protein Representation for Annotation, Discovery, and Engineering
Ariel S Schwartz, Gregory J Hannum, Zach R Dwiel, Michael E Smoot, Ana R Grant, Jason M Knight, Scott A Becker, Jonathan R Eads, Matthew C LaFave, Harini Eavani, Yinyin Liu, Arjun K Bansal, Toby H Richardson
bioRxiv 365965; doi: https://doi.org/10.1101/365965
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Deep Semantic Protein Representation for Annotation, Discovery, and Engineering
Ariel S Schwartz, Gregory J Hannum, Zach R Dwiel, Michael E Smoot, Ana R Grant, Jason M Knight, Scott A Becker, Jonathan R Eads, Matthew C LaFave, Harini Eavani, Yinyin Liu, Arjun K Bansal, Toby H Richardson
bioRxiv 365965; doi: https://doi.org/10.1101/365965

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