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Using Deep Learning to Annotate the Protein Universe

View ORCID ProfileMaxwell L. Bileschi, View ORCID ProfileDavid Belanger, Drew Bryant, View ORCID ProfileTheo Sanderson, View ORCID ProfileBrandon Carter, D. Sculley, View ORCID ProfileMark A. DePristo, View ORCID ProfileLucy J. Colwell
doi: https://doi.org/10.1101/626507
Maxwell L. Bileschi
1Google Research
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  • For correspondence: mlbileschi@google.com lcolwell@google.com
David Belanger
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Drew Bryant
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Theo Sanderson
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Brandon Carter
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2Computer Science and Artificial Intelligence Laboratory, MIT
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D. Sculley
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Mark A. DePristo
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Lucy J. Colwell
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3Dept. of Chemistry, Cambridge University
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  • For correspondence: mlbileschi@google.com lcolwell@google.com
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Abstract

Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications. Despite six decades of progress, state-of-the-art techniques cannot annotate 1/3 of microbial protein sequences, hampering our ability to exploit sequences collected from diverse organisms. To address this, we report a deep learning model that learns the relationship between unaligned amino acid sequences and their functional classification across all 17929 families of the Pfam database. Using the Pfam seed sequences we establish a rigorous benchmark assessment and find a dilated convolutional model that reduces the error of both BLASTp and pHMMs by a factor of nine. Using 80% of the full Pfam database we train a protein family predictor that is more accurate and over 200 times faster than BLASTp, while learning sequence features it was not trained on such as structural disorder and transmembrane helices. Our model co-locates sequences from unseen families in embedding space, allowing sequences from novel families to be accurately annotated. These results suggest deep learning models will be a core component of future protein function prediction tools.

Footnotes

  • Fixes: - author affiliation, - speed error about hmmsearch.

  • https://www.kaggle.com/googleai/pfam-seed-random-split

  • https://pantheon.corp.google.com/storage/browser/brain-genomics-public/research/proteins/pfam/random_split?pli=1

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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. All rights reserved. No reuse allowed without permission.
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Posted May 06, 2019.
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Using Deep Learning to Annotate the Protein Universe
Maxwell L. Bileschi, David Belanger, Drew Bryant, Theo Sanderson, Brandon Carter, D. Sculley, Mark A. DePristo, Lucy J. Colwell
bioRxiv 626507; doi: https://doi.org/10.1101/626507
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Using Deep Learning to Annotate the Protein Universe
Maxwell L. Bileschi, David Belanger, Drew Bryant, Theo Sanderson, Brandon Carter, D. Sculley, Mark A. DePristo, Lucy J. Colwell
bioRxiv 626507; doi: https://doi.org/10.1101/626507

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