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Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

Ananthan Nambiar, Simon Liu, Mark Hopkins, Maeve Heflin, View ORCID ProfileSergei Maslov, Anna Ritz
doi: https://doi.org/10.1101/2020.06.15.153643
Ananthan Nambiar
1Department of Bioengineering, Carl R. Woese Inst. for Genomic Biol., University of Illinois at Urbana-Champaign, Urbana, IL 61801
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Simon Liu
2Department of Computer Science, Carl R. Woese Inst. for Genomic Biol., University of Illinois at Urbana-Champaign, Urbana, IL 61801
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Mark Hopkins
3Department of Computer Science, Reed College, Portland, OR 97202
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Maeve Heflin
2Department of Computer Science, Carl R. Woese Inst. for Genomic Biol., University of Illinois at Urbana-Champaign, Urbana, IL 61801
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Sergei Maslov
4Department Bioengineering, Department of Physics, Carl R. Woese Inst. for Genomic Biol., University of Illinois at Urbana-Champaign, Urbana, IL 61801
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  • ORCID record for Sergei Maslov
Anna Ritz
5Department of Biology, Reed College, Portland, OR 97202
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  • For correspondence: aritz@reed.edu
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Abstract

The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have computational limitations or are designed to solve a specific task. We present a Transformer neural network that pre-trains task-agnostic sequence representations. This model is fine-tuned to solve two different protein prediction tasks: protein family classification and protein interaction prediction. Our method is comparable to existing state-of-the art approaches for protein family classification, while being much more general than other architectures. Further, our method outperforms all other approaches for protein interaction prediction. These results offer a promising framework for fine-tuning the pre-trained sequence representations for other protein prediction tasks.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • nambiar4{at}illinois.edu

Copyright 
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 4.0 International license.
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Posted June 16, 2020.
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Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks
Ananthan Nambiar, Simon Liu, Mark Hopkins, Maeve Heflin, Sergei Maslov, Anna Ritz
bioRxiv 2020.06.15.153643; doi: https://doi.org/10.1101/2020.06.15.153643
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Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks
Ananthan Nambiar, Simon Liu, Mark Hopkins, Maeve Heflin, Sergei Maslov, Anna Ritz
bioRxiv 2020.06.15.153643; doi: https://doi.org/10.1101/2020.06.15.153643

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