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DiffNets: Self-supervised deep learning to identify the mechanistic basis for biochemical differences between protein variants

Michael D. Ward, Maxwell I. Zimmerman, View ORCID ProfileS.J. Swamidass, Gregory R. Bowman
doi: https://doi.org/10.1101/2020.07.01.182725
Michael D. Ward
1Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110, USA
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Maxwell I. Zimmerman
1Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110, USA
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S.J. Swamidass
2Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
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  • ORCID record for S.J. Swamidass
Gregory R. Bowman
1Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110, USA
3Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO 63110, USA
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  • For correspondence: g.bowman@wustl.edu
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Abstract

A mechanistic understanding of how mutations modulate proteins’ biochemical properties would advance our understanding of biology, provide insight for engineering proteins with particular functions, and facilitate efforts in precision medicine. However, such mechanistic insight remains elusive in many cases. For example, experimentally-derived structures of protein variants with dramatically different behaviors are often nearly identical, suggesting that one must consider the entire ensemble of structures that a protein adopts. Molecular dynamics (MD) simulations provide access to such ensembles, but identifying the relevant features of these complex entities remains difficult. Here we develop DiffNets, a deep learning framework that combines supervised autoencoders with expectation maximization to identify the structural preferences that are responsible for the biochemical differences between protein variants. As a proof of principle, we show that DiffNets identify the important structural preferences that confer increased stability to TEM β-lactamase variants without any a priori knowledge of the relevant structural features.

Competing Interest Statement

The authors have declared no competing interest.

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-ND 4.0 International license.
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Posted July 02, 2020.
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DiffNets: Self-supervised deep learning to identify the mechanistic basis for biochemical differences between protein variants
Michael D. Ward, Maxwell I. Zimmerman, S.J. Swamidass, Gregory R. Bowman
bioRxiv 2020.07.01.182725; doi: https://doi.org/10.1101/2020.07.01.182725
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DiffNets: Self-supervised deep learning to identify the mechanistic basis for biochemical differences between protein variants
Michael D. Ward, Maxwell I. Zimmerman, S.J. Swamidass, Gregory R. Bowman
bioRxiv 2020.07.01.182725; doi: https://doi.org/10.1101/2020.07.01.182725

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