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Large-scale design and refinement of stable proteins using sequence-only models

View ORCID ProfileJedediah M. Singer, Scott Novotney, Devin Strickland, Hugh K. Haddox, Nicholas Leiby, Gabriel J. Rocklin, Cameron M. Chow, Anindya Roy, Asim K. Bera, Francis C. Motta, Longxing Cao, Eva-Maria Strauch, Tamuka M. Chidyausiku, Alex Ford, Ethan Ho, Craig O. Mackenzie, Hamed Eramian, Frank DiMaio, Gevorg Grigoryan, Matthew Vaughn, Lance J. Stewart, David Baker, Eric Klavins
doi: https://doi.org/10.1101/2021.03.12.435185
Jedediah M. Singer
1Two Six Technologies, Arlington, USA
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  • For correspondence: jed.singer@twosixtech.com
Scott Novotney
1Two Six Technologies, Arlington, USA
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Devin Strickland
2Department of Electrical and Computer Engineering, University of Washington, Seattle, USA
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Hugh K. Haddox
3Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA
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Nicholas Leiby
1Two Six Technologies, Arlington, USA
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Gabriel J. Rocklin
4Department of Pharmacology and Center for Synthetic Biology, Northwestern University Feinberg School of Medicine, Chicago, USA
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Cameron M. Chow
3Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA
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Anindya Roy
3Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA
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Asim K. Bera
3Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA
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Francis C. Motta
5Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, USA
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Longxing Cao
3Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA
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Eva-Maria Strauch
6Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, USA
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Tamuka M. Chidyausiku
3Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA
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Alex Ford
3Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA
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Ethan Ho
7Texas Advanced Computing Center, Austin, USA
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Craig O. Mackenzie
8Quantitative Biomedical Sciences Graduate Program, Dartmouth College, Hanover, USA
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Hamed Eramian
9Netrias, Arlington, USA
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Frank DiMaio
3Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA
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Gevorg Grigoryan
10Departments of Computer Science and Biological Sciences, Dartmouth College, Hanover, USA
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Matthew Vaughn
7Texas Advanced Computing Center, Austin, USA
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Lance J. Stewart
3Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA
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David Baker
3Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, USA
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Eric Klavins
2Department of Electrical and Computer Engineering, University of Washington, Seattle, USA
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Abstract

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.

Competing Interest Statement

JMS and NL are employed by Two Six Technologies, which has filed a patent on a portion of the technology described in this manuscript.

Footnotes

  • Minor formatting cleanups, link to data.

  • http://spear.sd2e.org

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-NC 4.0 International license.
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Posted April 06, 2021.
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Large-scale design and refinement of stable proteins using sequence-only models
Jedediah M. Singer, Scott Novotney, Devin Strickland, Hugh K. Haddox, Nicholas Leiby, Gabriel J. Rocklin, Cameron M. Chow, Anindya Roy, Asim K. Bera, Francis C. Motta, Longxing Cao, Eva-Maria Strauch, Tamuka M. Chidyausiku, Alex Ford, Ethan Ho, Craig O. Mackenzie, Hamed Eramian, Frank DiMaio, Gevorg Grigoryan, Matthew Vaughn, Lance J. Stewart, David Baker, Eric Klavins
bioRxiv 2021.03.12.435185; doi: https://doi.org/10.1101/2021.03.12.435185
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Large-scale design and refinement of stable proteins using sequence-only models
Jedediah M. Singer, Scott Novotney, Devin Strickland, Hugh K. Haddox, Nicholas Leiby, Gabriel J. Rocklin, Cameron M. Chow, Anindya Roy, Asim K. Bera, Francis C. Motta, Longxing Cao, Eva-Maria Strauch, Tamuka M. Chidyausiku, Alex Ford, Ethan Ho, Craig O. Mackenzie, Hamed Eramian, Frank DiMaio, Gevorg Grigoryan, Matthew Vaughn, Lance J. Stewart, David Baker, Eric Klavins
bioRxiv 2021.03.12.435185; doi: https://doi.org/10.1101/2021.03.12.435185

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