Improving de novo Protein Binder Design with Deep Learning

Nathaniel Bennett; Brian Coventry; Inna Goreshnik; Buwei Huang; Aza Allen; Dionne Vafeados; Ying Po Peng; Justas Dauparas; Minkyung Baek; Lance Stewart; Frank DiMaio; Steven De Munck; Savvas N. Savvides; David Baker

doi:10.1101/2022.06.15.495993

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Improving de novo Protein Binder Design with Deep Learning

View ORCID ProfileNathaniel Bennett, View ORCID ProfileBrian Coventry, View ORCID ProfileInna Goreshnik, View ORCID ProfileBuwei Huang, View ORCID ProfileAza Allen, View ORCID ProfileDionne Vafeados, View ORCID ProfileYing Po Peng, View ORCID ProfileJustas Dauparas, View ORCID ProfileMinkyung Baek, View ORCID ProfileLance Stewart, View ORCID ProfileFrank DiMaio, View ORCID ProfileSteven De Munck, View ORCID ProfileSavvas N. Savvides, View ORCID ProfileDavid Baker

doi: https://doi.org/10.1101/2022.06.15.495993

Nathaniel Bennett

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

3Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA

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Brian Coventry

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

7Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

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Inna Goreshnik

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

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Buwei Huang

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

4Department of Bioengineering, University of Washington, Seattle, WA, USA

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Aza Allen

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

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Dionne Vafeados

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

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Ying Po Peng

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

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Justas Dauparas

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

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Minkyung Baek

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

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Lance Stewart

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

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Frank DiMaio

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

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Steven De Munck

5VIB-UGent Center for Inflammation Research, Ghent, Belgium

6Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium

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Savvas N. Savvides

5VIB-UGent Center for Inflammation Research, Ghent, Belgium

6Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium

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David Baker

1Department of Biochemistry, University of Washington, Seattle, WA, USA

2Institute for Protein Design, University of Washington, Seattle, WA, USA

7Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

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For correspondence: [email protected]

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Abstract

We explore the improvement of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.

Competing Interest Statement

N.B., B.C., I.G., L.S., D.B., A.A., and D.V. are co-inventors on a provisional patent application (application not yet submitted) that incorporates discoveries described in this manuscript.

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-ND 4.0 International license.

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Posted June 17, 2022.

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Improving de novo Protein Binder Design with Deep Learning

Nathaniel Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, Frank DiMaio, Steven De Munck, Savvas N. Savvides, David Baker

bioRxiv 2022.06.15.495993; doi: https://doi.org/10.1101/2022.06.15.495993

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