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Function-guided protein design by deep manifold sampling

View ORCID ProfileVladimir Gligorijević, Daniel Berenberg, View ORCID ProfileStephen Ra, View ORCID ProfileAndrew Watkins, Simon Kelow, Kyunghyun Cho, View ORCID ProfileRichard Bonneau
doi: https://doi.org/10.1101/2021.12.22.473759
Vladimir Gligorijević
1Prescient Design, Genentech
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  • For correspondence: gligorv1@gene.com
Daniel Berenberg
1Prescient Design, Genentech
2Department of Computer Science, Courant Institute of Mathematical Sciences, New York University
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Stephen Ra
1Prescient Design, Genentech
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Andrew Watkins
1Prescient Design, Genentech
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Simon Kelow
1Prescient Design, Genentech
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Kyunghyun Cho
1Prescient Design, Genentech
2Department of Computer Science, Courant Institute of Mathematical Sciences, New York University
3Center for Data Science, New York University
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Richard Bonneau
1Prescient Design, Genentech
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Abstract

Protein design is challenging because it requires searching through a vast combinatorial space that is only sparsely functional. Self-supervised learning approaches offer the potential to navigate through this space more effectively and thereby accelerate protein engineering. We introduce a sequence denoising autoencoder (DAE) that learns the manifold of protein sequences from a large amount of potentially unlabelled proteins. This DAE is combined with a function predictor that guides sampling towards sequences with higher levels of desired functions. We train the sequence DAE on more than 20M unlabeled protein sequences spanning many evolutionarily diverse protein families and train the function predictor on approximately 0.5M sequences with known function labels. At test time, we sample from the model by iteratively denoising a sequence while exploiting the gradients from the function predictor. We present a few preliminary case studies of protein design that demonstrate the effectiveness of this proposed approach, which we refer to as “deep manifold sampling”, including metal binding site addition, function-preserving diversification, and global fold change.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • gligorijevic.vladimir{at}gene.com

  • https://www.mlsb.io/papers_2021/MLSB2021_Function-guided_protein_design_by.pdf

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-ND 4.0 International license.
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Posted December 23, 2021.
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Function-guided protein design by deep manifold sampling
Vladimir Gligorijević, Daniel Berenberg, Stephen Ra, Andrew Watkins, Simon Kelow, Kyunghyun Cho, Richard Bonneau
bioRxiv 2021.12.22.473759; doi: https://doi.org/10.1101/2021.12.22.473759
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Function-guided protein design by deep manifold sampling
Vladimir Gligorijević, Daniel Berenberg, Stephen Ra, Andrew Watkins, Simon Kelow, Kyunghyun Cho, Richard Bonneau
bioRxiv 2021.12.22.473759; doi: https://doi.org/10.1101/2021.12.22.473759

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