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Toward machine-guided design of proteins

View ORCID ProfileSurojit Biswas, View ORCID ProfileGleb Kuznetsov, Pierce J. Ogden, Nicholas J. Conway, Ryan P. Adams, George M. Church
doi: https://doi.org/10.1101/337154
Surojit Biswas
1Wyss Institute for Biologically Inspired Engineering. Harvard University. Boston, MA.
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Gleb Kuznetsov
1Wyss Institute for Biologically Inspired Engineering. Harvard University. Boston, MA.
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Pierce J. Ogden
1Wyss Institute for Biologically Inspired Engineering. Harvard University. Boston, MA.
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Nicholas J. Conway
1Wyss Institute for Biologically Inspired Engineering. Harvard University. Boston, MA.
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Ryan P. Adams
2Dept. of Computer Science. Princeton University. Princeton, NJ.
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George M. Church
1Wyss Institute for Biologically Inspired Engineering. Harvard University. Boston, MA.
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Abstract

Proteins—molecular machines that underpin all biological life—are of significant therapeutic and industrial value. Directed evolution is a high-throughput experimental approach for improving protein function, but has difficulty escaping local maxima in the fitness landscape. Here, we investigate how supervised learning in a closed loop with DNA synthesis and high-throughput screening can be used to improve protein design. Using the green fluorescent protein (GFP) as an illustrative example, we demonstrate the opportunities and challenges of generating training datasets conducive to selecting strongly generalizing models. With prospectively designed wet lab experiments, we then validate that these models can generalize to unseen regions of the fitness landscape, even when constrained to explore combinations of non-trivial mutations. Taken together, this suggests a hybrid optimization strategy for protein design in which a predictive model is used to explore difficult-to-access but promising regions of the fitness landscape that directed evolution can then exploit at scale.

Footnotes

  • surojitbiswas{at}g.harvard.edu, gleb.kuznetsov{at}wyss.harvard.edu, pierceogden{at}g.harvard.edu, nick.conway{at}wyss.harvard.edu, rpa{at}princeton.edu, gchurch{at}genetics.med.harvard.edu

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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 02, 2018.
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Toward machine-guided design of proteins
Surojit Biswas, Gleb Kuznetsov, Pierce J. Ogden, Nicholas J. Conway, Ryan P. Adams, George M. Church
bioRxiv 337154; doi: https://doi.org/10.1101/337154
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Toward machine-guided design of proteins
Surojit Biswas, Gleb Kuznetsov, Pierce J. Ogden, Nicholas J. Conway, Ryan P. Adams, George M. Church
bioRxiv 337154; doi: https://doi.org/10.1101/337154

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