Health AI Central
Peer-Reviewed Publication
Nat Methods2021;18(4):389-396.April 1, 2021Journal Article

Low-N protein engineering with data-efficient deep learning.

Surojit Biswas1,2, Grigory Khimulya3, Ethan C Alley4, Kevin M Esvelt4, George M Church5,6
1Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
2Nabla Bio, Inc., Boston, MA, USA.
3Telis Bioscience Inc., Boston, MA, USA.
4MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
5Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA. gchurch@genetics.med.harvard.edu.
6Department of Genetics, Harvard Medical School, Boston, MA, USA. gchurch@genetics.med.harvard.edu.
PubMed DOI

Abstract

Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and…

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