Low-N protein engineering with data-efficient deep learning
Name
2020.01.23.917682v2.full.pdf
Description
Submitted version
Size
2.01 MB
Format
Adobe PDF
Checksum (MD5)
6fe08274d21543598ba4b397ef057457
Author(s)
Biswas, Surojit
Khimulya, Grigory
Alley, Ethan C
Esvelt, Kevin M
Church, George M
Date Issued
2021
Journal
Nature Methods
Publisher
Springer Science and Business Media LLC
Version
Original manuscript
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 screen ten million sequences via in silico directed evolution. As demonstrated in two dissimilar proteins, GFP from Aequorea victoria (avGFP) and E. coli strain TEM-1 β-lactamase, top candidates from a single round are diverse and as active as engineered mutants obtained from previous high-throughput efforts. By distilling information from natural protein sequence landscapes, our model learns a latent representation of 'unnaturalness', which helps to guide search away from nonfunctional sequence neighborhoods. Subsequent low-N supervision then identifies improvements to the activity of interest. In sum, our approach enables efficient use of resource-intensive high-fidelity assays without sacrificing throughput, and helps to accelerate engineered proteins into the fermenter, field and clinic.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41592-021-01100-y
