Discovery of novel gain-of-function mutations guided by structure-based deep learning

Raghav Shroff, Austin W. Cole, Daniel J. Diaz, Barrett R. Morrow, Isaac Donnell, Ankur Annapareddy, Jimmy Gollihar, Andrew D. Ellington, Ross Thyer

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Despite the promise of deep learning accelerated protein engineering, examples of such improved proteins are scarce. Here we report that a 3D convolutional neural network trained to associate amino acids with neighboring chemical microenvironments can guide identification of novel gain-of-function mutations that are not predicted by energetics-based approaches. Amalgamation of these mutations improved protein function in vivo across three diverse proteins by at least 5-fold. Furthermore, this model provides a means to interrogate the chemical space within protein microenvironments and identify specific chemical interactions that contribute to the gain-of-function phenotypes resulting from individual mutations.

Original languageEnglish (US)
Pages (from-to)2927-2935
Number of pages9
JournalACS Synthetic Biology
Volume9
Issue number11
DOIs
StatePublished - Nov 20 2020

Keywords

  • Computational protein design
  • Machine learning
  • Neural networks
  • Protein engineering

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)

Fingerprint Dive into the research topics of 'Discovery of novel gain-of-function mutations guided by structure-based deep learning'. Together they form a unique fingerprint.

Cite this