Bayesian optimization with evolutionary and structure-based regularization for directed protein evolution.

ABSTRACT:

Background

Directed evolution (DE) is a technique for protein engineering that involves iterative rounds of mutagenesis and screening to search for sequences that optimize a given property, such as binding affinity to a specified target. Unfortunately, the underlying optimization problem is under-determined, and so mutations introduced to improve the specified property may come at the expense of unmeasured, but nevertheless important properties (ex. solubility, thermostability, etc). We address this issue by formulating DE as a regularized Bayesian optimization problem where the regularization term reflects evolutionary or structure-based constraints.

Results

We applied our approach to DE to three representative proteins, GB1, BRCA1, and SARS-CoV-2 Spike, and evaluated both

SUBMITTER: Frisby TS 

PROVIDER: S-EPMC8246133 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature