Data-driven protease engineering by DNA-recording and epistasis-aware machine learning.

ABSTRACT: Protein engineering has recently seen tremendous transformation due to machine learning (ML) tools that predict structure from sequence at unprecedented precision. Predicting catalytic activity, however, remains challenging, restricting our capabilities to design protein sequences with desired catalytic function in silico. This predicament is mainly rooted in a lack of experimental methods capable of recording sequence-activity data in quantities sufficient for data-intensive ML techniques, and the inefficiency of searches in the enormous sequence spaces inherent to proteins. Herein, we address both limitations in the context of engineering proteases with tailored substrate specificity. We introduce a DNA recorder for deep specificity profiling of proteases in Escherichia coli as we demons

SUBMITTER: Huber L 

PROVIDER: S-EPMC12217912 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature