Enzyme structure correlates with variant effect predictability.

ABSTRACT: Protein engineering increasingly relies on machine learning models to computationally pre-screen promising novel candidates. Although machine learning approaches have proven effective, their performance on prospective screening data leaves room for improvement; prediction accuracy can vary greatly from one protein variant to the next. So far, it is unclear what characterizes variants that are associated with large prediction error. In order to establish whether structural characteristics influence predictability, we created a novel high-order combinatorial dataset for an enzyme spanning 3,706 variants, that can be partitioned into subsets of variants with mutations at positions exclusively belonging to a particular structural class. By training four different supervised variant effect pred

SUBMITTER: van der Flier F 

PROVIDER: S-EPMC11491678 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature