Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning.

ABSTRACT: The turnover number k_cat, a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental k_cat estimates are unavailable for the vast majority of enzymatic reactions, the development of accurate computational prediction methods is highly desirable. However, existing machine learning models are limited to a single, well-studied organism, or they provide inaccurate predictions except for enzymes that are highly similar to proteins in the training set. Here, we present TurNuP, a general and organism-independent model that successfully predicts turnover numbers for natural reactions of wild-type enzymes. We constructed model inputs by representing complete chemical reactions through differential reaction fingerprint

SUBMITTER: Kroll A 

PROVIDER: S-EPMC10338564 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature