Robust Prediction of Enzyme Variant Kinetics with RealKcat.

ABSTRACT: Accurate prediction of kinetic parameters is crucial for understanding known and tailoring novel enzymes for biocatalysis. Current models fail to capture mutation effects on catalytically essential residues, limiting their utility in enzyme design. We grid-searched through ten model architectures (25,671 hyperparameter combinations) to identify a gradient-based additive framework called RealKcat, trained on 27,176 experimental entries curated manually (KinHub-27k) by screening 2,158 articles. Clustering catalytic turnover (k _cat ) and substrate affinity (K _M ) by rational orders of magnitude, RealKcat achieves >85% test accuracy, demonstrating highest sensitivity to mutation-induced variability thus far, and is the first-of-its-kind-model t

SUBMITTER: Anna Sajeevan K 

PROVIDER: S-EPMC11844551 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature