Pretrained language models and weight redistribution achieve precisekcatprediction

Abstract

AbstractThe enzyme turnover number (kcat) is a meaningful and valuable kinetic parameter, reflecting the catalytic efficiency of an enzyme to a specific substrate, which determines the global proteome allocation, metabolic fluxes and cell growth. Here, we present a precisekcatprediction model (PreKcat) leveraging pretrained language models and a weight redistribution strategy. PreKcat significantly outperforms the previouskcatprediction method in terms of various evaluation metrics. We also confirmed the ability of PreKcat to discriminate enzymes of different metabolic contexts and different types. Additionally, the proposed weight redistribution strategies effectively reduce the prediction error of highkcatvalues and capture minor effects of amino acid substitutions on two crucial enzymes of the naringenin synthetic pathway, leading to obvious distinctions. Overall, the presentedkcatprediction model provides a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzymology and biomedical applications.