A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-Dependent <i>k</i>_cat/<i>K</i>_m Prediction in β-Glucosidases.

ABSTRACT: The catalytic activity of enzymes is intricately determined by their amino acid sequences and assay conditions, particularly temperature. Navigating the complex interplay among sequence, temperature, and catalytic function is crucial for unlocking a multitude of enzyme applications. Machine learning has recently emerged as a tool for quantitative prediction of enzyme activity from protein sequences. Unfortunately, ML models designed to predict the comprehensive enzyme activity parameter, k_cat/K_m, from protein sequences are rare compared to those predicting k_cat or K_m alone. Combining both protein sequence and temperature as input features further challenges predictions; no current ML models capture the nonlinear relati

SUBMITTER: Erkanli ME 

PROVIDER: S-EPMC12538591 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature