Predicting Enzyme Turnover Numbers and Enabling Rational Enzyme Evolution

ABSTRACT Enzyme turnover number () is a central kinetic parameter for biocatalysis, but experimental determination is low‐throughput and existing computational methods inadequately model enzyme–reaction interplay. Here, we introduced MCKcat, a deep learning framework that integrates multi‐scale convolutional feature extraction and cross‐attention to enable deep, reciprocal fusion of enzyme sequence and reaction fingerprint representations for accurate prediction. We constructed MCKcat‐DB, a large‐scale dataset comprising 33 396 enzyme‐reaction‐based data points, covering both wild‐type enzymes and a large number of mutants. MCKcat demonstrated competitive performance across diverse prediction scenarios on two benchmarks. A two‐step strategy was developed to engineer Bacillus aryabhattai laccase with synergistically improved thermostability and catalytic activity. Rational design generated 217 candidate thermostable mutants, followed by MCKcat‐based screening that identified 20 hits. Validation showed 15 mutants (75% positive rate) exhibited simultaneous enhancements in both thermostability and . A user‐friendly web server was provided to facilitate broad adoption. MCKcat establishes a robust, generalizable strategy for data‐driven prediction and artificial intelligence‐assisted enzyme engineering.