Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Abstract

Enzyme turnover numbers ( k _cat) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured k _cat data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput k _cat prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture k _cat changes for mutated enzymes and identify amino acid residues with a strong impact on k _cat values. We applied this approach to predict genome-scale k _cat values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted k _cat values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale.