MPEK: a multi-task learning based on pre-trained language model for predicting enzymatic reaction kinetic parameters

Abstract Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (kcat) and Michaelis constant (Km), key kinetic parameters for measuring enzyme catalytic efficiency are crucial for analyzing enzymatic reaction mechanisms and the directed evolution of target enzymes. Experimental determination of kcat and Km is costly in terms of time, labor, and cost. To consider the intrinsic connection between kcat and Km and further improve the prediction performance, we propose a universal pre-trained multi-task deep learning model, MPEK, to predict these parameters simultaneously while considering pH, temperature, and organismal information. MPEK achieved superior predictive performance on the whole test dataset. Using the same test dataset, MPEK outperformed other state-of-the-art models. More importantly, MPEK was able to reveal enzyme promiscuity and was sensitive to slight changes in the mutant enzyme sequence. In addition, in three case studies, it was shown MPEK has the potential for assisted enzyme mining and directed evolution. To facilitate in silico evaluation of enzyme catalytic efficiency, we have established a web server implementing this model (http://mathtc.nscc-tj.cn/mpek).