Machine Learning Gap-Fills Missing Transporter Kinetics in Biosystems Across Scales
TL;DR
The first compound-protein interaction machine learning model of transporter Vmax and Km, MMTKPred is developed, which captures the effects of point mutations and substrate changes on transporters and effectively models metabolite transport spanning from molecular to multi- species scales.
Abstract
Understanding transporter kinetics is essential for deciphering metabolite exchanges in biosystems, particularly for cells subject to substrate gradients. Nevertheless, the prediction of transporter kinetic parameters, maximum rate per gram protein (Vmax) and Michaelis-Menten constant (Km), has not yet been tackled. Here, we developed the first compound-protein interaction machine learning model of transporter Vmax and Km, MMTKPred, which achieved R2=0.553, RMSE=1.155 mmol/hr/g Protein and R2=0.330, RMSE=0.935 mM for log10-scaled Vmax and Km prediction, respectively. Moreover, we demonstrated MMTKPred’s predictive power across biosystem scales, from capturing transporter kinetics modulated by point mutations and substrate changes at the molecular level, to enabling substrate-sensitive metabolic modelling of non-model yeasts at the cellular level, and rationalizing inter-species substrate competition in co-cultures. Collectively, MMTKPred effectively models metabolite transport spanning from molecular to multi- species scales, thereby offering a computational tool for rational microbial cell factory optimization. Graphical abstract Highlights MMTKPred, first transporter kinetics CPI model, reaches ∼1 log10 RMSE for Vmax and Km. MMTKPred captures the effects of point mutations and substrate changes on transporters. Predicted kinetics enables substrate sensitivity in metabolic flux modelling. Predicted kinetics explains inter-species substrate competition outcomes.