Machine Learning Gap-Fills Missing Transporter Kinetics in Biosystems Across Scales

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 (V _max ) and Michaelis-Menten constant (K _m ), has not yet been tackled. Here, we developed the first compound-protein interaction machine learning model of transporter V _max and K _m , MMTKPred, which achieved R ² =0.553, RMSE=1.155 mmol/hr/g Protein and R ² =0.330, RMSE=0.935 mM for log10-scaled V _max and K _m 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.