Machine Learning Gap-Fills Missing Transporter Kinetics in Biosystems Across 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 MMTKPreds 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 O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/735998v2_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@18e7bb6org.highwire.dtl.DTLVardef@15bec93org.highwire.dtl.DTLVardef@8ce92org.highwire.dtl.DTLVardef@31fb44_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMMTKPred, first transporter kinetics CPI model, reaches [~]1 log10 RMSE for Vmax and Km. C_LIO_LIMMTKPred captures the effects of point mutations and substrate changes on transporters. C_LIO_LIPredicted kinetics enables substrate sensitivity in metabolic flux modelling. C_LIO_LIPredicted kinetics explains inter-species substrate competition outcomes. C_LI