ACS Omega. 2026 Aug 27;11(35):52427-52438. doi: 10.1021/acsomega.5c11697. eCollection 2026 Sep 8.

ABSTRACT

Promiscuous enzymes catalyze multiple biochemical reactions, but predicting their substrate profiles remains challenging because annotations are incomplete, reliable negative labels are scarce, and enzyme-substrate relationships are inherently multilabel. Here, we present PreSEPM (Predictive Substrate Explorer for Promiscuous Enzymes), a sequence-only, closed-set framework that combines pretrained protein representations, Gaussian mixture modeling, cluster-substrate alignment, and Bayesian optimization of decision thresholds. PreSEPM operates within a predefined substrate panel and does not require explicit substrate or structural descriptors. On a UniProt-derived triacylglycerol lipase data set, PreSEPM achieved an AUROC of 0.86, an AUPRC of 0.73, and a maximum F1 score of 0.71, outperforming the evaluated conventional machine learning baselines. On an independent high-throughput lipase screen, PreSEPM also outperformed a strong single-task logistic-regression baseline under enzyme-wise cross-validation, increasing Macro-AUPRC from 0.65 to 0.68 and F1 max from 0.63 to 0.69. Under the cross-data set setting, SMOTE-based augmentation increased AUPRC from 0.62 to 0.70. These results support PreSEPM as a useful sequence-based baseline for substrate profile completion and experimental prioritization within promiscuous enzyme families when annotations are sparse and structural or ligand-level information is unavailable.

PMID:42729836 | PMC:PMC13563996 | DOI:10.1021/acsomega.5c11697