DecoyOpt: A two-stage pipeline for decoy-aware enzyme discrimination and EC number prediction

Abstract

Accurate and efficient EC number prediction is essential for elucidating the catalytic roles of enzymes. Although similarity-based and deep learning-based approaches have advanced this task, they often fail to distinguish active enzymes from closely related decoys inactivated by local functional-site mutations, leading to high false-positive rates (FPRs). We propose DecoyOpt, a two-stage pipeline for decoy-aware enzyme discrimination and EC number prediction. Specifically, we generated putatively inactive functional-site decoys using ESM3 and evaluated 16 protein language models across three splits. An MLP trained with classification and ranking losses distinguishes enzymes from unannotated proteins and decoys before a contrastive learning model assigns EC numbers to predicted enzymes. On the most stringent GraphPart 40% split, DecoyOpt achieves an MCC of 0.899 and a D0 decoy FPR of 6.17%, outperforming retrained baselines. Across all splits, it outperforms these baselines in EC number prediction on the full test set while maintaining performance close to CLEAN on the enzyme subset. It also correctly annotates all four wild-type enzymes and rejects all eight inactive or nearly inactive variants. We believe that DecoyOpt has potential for annotating uncharacterized proteins and their variants, with applications in genomics, synthetic biology, and biocatalysis.