Partner-specific protein-protein binding site prediction, identifying which residues of a protein form the interface when bound to a specific partner, remains a challenging task with significant implications for drug discovery and understanding of protein structure and function. Existing computational methods are limited by small training datasets, inconsistent redundancy filtering, and reliance on three-dimensional structural information at test time. Here we present a sequence-only, partner-specific protein-protein interface predictor called Handshake. It combines ProstT5, a protein language model pre-trained on structural data, with Low-Rank Adaptation (LoRA), a cross-chain attention mechanism and a contact supervision head. Our method can detect both binding interfaces and pairwise contact matrices. We trained our model on very large datasets of non-redundant protein-protein pairs derived from the PPInterface dataset, the most comprehensive structural protein-protein database to date, and evaluated it on systematically filtered benchmarks at four redundancy thresholds (30%--90% sequence identity). We demonstrate that sequence redundancy inflates reported AUROC by up to 0.079 and MCC by up to 0.145 on identical models, representing a substantial methodological confound in the field. Even at 30% redundancy threshold, our results (AUROC=0.811, MCC=0.367, F1=0.45) exceed the best published sequence-only result on this convention. Our method also achieves comparable performance to existing partner-specific methods that use explicit structural information. The comprehensive training and evaluation dataset, in addition to the systematic redundancy inflation, can help gain insight into protein-protein interactions and the abilities and limitations of current detection methods.
Haspel, N.
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