X-PAIR: an ultrafast multitask framework for proteome-scale reconstruction of PPI networks and partner-specific interfaces from sequence

Protein-protein interaction prediction and residue-level interface localisation are biologically intertwined but usually treated as separate computational problems. Here we present X-PAIR, a sequence-based multitask deep learning framework that jointly predicts whether two proteins interact and identifies their partner-specific interface residues. By combining protein language-model representations with lightweight cross-attention, X-PAIR requires neither structural templates nor multiple-sequence alignments. Across leakage-controlled benchmarks, it outperforms existing methods in both tasks, with substantial gains in interface localisation. Multitask learning preserves single-task performance while returning both outputs at near-single-task cost, enabling one million protein pairs to be analysed in under two hours--approximately 500-fold faster for interface prediction and 20-fold faster for PPI prediction than current approaches--thereby enabling proteome-scale analysis. Cross-species analyses reveal distinct evolutionary dependencies: interaction prediction benefits from multispecies training, whereas interface localisation remains robust across taxonomic scales. X-PAIR thus links proteome-scale interaction discovery to the residue-level determinants of partner-specific molecular recognition.