From Pairs to Networks: Motif Graph Prompt Tuning for Inductive Protein–Protein Interaction Prediction

Predicting protein–protein interactions (PPIs) is essential for reconstructing the networks that organize cellular function. Predictors trained on individual pairs can score pairs accurately yet assemble networks with distorted topology, and supplying network context is hardest when both queried proteins are unseen and have no recorded interactions. Our key idea is that topology, although unavailable for the queried proteins, is fully observed during training, so it can serve as supervision rather than as input, at two levels. We propose GraSPPI (GRAph Structural Prompting for PPI), a motif graph prompt tuning method inspired by prompt tuning in language, vision-language, and graph models. At the pair level, it infers from the two sequences a virtual motif graph of the pair's triadic closure, the shared partners that interacting proteins tend to have; this graph is learned against the true motif graphs of the training network and read as a prompt for the interaction decision. At the network level, structural losses regularise training: the decisions on sampled training subgraphs are scored jointly, so that the network they form follows the structure of the training network, while every test pair is still scored independently. Extensive experiments demonstrate that GraSPPI is effective in reconstructing held-out interaction networks, with the lowest clustering discrepancy among the compared methods and the highest complex-pathway recall.