Resolving context-specific protein-protein interactomes forbiological discovery and therapeutic target prioritisation

Abstract

Protein function is shaped by cellular context, yet most protein representations and interaction maps remain context-agnostic. Here we present ProtScape, a multiscale graph-learning framework integrating global protein interactions, cell-type gene expression and protein language models to learn context-specific representations and infer interactomes across more than 200 cell types. ProtScape substantially outperforms existing approaches in interaction reconstruction, increasing the area under the precision-recall curve by 40 percentage points. Its predicted interactions were supported by held-out continuous STRING global evidence, while its representations recovered higher-order protein organisation. In patient-derived amyotrophic lateral sclerosis motor neurons, ProtScape revealed stage-specific network changes implicating RAB-dependent trafficking as a candidate early disease mechanism. In Parkinson's disease, it recovered clinically supported therapeutic targets from a proteome-wide search space 16-fold smaller than that required by competing representations. Together, ProtScape provides a scalable framework for translating context-specific interactome organisation into experimentally testable disease mechanisms and therapeutic hypotheses.