Resolving context-specific protein-protein interactomes forbiological discovery and therapeutic target prioritisation
- 1University of Bern
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.
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BibTeX
@misc{thomas2026resolving,
title = {{Resolving context-specific protein-protein interactomes forbiological discovery and therapeutic target prioritisation}},
author = {A. Thomas and L. Fournier and V. Jung and R. Patani and P. Frossard and R. Luisier and C. Vincent-Cuaz},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.08.750135},
url = {https://www.biorxiv.org/content/10.64898/2026.09.08.750135v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Resolving context-specific protein-protein interactomes forbiological discovery and therapeutic target prioritisation
AU - A. Thomas
AU - L. Fournier
AU - V. Jung
AU - R. Patani
AU - P. Frossard
AU - R. Luisier
AU - C. Vincent-Cuaz
PY - 2026
DA - 2026/09/08
PB - bioRxiv
DO - 10.64898/2026.09.08.750135
UR - https://www.biorxiv.org/content/10.64898/2026.09.08.750135v1
AB - 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.
ER -