PCIPG2.0: Multi-omics fusion and structure-aware graph autoencoding for protein complex identification

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ID: 322525
2026
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Abstract
Abstract Motivation Protein complexes execute cellular functions, yet identifying them from protein–protein interaction (PPI) networks remains challenging because interactomes are incomplete and purely topology-driven clustering often lacks mechanistic interpretability. Here we present PCIPG 2.0, an unsupervised framework that explicitly addresses two major bottlenecks in PPI-based complex discovery: missing interactions and limited mechanistic specificity. PCIPG 2.0 first fuses multiple omics views to prioritize high-confidence candidate protein associations and enhance the observed interactome, and then learns structure-aware node representations by aggregating residue embeddings on residue graphs, coupled with a PPI-level graph autoencoder to infer latent complex memberships. Results Across five yeast benchmarks, PCIPG 2.0 consistently improves complex recovery over representative baselines and yields predicted complexes with significantly higher Gene Ontology semantic coherence than size-matched random sets. Literature-supported case studies and AlphaFold3-based assembly analyses further suggest that representative predictions are consistent with functionally coherent and structurally plausible protein assemblies. Together, these results suggest that combining multi-omics-driven interactome completion with residue-informed representation learning provides a useful and mechanistically informed framework for protein complex identification under incomplete interactome measurements. Availability The source code is available at GitHub https://github.com/hyx-1/PCIPG2.0. The complete reproducibility package, including the code, processed data, configuration files and materials required to reproduce the experiments reported in this manuscript, has been archived on Zenodo with an archival DOI https://doi.org/10.5281/zenodo.20133228. The GitHub repository also provides the README-based usage instructions.
Reference Key
openalex_W7171206462 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yixiang Huang, J. Q. Wang, Lei Yang, Xinqi Gong
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag552
URL
https://doi.org/10.1093/bioinformatics/btag552
Keywords Keywords not found

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