Authors
Dapeng Xiong, Yunguang Qiu, Junfei Zhao, Yadi Zhou, Dongjin Lee, Shobhita Gupta, Mateo Torres, Weiqiang Lu, Siqi Liang, Jin Joo Kang, Charis Eng, Joseph Loscalzo, Feixiong Cheng, Haiyuan Yu
Published in
Nature biotechnology. Volume 43. Issue 9. Pages 1510-1524. Epub Oct 24, 2024.
Abstract
To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.
PMID:
39448882
Bibliographic data and abstract were imported from PubMed on 13 Sep 2026.
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