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A structurally informed human protein–protein interactome reveals proteome-wide perturbations caused by disease mutations

Nature Biotechnology volume 43, pages 1510–1524 (2025)Cite this article

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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.

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Fig. 1: Overview of the PIONEER framework.
Fig. 2: PIONEER-predicted PPI alleles are enriched in disease-associated mutations.
Fig. 3: A landscape of oncoPPIs identified by PIONEER across 33 cancer types (~11,000 cancer genomes).
Fig. 4: PIONEER-predicted oncoPPIs are associated with patient survival.
Fig. 5: PIONEER-predicted PPI-perturbing tumor alleles in ubiquitination by E3 ligases.

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Data availability

Mutation data from the TCGA study were downloaded from the National Cancer Instituteʼs Genomic Data Commons (https://portal.gdc.cancer.gov). The MSK MetTropism dataset was downloaded from the cBioPortal (https://www.cbioportal.org/study/summary?id=msk_met_2021). Variant data from the 1000 Genomes Project were downloaded from the National Center for Biotechnology Informationʼs FTP site (https://ftp-trace.ncbi.nih.gov/1000genomes/ftp). The ExAC dataset was downloaded from the Genome Aggregation Database (https://gnomad.broadinstitute.org/downloads#exac-variants). Variants collected by the HGMD were downloaded from https://www.hgmd.cf.ac.uk/ac/index.php. Genomic variants and drug response data of human cancer cell lines were downloaded from GDSC datasets (https://www.cancerrxgene.org/downloads/bulk_download). Genomic profiling of PDXs and drug response curve metrics of PDX clinical trials were downloaded from Supplementary Table 1 of the corresponding paper (https://www.nature.com/articles/nm.3954#Sec28). The homologous structures of PPIs that do not have co-crystal structures were collected from Interactome3D (https://interactome3d.irbbarcelona.org). The ModBase data were downloaded from https://modbase.compbio.ucsf.edu. The PDB data were downloaded from the PDB FTP site (https://files.wwpdb.org/pub/pdb/data/structures/divided/pdb). The AlphaFold2-predicted protein structures were download from the AlphaFold2 database (https://alphafold.ebi.ac.uk). All other data supporting the results in this study are available in the supplementary materials and at https://pioneer.yulab.org. Source data are provided with this paper.

Code availability

The source code of PIONEER is available at GitHub126.

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Acknowledgements

This work was supported by the National Institute of General Medical Sciences (R01GM124559, R01GM125639, R01GM130885 and RM1GM139738) and the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK115398) to H.Y.; the National Institute on Aging (R01AG084250, R56AG074001, U01AG073323, R01AG066707, R01AG076448, R01AG082118, RF1AG082211 and R21AG083003) and the National Institute of Neurological Disorders and Stroke (RF1NS133812) to F.C.; and the National Human Genome Research Institute (U01HG007691), the National Heart, Lung, and Blood Institute (R01HL155107, R01HL155096, R01HL166137 and U54HL119145), the American Heart Association (AHA957729 and 24MERIT1185447) and European Union Horizon Health 2021 (101057619) to J.L. C.E. is the Sondra J. and Stephen R. Hardis Chair of Cancer Genomic Medicine at the Cleveland Clinic. This work partially used Jetstream2 at Indiana University through allocation BIO220060 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services and Support program, which is supported by the National Science Foundation (2138259, 2138286, 2138307, 2137603 and 2138296).

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Author notes

  1. These authors contributed equally: Dapeng Xiong, Yunguang Qiu, Junfei Zhao, Yadi Zhou, Dongjin Lee.

Authors and Affiliations

  1. Department of Computational Biology, Cornell University, Ithaca, NY, USA

    Dapeng Xiong, Dongjin Lee, Mateo Torres, Siqi Liang, Jin Joo Kang & Haiyuan Yu

  2. Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA

    Dapeng Xiong, Dongjin Lee, Shobhita Gupta, Mateo Torres, Siqi Liang, Jin Joo Kang & Haiyuan Yu

  3. Center for Innovative Proteomics, Cornell University, Ithaca, NY, USA

    Dapeng Xiong, Shobhita Gupta, Mateo Torres, Jin Joo Kang & Haiyuan Yu

  4. Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Yunguang Qiu, Yadi Zhou, Charis Eng & Feixiong Cheng

  5. Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Yunguang Qiu, Yadi Zhou & Feixiong Cheng

  6. Department of Systems Biology, Herbert Irving Comprehensive Center, Columbia University, New York, NY, USA

    Junfei Zhao

  7. Biophysics Program, Cornell University, Ithaca, NY, USA

    Shobhita Gupta

  8. Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China

    Weiqiang Lu

  9. Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA

    Charis Eng & Feixiong Cheng

  10. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Charis Eng & Feixiong Cheng

  11. Channing Division of Network Medicine, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Joseph Loscalzo

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Contributions

D.X., Y.Q., J.Z., Y.Z., D.L., F.C. and H.Y. conceived and developed the project. Under close supervision of H.Y., D.X., D.L. and S.L. developed the models and conducted computational experiments; and D.X., S.G., M.T. and S.L. built the web server. W.L. and J.K. conducted biological experiments. D.X., Y.Q., J.Z., Y.Z., D.L., F.C. and H.Y. performed the analyses. D.X., Y.Q., J.Z., Y.Z., D.L., S.G., C.E., J.L., F.C. and H.Y. wrote and critically revised the manuscript. All authors discussed the results and reviewed the manuscript.

Corresponding authors

Correspondence to Feixiong Cheng or Haiyuan Yu.

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Competing interests

J.L. is co-scientific founder of Scipher Medicine, Inc., which applies network medicine strategies to biomarker development and personalized drug selection. The remaining authors declare no competing interests.

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Nature Biotechnology thanks Leng Han and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 PIONEER provides high-quality interfaces for the whole proteome.

a, Workflow for compiling interactome PIONEER. The interfaces calculated from experimentally determined co-crystal structures or homology models are primarily used, the remaining unresolved interactions are predicted by PIONEER. b, Percentage of CAPRI decoys having a given average PIONEER prediction score at interfaces. Percentages are plotted along the y axis for 4 classes of CAPRI models. The total number of models in each class is indicated in the text in the figure. c, Fraction of interactions disrupted by random population variants in PIONEER-predicted and known interfaces. The error bar denotes standard error for the binomial distribution. Significance was determined by two-sided z-test. The n numbers are shown in Supplementary Table 6. d, Enrichment of disease-associated mutations in PIONEER-predicted and known interfaces. The error bar denotes standard error for the log odds ratio. Significance was determined by two-sided z-test. The n numbers are shown in Supplementary Table 7. e, Enrichment of population variants in PIONEER-predicted and known interfaces. The error bar denotes standard error for the log odds ratio. The n numbers are shown in Supplementary Table 8.

Extended Data Fig. 2 Pharmacogenomic landscape identified by the PIONEER-predicted interactome network.

a, Drug responses evaluated by oncoPPIs in the PDX models. Effect size was quantified by Cohen’s d statistic using the difference between two means divided by a pooled s.d. for the data. Significance was determined by ANOVA adjusted by Benjamini-Hochberg method. b, Circos plot displaying drug responses evaluated by putative PIONEER-predicted oncoPPIs harboring a statistically significant excess number of missense mutations at PPI interfaces, following a binomial distribution across selected anti-cancer therapeutic agents in cancer cell lines. Each node denotes a specific oncoPPI. Node size denotes significance determined by ANOVA. Effect size was quantified by Cohen’s d statistic using the difference between two means divided by a pooled s.d. for the data. Node color denotes three different types of PPIs: (1) PDB: Red; (2) HM: Blue; and (3) PIONEER: Green. ‘HM’ represents homolog models. c, Highlighted examples of drug responses. Data are represented as a box plot with an underlaid violin plot in which the middle line is the median, the lower and upper edges of the box are the first and third quartiles, the whiskers represent IQR × 1.5, and the dots are outlier points. Significance was determined by ANOVA. The n numbers are shown in Supplementary Table 16.

Extended Data Fig. 3 Proteogenomics of the PIONEER-predicted interactome network.

a, Phosphorylation-associated PPI-perturbing mutations altered the proteomic changes in COAD and UCEC. The abundance of proteins was quantified using the TMT technique. Data are represented as a box plot with an underlaid violin plot in which the middle line is the median, the lower and upper edges of the box are the first and third quartiles, the whiskers represent IQR × 1.5, and the dots are outlier points. Significance was determined by two-tailed Wilcoxon rank-sum test. The n numbers are shown in Supplementary Table 17. b, Phosphorylation-associated PPI-perturbing mutations in the EGFR–RAS–RAF–MEK–ERK cascade signaling pathway. The whole transmembrane EGFR structures were constructed by three crystal structures (PDB: 3NJP, 2M20, 2GS6). The membrane model is shown in green. The phosphorylation sites are indicated by the symbol 'P'. The detailed interface structure of SOS1–KRAS is also shown in the inset. The key mutated residue Gln61 on KRAS forms a hydrogen bond (purple dashed line) with residue Thr935 on SOS1, and Tyr884 on SOS1 is involved in a cation-π interaction (red dash line) with residue Arg73 on KRAS. Two subunits of RAF protein structure models were built by RAF1 and BRAF, separately (PDB: 6VJJ and 6Q0J). The two subunits are connected by a disordered loop indicated by blue cartoon lines. Two heterodimers of KRAS–RAF1 and BRAF–MEK1 constitutes the KRAS–RAF–MEK1 complex. PDB ID of each complex structure model is provided. c, Highlighted examples of drug responses. Data are represented as a box plot with an underlaid violin plot in which the middle line is the median, the lower and upper edges of the box are the first and third quartiles, the whiskers represent IQR × 1.5, and the dots are outlier points. Significance was determined by ANOVA. The n numbers are shown in Supplementary Table 16.

Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Figs. 1–14.

Reporting Summary

Supplementary Table 1

Supplementary Tables 1–18.

Supplementary Data 1

The labeled dataset used for PIONEER model training, validation and testing.

Supplementary Data 2

Somatic mutations in 33 cancer types of TCGA.

Supplementary Data 3

Significance test of somatic mutation enrichment in PPI interfaces by 33 cancer types in TCGA.

Source data

Source Data Fig. 4

Unprocessed western blots for Fig. 4d.

Source Data Fig. 5

Unprocessed western blots for Fig. 5d.

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Xiong, D., Qiu, Y., Zhao, J. et al. A structurally informed human protein–protein interactome reveals proteome-wide perturbations caused by disease mutations. Nat Biotechnol 43, 1510–1524 (2025). https://doi.org/10.1038/s41587-024-02428-4

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