Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Comprehensive characterization of protein–protein interactions perturbed by disease mutations


Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein–protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Proof of concept of protein–protein interaction–perturbing alleles in human diseases.
Fig. 2: Network perturbation by missense somatic mutations in human cancers.
Fig. 3: Landscape of protein–protein interaction–perturbing mutations across 33 cancer types.
Fig. 4: Pharmacogenomics landscape of protein–protein interaction–perturbing alleles.
Fig. 5: Protein–protein interaction–perturbing alleles in histone H4 complex.
Fig. 6: Experimental investigation of alleles with perturbed physical protein–protein interactions.
Fig. 7: Mutants of RXRA and ALOX5 promote cancer cell growth.

Data availability

All mapping interface mutations, network-predicted oncoPPIs across pan-cancer and 33 individual cancer types, the human protein–protein interactome and predicted drug responses and patient survival analysis are freely available at the websites and Publicly available databases used in the present study include the RCSB protein data bank (, Interactome3D (v.2017.12,, Interactome INSIDER (v.2018.3,, GeneCards (, NCBI (, TCGA GDC Data Portal (, HGMD (, 1000 Genomes (phase 3, 2,504 individuals,, ExAC database (v.0.3.1, 60,706 individuals, and GDSC (

Code availability

All codes written for and used in this study are available from


  1. 1.

    Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Cheng, F., Liang, H., Butte, A. J., Eng, C. & Nussinov, R. Personal mutanomes meet modern oncology drug discovery and precision health. Pharmacol. Rev. 71, 1–19 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Sahni, N. et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Ng, P. K. et al. Systematic functional annotation of somatic mutations in cancer. Cancer Cell 33, 450–462 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Chen, S. et al. An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders. Nat. Genet. 50, 1032–1040 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Menche, J. et al. Disease networks. Uncovering disease–disease relationships through the incomplete interactome. Science 347, 1257601 (2015).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Cheng, F. et al. Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat. Commun. 9, 2691 (2018).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Cheng, F., Kovacs, I. A. & Barabasi, A. L. Network-based prediction of drug combinations. Nat. Commun. 10, 1197 (2019).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Cheng, F. et al. A genome-wide positioning systems network algorithm for in silico drug repurposing. Nat. Commun. 10, 3476 (2019).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Porta-Pardo, E., Garcia-Alonso, L., Hrabe, T., Dopazo, J. & Godzik, A. A pan-cancer catalogue of cancer driver protein interaction interfaces. PLoS Comput. Biol. 11, e1004518 (2015).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Gao, J. et al. 3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets. Genome Med. 9, 4 (2017).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Niu, B. et al. Protein-structure-guided discovery of functional mutations across 19 cancer types. Nat. Genet. 48, 827–837 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Tokheim, C. et al. Exome-scale discovery of hotspot mutation regions in human cancer using 3D protein structure. Cancer Res. 76, 3719–3731 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Kamburov, A. et al. Comprehensive assessment of cancer missense mutation clustering in protein structures. Proc. Natl Acad. Sci. USA 112, E5486–E5495 (2015).

    CAS  PubMed  Google Scholar 

  15. 15.

    Mosca, R. et al. dSysMap: exploring the edgetic role of disease mutations. Nat. Methods 12, 167–168 (2015).

    CAS  PubMed  Google Scholar 

  16. 16.

    Wang, X. et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat. Biotechnol. 30, 159–164 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Rose, P. W. et al. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res. 45, D271–D281 (2017).

    CAS  PubMed  Google Scholar 

  18. 18.

    Mosca, R., Ceol, A. & Aloy, P. Interactome3D: adding structural details to protein networks. Nat. Methods 10, 47–53 (2013).

    CAS  PubMed  Google Scholar 

  19. 19.

    Meyer, M. J. et al. Interactome INSIDER: a structural interactome browser for genomic studies. Nat. Methods 15, 107–114 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Stenson, P. D. et al. The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum. Genet. 136, 665–677 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Genomes Project, C. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Google Scholar 

  22. 22.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Rolland, T. et al. A proteome-scale map of the human interactome network. Cell 159, 1212–1226 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Mullard, A. Nine paths to PCSK9 inhibition. Nat. Rev. Drug Discov. 16, 299–301 (2017).

    CAS  PubMed  Google Scholar 

  25. 25.

    Pandit, S. et al. Functional analysis of sites within PCSK9 responsible for hypercholesterolemia. J. Lipid Res. 49, 1333–1343 (2008).

    CAS  PubMed  Google Scholar 

  26. 26.

    Diedrich, B. et al. Discrete cytosolic macromolecular BRAF complexes exhibit distinct activities and composition. EMBO J. 36, 646–663 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Zillhardt, M. et al. Foretinib (GSK1363089), an orally available multikinase inhibitor of c-Met and VEGFR-2, blocks proliferation, induces anoikis, and impairs ovarian cancer metastasis. Clin. Cancer Res. 17, 4042–4051 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Wang, Y., Shi, J., Chai, K., Ying, X. & Zhou, B. P. The role of Snail in EMT and tumorigenesis. Curr. Cancer Drug Targets 13, 963–972 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Raymond, J. R. Jr., Appleton, K. M., Pierce, J. Y. & Peterson, Y. K. Suppression of GNAI2 message in ovarian cancer. J. Ovarian Res. 7, 6 (2014).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).

    Google Scholar 

  31. 31.

    Koelblinger, P., Thuerigen, O. & Dummer, R. Development of encorafenib for BRAF-mutated advanced melanoma. Curr. Opin. Oncol. 30, 125–133 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Blessing, M. M. et al. Novel BRAF alteration in desmoplastic infantile ganglioglioma with response to targeted therapy. Acta Neuropathol. Commun. 6, 118 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Zhang, J. et al. Whole-genome sequencing identifies genetic alterations in pediatric low-grade gliomas. Nat. Genet. 45, 602–612 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Tripathy, D., Bardia, A. & Sellers, W. R. Ribociclib (LEE011): mechanism of action and clinical impact of this selective cyclin-dependent kinase 4/6 inhibitor in various solid tumors. Clin. Cancer Res. 23, 3251–3262 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Anczukow, O. et al. SRSF1-regulated alternative splicing in breast cancer. Mol. Cell 60, 105–117 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Yan, G. et al. Selective inhibition of p300 HAT blocks cell cycle progression, induces cellular senescence, and inhibits the DNA damage response in melanoma cells. J. Invest. Dermatol. 133, 2444–2452 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Urdinguio, R. G. et al. Chromatin regulation by histone H4 acetylation at lysine 16 during cell death and differentiation in the myeloid compartment. Nucleic Acids Res. 47, 5016–5037 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Yuen, B. T. & Knoepfler, P. S. Histone H3.3 mutations: a variant path to cancer. Cancer Cell 24, 567–574 (2013).

    CAS  PubMed  Google Scholar 

  39. 39.

    Luck, K. et al. A reference map of the human binary protein interactome. Nature 580, 402–408 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Wang, Y. et al. ALOX5 exhibits anti-tumor and drug-sensitizing effects in MLL-rearranged leukemia. Sci. Rep. 7, 1853 (2017).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Tsukasaki, K. et al. Mutations in the mitotic check point gene, MAD1L1, in human cancers. Oncogene 20, 3301–3305 (2001).

    CAS  PubMed  Google Scholar 

  42. 42.

    Pierce, B. G. et al. ZDOCK server: interactive docking prediction of protein–protein complexes and symmetric multimers. Bioinformatics 30, 1771–1773 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Nagata, Y. et al. Variegated RHOA mutations in adult T-cell leukemia/lymphoma. Blood 127, 596–604 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Liang, L. et al. Loss of ARHGDIA expression is associated with poor prognosis in HCC and promotes invasion and metastasis of HCC cells. Int. J. Oncol. 45, 659–666 (2014).

    CAS  PubMed  Google Scholar 

  45. 45.

    Lu, W. et al. Downregulation of ARHGDIA contributes to human glioma progression through activation of Rho GTPase signaling pathway. Tumour Biol. 37, 15783–15793 (2016).

    CAS  PubMed Central  Google Scholar 

  46. 46.

    Evans, R. M. & Mangelsdorf, D. J. Nuclear receptors, RXR, and the big bang. Cell 157, 255–266 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Halstead, A. M. et al. Bladder-cancer-associated mutations in RXRA activate peroxisome proliferator-activated receptors to drive urothelial proliferation. eLife 6, e30862 (2017).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Radmark, O. & Samuelsson, B. 5-Lipoxygenase: mechanisms of regulation. J. Lipid Res. 50, S40–S45 (2009).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Pidgeon, G. P. et al. Lipoxygenase metabolism: roles in tumor progression and survival. Cancer Metastasis Rev. 26, 503–524 (2007).

    CAS  PubMed  Google Scholar 

  50. 50.

    Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).

    CAS  PubMed  Google Scholar 

  51. 51.

    Han, L. et al. The genomic landscape and clinical relevance of A-to-I RNA editing in human cancers. Cancer Cell 28, 515–528 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Sarnowski, C. et al. Impact of rare and common genetic variants on diabetes diagnosis by hemoglobin A1c in multi-ancestry cohorts: the trans-omics for precision medicine program. Am. J. Hum. Genet. 105, 706–718 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Hemnes, A. R. et al. PVDOMICS: a multi-center study to improve understanding of pulmonary vascular disease through phenomics. Circ. Res. 121, 1136–1139 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Consortium, I. T. P.-C. Ao. W. G. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

    Google Scholar 

  55. 55.

    Reardon, S. Giant study poses DNA data-sharing dilemma. Nature 525, 16–17 (2015).

    CAS  PubMed  Google Scholar 

  56. 56.

    Coordinators, N. R. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 44, D7–D19 (2016).

    Google Scholar 

  57. 57.

    Grossman, R. L. et al. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Zhu, Y., Qiu, P. & Ji, Y. TCGA-assembler: open-source software for retrieving and processing TCGA data. Nat. Methods 11, 599–600 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  64. 64.

    Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


We thank S. Tribuna for expert technical assistance. A portion of this work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under contract no. DE-AC52-07NA27344 (release no. LLNL-JRNL-797982). This work was supported by National Institutes of Health (NIH) grant nos. K99 HL138272, R00 HL138272, 3R01AG066707-01S1 and R01AG066707 to F.C. This work was also supported in part by NIH grant nos. U01 HG007690, P50 GM107618, U54 HL119145, R01 HL155107 and R01 HL155096 to J.L., as well as by AHA grant nos. D700382 and CV-19 to J.L. F.C.L. was supported by AHA CRADA no. TC02274.0. C.E. is the Sondra J. and Stephen R. Hardis Endowed Chair in Cancer Genomic Medicine at the Cleveland Clinic, and an ACS Clinical Research Professor. M.V. and D.E.H. were supported by NIH grant nos. P50 HG004233 and U41 HG001715 from NHGRI. This work has been also supported in part by the VeloSano Pilot Program (Cleveland Clinic Taussig Cancer Institute) to F.C.

Author information




J.L. and F.C. conceived the study. F.C., J.Z., Y.W., W.L. and Z.L. performed experiments and data analysis. Y.Z., W.R.M., H.Y., J.H., J.M., R.W., T.H., D.E.H., J.A.C., J.F., Y.H., J.D.L., R.A.K., F.C.L., E.M.A., R.R., C.E. and M.V. interpreted the data analysis. F.C., J.Z., Y.W., W.L. and J.L. drafted the manuscript and critically revised the manuscript. All authors critically revised and gave final approval of the manuscript.

Corresponding author

Correspondence to Joseph Loscalzo.

Ethics declarations

Competing interests

J.L. is the scientific cofounder of Scipher Medicine, Inc., a startup company that uses network medicine to identify biomarkers for disease and specific pathway targets for drug development. M.V. is a shareholder and scientific advisor of seqWell, Inc. and founder and scientific advisor of Gloucester Marine Genomics Institute, Inc. The other authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Leng Han, Ulrich Stelzl and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The 13 selected pan-cancer oncoPPIs with crystal structure-based PPI interface mutations.

The images were prepared by PyMOL ( using the Protein Data Bank (PDB) IDs (highlighted in figures) downloaded from PDB database ( Structural views of all oncoPPIs in pan-cancer and individual cancer types/subtypes are freely available:

Extended Data Fig. 2 Survival analyses of p53-SRSF1 PPI perturbing-mutations and p53 mutations alone.

Three exemplary cancer types, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), and colon adenocarcinoma (COAD), are illustrated. Survival analyses of p53-SRSF1 PPI perturbing-mutations across other cancer types/subtypes are provided in Supplementary Fig. 14. The p-value (P) was computed by log-rank test. All oncoPPI-predicted survival analyses for 33 cancer types/subtypes are freely available at the following website:

Supplementary information

Supplementary Information

Supplementary Figs. 1–22 and Table 1

Reporting Summary

Supplementary Data 1

Lists of oncoPPIs in pan-cancer and individual cancer analyses

Supplementary Data 2

Landscape of pharmacogenomics predicted by PPI-perturbing mutations in cancer cell lines

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cheng, F., Zhao, J., Wang, Y. et al. Comprehensive characterization of protein–protein interactions perturbed by disease mutations. Nat Genet 53, 342–353 (2021).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing