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Three-dimensional reconstruction of protein networks provides insight into human genetic disease

Abstract

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their ___location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.

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Figure 1: Disease-associated proteins in the human structural interaction network (hSIN).
Figure 2: Analysis of disease-associated mutations with respect to interaction interfaces.
Figure 3: Analysis of pleiotropy and locus heterogeneity.
Figure 4: Modeling molecular mechanisms of disease genes and mutations through our structurally resolved interaction network.

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Acknowledgements

This work was supported by the Startup Fund from Cornell University (to H.Y.), National Cancer Institute R01 CA098626 (to S.M.L.), R21 CA122937 (to S.M.L.) and a generous donation by M. Bell (to S.M.L.). J.D. is supported by the Tata Graduate Fellowship. We thank A. Paccanaro, K. Salehi-Ashtiani, M.A. Yildirim and the anonymous reviewers for critical reading and constructive comments of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

H.Y. conceived the study, designed all analyses and oversaw all aspects of the project. X. Wang and B.T. performed all computational analyses, interpreted the results and prepared all figures with J.D.'s help. B.T. and J.D. designed the supplementary website. X. Wei, S.M.L. and H.Y. designed all experiments. X. Wei did all experiments and interpreted the results. H.Y. wrote the manuscript with contributions from all authors. X. Wang and B.T. wrote the supplementary materials. X. Wei wrote all parts related to the background, interpretations and discussion of the experimental results and protocols.

Corresponding author

Correspondence to Haiyuan Yu.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Notes 1–17 and Supplementary Figs. 1–25 (PDF 4048 kb)

Supplementary Table 1

List of evidence codes representing binary protein-protein interactions (XLSX 12 kb)

Supplementary Table 2

Binary interactions of the human protein interactome (XLSX 326 kb)

Supplementary Table 3

Structurally resolved human interactome (XLSX 2800 kb)

Supplementary Table 4

List of disease gene predictions (XLSX 26 kb)

Supplementary Table 5

Enrichment in co-expressed and functionally similar protein-protein interaction pairs (XLSX 41 kb)

Supplementary Table 6

Sample size in the calculations (XLSX 41 kb)

Supplementary Table 7

List of primers (XLSX 12 kb)

Supplementary Table 8

Diseases and their associated genes curated from OMIM and HGMD (XLSX 637 kb)

Supplementary Table 9

In-frame indels enriched on interaction interfaces (XLSX 68 kb)

Supplementary Table 10

Missense point mutations enriched on interaction interfaces (XLSX 157 kb)

Supplementary Table 11

SNPs on interaction interfaces of human structural interactome (XLSX 465 kb)

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Wang, X., Wei, X., Thijssen, B. et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat Biotechnol 30, 159–164 (2012). https://doi.org/10.1038/nbt.2106

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