Network-based prediction of protein interactions

István A. Kovács*, Katja Luck, Kerstin Spirohn, Yang Wang, Carl Pollis, Sadie Schlabach, Wenting Bian, Dae Kyum Kim, Nishka Kishore, Tong Hao, Michael A. Calderwood, Marc Vidal, Albert László Barabási

*Corresponding author for this work

Research output: Contribution to journalArticle

39 Scopus citations

Abstract

Despite exceptional experimental efforts to map out the human interactome, the continued data incompleteness limits our ability to understand the molecular roots of human disease. Computational tools offer a promising alternative, helping identify biologically significant, yet unmapped protein-protein interactions (PPIs). While link prediction methods connect proteins on the basis of biological or network-based similarity, interacting proteins are not necessarily similar and similar proteins do not necessarily interact. Here, we offer structural and evolutionary evidence that proteins interact not if they are similar to each other, but if one of them is similar to the other’s partners. This approach, that mathematically relies on network paths of length three (L3), significantly outperforms all existing link prediction methods. Given its high accuracy, we show that L3 can offer mechanistic insights into disease mechanisms and can complement future experimental efforts to complete the human interactome.

Original languageEnglish (US)
Article number1240
JournalNature communications
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

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    Kovács, I. A., Luck, K., Spirohn, K., Wang, Y., Pollis, C., Schlabach, S., Bian, W., Kim, D. K., Kishore, N., Hao, T., Calderwood, M. A., Vidal, M., & Barabási, A. L. (2019). Network-based prediction of protein interactions. Nature communications, 10(1), [1240]. https://doi.org/10.1038/s41467-019-09177-y