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Nat Commun. 2019 Mar 18;10(1):1240. doi: 10.1038/s41467-019-09177-y.

Network-based prediction of protein interactions.

Author information

1
Network Science Institute and Department of Physics, Northeastern University, Boston, MA, 02115, USA. i.kovacs@northeastern.edu.
2
Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA. i.kovacs@northeastern.edu.
3
Wigner Research Centre for Physics, Institute for Solid State Physics and Optics, H-1525, Budapest, P.O.Box 49, Hungary. i.kovacs@northeastern.edu.
4
Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA.
5
Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
6
Donnelly Centre, Toronto, Ontario, Canada, Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada, Department of Computer Science, University of Toronto, Toronto, Ontario, Canada, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
7
Network Science Institute and Department of Physics, Northeastern University, Boston, MA, 02115, USA. a.barabasi@northeastern.edu.
8
Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02115, USA. a.barabasi@northeastern.edu.
9
Division of Network Medicine and Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. a.barabasi@northeastern.edu.
10
Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary. a.barabasi@northeastern.edu.

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.

PMID:
30886144
PMCID:
PMC6423278
DOI:
10.1038/s41467-019-09177-y
[Indexed for MEDLINE]
Free PMC Article

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