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 language | English (US) |
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Article number | 1240 |
Journal | Nature communications |
Volume | 10 |
Issue number | 1 |
DOIs | |
State | Published - Dec 1 2019 |
Funding
We thank D. Hill, F.P. Roth, F. Cheng and M. Santolini for useful discussions on the manuscript and A. Grishchenko for help with visualization. This work was supported by an NHGRI Center of Excellence in Genome Science grant P50HG004233 to M.V., and A.-L.B; an NHGRI U41HG001715 awarded to M.V., and M.A.C.; and an NIGMS R01GM109199 awarded to M.A.C. A.-L.B. and I.A.K were also funded by P01HL132825. We gratefully acknowledge the support of The National Human Genome Research Institute (NHGRI) and National Institute of General Medical Sciences (NIGMS) of NIH.
ASJC Scopus subject areas
- General Chemistry
- General Biochemistry, Genetics and Molecular Biology
- General Physics and Astronomy