Format

Send to

Choose Destination
Elife. 2016 Oct 22;5. pii: e18715. doi: 10.7554/eLife.18715.

A computational interactome and functional annotation for the human proteome.

Author information

1
Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States.
2
School of Software, Central South University, Changsha, China.
3
Department of Microbiology and Immunology, Columbia University, New York, United States.
4
Howard Hughes Medical Institute, Columbia University, New York, United States.
5
Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States.
6
Department of Medicine, Columbia University, New York, United States.
7
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.

Abstract

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein's function. We provide annotations for most human proteins, including many annotated as having unknown function.

KEYWORDS:

computational biology; function annotation; human; machine learning; protein interactions; systems biology

PMID:
27770567
PMCID:
PMC5115866
DOI:
10.7554/eLife.18715
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for eLife Sciences Publications, Ltd Icon for PubMed Central
Loading ...
Support Center