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Nat Commun. 2019 Mar 8;10(1):1118. doi: 10.1038/s41467-019-09123-y.

Topological scoring of protein interaction networks.

Author information

1
Stowers Institute for Medical Research, Kansas City, MO, 64110, USA.
2
Boehringer Ingelheim Vetmedica, St. Joseph, MO, 64506, USA.
3
Thermo Fisher Scientific, Waltham, MA, 02451, USA.
4
Department of Cell and Molecular Biology, University of Rhode Island, 287 CBLS, 120 Flagg Road, Kingston, RI, 02881, USA.
5
Stowers Institute for Medical Research, Kansas City, MO, 64110, USA. mpw@stowers.org.
6
Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS, 66160, USA. mpw@stowers.org.

Abstract

It remains a significant challenge to define individual protein associations within networks where an individual protein can directly interact with other proteins and/or be part of large complexes, which contain functional modules. Here we demonstrate the topological scoring (TopS) algorithm for the analysis of quantitative proteomic datasets from affinity purifications. Data is analyzed in a parallel fashion where a prey protein is scored in an individual affinity purification by aggregating information from the entire dataset. Topological scores span a broad range of values indicating the enrichment of an individual protein in every bait protein purification. TopS is applied to interaction networks derived from human DNA repair proteins and yeast chromatin remodeling complexes. TopS highlights potential direct protein interactions and modules within complexes. TopS is a rapid method for the efficient and informative computational analysis of datasets, is complementary to existing analysis pipelines, and provides important insights into protein interaction networks.

PMID:
30850613
PMCID:
PMC6408525
DOI:
10.1038/s41467-019-09123-y
[Indexed for MEDLINE]
Free PMC Article

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