Topological scoring of protein interaction networks

Mihaela E. Sardiu, Joshua M. Gilmore, Brad D. Groppe, Arnob Dutta, Laurence Florens and Michael P. Washburn ()
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Mihaela E. Sardiu: Stowers Institute for Medical Research
Joshua M. Gilmore: Stowers Institute for Medical Research
Brad D. Groppe: Stowers Institute for Medical Research
Arnob Dutta: Stowers Institute for Medical Research
Laurence Florens: Stowers Institute for Medical Research
Michael P. Washburn: Stowers Institute for Medical Research

Nature Communications, 2019, vol. 10, issue 1, 1-14

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-09123-y

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