Identifying protein function and functional links based on large-scale co-occurrence patterns

Zohar Pasternak, Noam Chapnik, Roy Yosef, Naama M. Kopelman*, Edouard Jurkevitch, Elad Segev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Objective The vast majority of known proteins have not been experimentally tested even at the level of measuring their expression, and the function of many proteins remains unknown. In order to decipher protein function and examine functional associations, we developed "Cliquely", a software tool based on the exploration of co-occurrence patterns. Computational model Using a set of more than 23 million proteins divided into 404,947 orthologous clusters, we explored the co-occurrence graph of 4,742 fully sequenced genomes from the three domains of life. Edge weights in this graph represent co-occurrence probabilities. We use the Bron-Kerbosch algorithm to detect maximal cliques in this graph, fully-connected subgraphs that represent meaningful biological networks from different functional categories. Main results We demonstrate that Cliquely can successfully identify known networks from various pathways, including nitrogen fixation, glycolysis, methanogenesis, mevalonate and ribosome proteins. Identifying the virulence-associated type III secretion system (T3SS) network, Cliquely also added 13 previously uncharacterized novel proteins to the T3SS network, demonstrating the strength of this approach. Cliquely is freely available and open source. Users can employ the tool to explore co-occurrence networks using a protein of interest and a customizable level of stringency, either for the entire dataset or for a one of the three domains- Archaea, Bacteria, or Eukarya.

Original languageAmerican English
Article numbere0264765
JournalPLoS ONE
Volume17
Issue number3 March
DOIs
StatePublished - Mar 2022

Bibliographical note

Publisher Copyright:
© 2022 Pasternak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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