K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores

  • Arnold I Emerson Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA. Genomic Core, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha 24144, Qatar
  • Simeon Andrews Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA. Genomic Core, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha 24144, Qatar
  • Ikhlak Ahmed Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA. Genomic Core, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha 24144, Qatar
  • Thasni KA Azis Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA. Genomic Core, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha 24144, Qatar
  • Joel A Malek Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA. Genomic Core, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha 24144, Qatar

Abstract

Background: Network biology currently focuses primarily on metabolic pathways, gene regulatory, and protein-protein interaction networks. While these approaches have yielded critical information, alternative methods to network analysis will offer new perspectives on biological information. A little explored area is the interactions between domains that can be captured using domain co-occurrence networks (DCN). A DCN can be used to study the function and interaction of proteins by representing protein domains and their co-existence in genes and by mapping cancer mutations to the individual protein domains to identify signals.
Results: The domain co-occurrence network was constructed for the human proteome based on PFAM domains in proteins. Highly connected domains in the central cores were identified using the k-core decomposition technique. Here we show that these domains were found to be more evolutionarily conserved than the peripheral domains. The somatic mutations for ovarian, breast and prostate cancer diseases were obtained from the TCGA database. We mapped the somatic mutations to the individual protein domains and the local false discovery rate was used to identify significantly mutated domains in each cancer type. Significantly mutated domains were found to be enriched in cancer disease pathways. However, we found that the inner cores of the DCN did not contain any of the significantly mutated domains. We observed that the inner core protein domains are highly conserved and these domains co-exist in large numbers with other protein domains.
Conclusion: Mutations and domain co-occurrence networks provide a framework for understanding hierarchal designs in protein function from a network perspective. This study provides evidence that a majority of protein domains in the inner core of the DCN have a lower mutation frequency and that protein domains present in the peripheral regions of the k-core contribute more heavily to the disease. These findings may contribute further to drug development.

Keywords

Domain co-occurrence network, K-core decomposition, Somatic mutations, Cancer, Cancer mutations, TCGA
Published
2015-03-03
How to Cite
EMERSON, Arnold I et al. K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores. Journal of Clinical Bioinformatics, [S.l.], v. 5, n. 1, mar. 2015. ISSN 2043-9113. Available at: <http://jclinbioinformatics.com/article/view/56>. Date accessed: 26 sep. 2017. doi: https://doi.org/10.1186/s13336-015-0016-6.