Computational identification of non-synonymous polymorphisms within regions corresponding to protein interaction sites.

BACKGROUND: Protein-protein interactions (PPI) play an important role in function of all organisms and enable understanding of underlying metabolic processes. Computational predictions of PPIs are an important aspect in proteomics, as experimental methods may result in high degree of false positive results and are more expensive. Although there are many databases collecting predicted PPIs, exploration of genetics information underlying PPI interactions has not been investigated thoroughly. The aim of the present study was to identify genomic locations corresponding to regions involved in predicted PPIs and to collect non-synonymous polymorphisms (nsSNPs) located within those regions; which we termed PPI-SNPs.

METHODS: Predicted PPIs were obtained from PiSITE database (https://pisite.hgc.jp). Non-synonymous SNPs mapped on protein structural data (PDBs) were obtained from the UCSC server. Polymorphism locations on protein structures were mapped to predicted PPI regions. DAVID tool was used for pathway enrichment and gene cluster analysis (https://david.ncifcrf.gov/).

RESULTS: We collected 544 polymorphisms located within predicted PPI sites that map to 197 genes. We identified 9 SNPs, previously associated with diseases, but not yet associated with PPI sites. We also found examples in which polymorphisms located within predicted PPI regions are also occurring within previously experimentally validated PPIs and within experimentally determined functional domains.

CONCLUSIONS: Our study provides the first catalog of nsSNPs located within predicted PPIs. These prioritized SNPs present the basis for planning experimental validation of SNPs that cause gain or loss of PPIs. Our implementation is expandable, as datasets used are constantly updated.