Proteome‐wide analysis of nonsynonymous single‐nucleotide variations in active sites of human proteins

Abstract

An enzyme's active site is essential to normal protein activity such that any disruptions at this site may lead to dysfunction and disease. Nonsynonymous single-nucleotide variations (nsSNVs), which alter the amino acid sequence, are one type of disruption that can alter the active site. When this occurs, it is assumed that enzyme activity will vary because of the criticality of the site to normal protein function. We integrate nsSNV data and active site annotations from curated resources to identify all activesite-impacting nsSNVs in the human genome and search for all pathways observed to be associated with this data set to assess the likely consequences. We find that there are 934 unique nsSNVs that occur at the active sites of 559 proteins. Analysis of the nsSNV data shows an over-representation of arginine and an under-representation of cysteine, phenylalanine and tyrosine when comparing the list of nsSNV-impacted active site residues with the list of all possible proteomic active site residues, implying a potential bias for or against variation of these residues at the active site. Clustering analysis shows an abundance of hydrolases and transferases. Pathway and functional analysis shows several pathways over-or underrepresented in the data set, with the most significantly affected pathways involved in carbohydrate metabolism. We provide a table of 32 variationsubstrate/product pairs that can be used in targeted metabolomics experiments to assay the effects of specific variations. In addition, we report the significant prevalence of aspartic acid to histidine variation in eight proteins associated with nine diseases including glycogen storage diseases, lacrimo-auriculo-dento-digital syndrome, Parkinson's disease and several cancers.

A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE)

“…Unless otherwise noted, all accessions and identifications (IDs) are mapped using ID Mapping table (40), followed by pairwise alignment and mapping of sequence positions with methods that have been used previously (24, 41). Only those nsSNVs that could be mapped to UniProtKB/Swiss-Prot human protein that have the Complete Proteome keyword tag are retained in BioMuta.…”

Non-synonymous variations in cancer and their effects on the human proteome: workflow for NGS data biocuration and proteome-wide analysis of TCGA data

“…The effects that a specific variation has on a protein function have been the focus of studies for quite some time with several tools that predict SNV effects [54-57]. Proteome-wide analysis of variation that affects known functional sites [26-28,58] is another way of estimating how variation can affect function at a system level and if there are specific domains or pathways that are more prone to having variations.…”

“…Additional functional analysis of proteins affected by nsSNVs is performed based on methods described earlier [26-28]. Briefly, nsSNV data is uploaded into SNVDis database and integrated with protein sequence features obtained from UniProtKB/Swiss-Prot [20], Conserved Domain Database (CDD) and RefSeq of NCBI [42].…”

“…We chose to curate publicly available NGS data to provide users an unbiased view of variation that is present at the individual person level and is not yet completely captured in dbSNP, thereby providing a better understanding of the human variome. Additionally, based on our previous work on functional analysis of non-synonymous Single Nucleotide Variations (nsSNVs) from dbSNP [21], UniProt [20] and COSMIC [25] we show how proteome-wide analysis of variation can provide a detailed view of the distribution of variation and possible functional impact [26-29]. …”

A framework for application of metabolic modeling in yeast to predict the effects of nsSNV in human orthologs

“…In our previous paper [11] we used the SNVDis Tool [12], part of the High-performance Integrated Virtual Environment (HIVE) tools suite (accessed at hive.biochemistry.gwu.edu) [13] to identify the entire set of 559 human proteins with nonsynonymous variation at an active site residue resulting from a single nucleotide variation. Pathway, substrate and product annotation was manually retrieved from Kyoto Encyclopedia of Genes and Genomes (KEGG) [14] for all possible proteins in the dataset.…”