In Silico Analysis Identified Putative Pathogenic Missense nsSNPs in Human SLITRK1 Gene

Identification of Four Novel Candidate Genes for Non-syndromic Intellectual Disability in Pakistani Families

Intellectual disability, a genetically and clinically varied disorder and is a significant health problem, particularly in less developed countries due to larger family size and high ratio of consanguineous marriages. In the current genetic study, we investigate and find the novel disease causative factors in the four Pakistani families with severe type of non-syndromic intellectual disability. For genetic analysis whole-exome sequencing (WES) and Sanger sequencing was performed. I-TASSER and Cluspro tools were used for Protein modeling and Protein-protein docking. Sanger sequencing confirms the segregation of novel homozygous variants in all the families i.e., c.245 T > C; p.Leu82Pro in SLC50A1 gene in family 1, missense variant c.1037G > A; p.Arg346His in TARS2 gene in family 2, in family 3 and 4, nonsense mutation c.234G > A; p.Trp78Term and missense mutation c.2200G > A; p.Asp734Asn in TBC1D3 and ANAPC2 gene, respectively. In silico functional studies have found the drastic effect of these mutations on protein structure and its interaction properties. Substituted amino acids were highly conserved and present on highly conserved region throughout the species. The discovery of pathogenic variants in SLC50A1, TARS2, TBC1D1 and ANAPC2 shows that the specific pathways connected with these genes may be important in cognitive impairment. The decisive role of pathogenic variants in these genes cannot be determined with certainty due to lack of functional data. However, exome sequencing and segregation analysis of all filtered variants revealed that the currently reported variants were the only variations from the respective families that segregated with the phenotype in the family.

In Silico-Based Structural Evaluation to Categorize the Pathogenicity of Mutations Identified in the RAD Class of Proteins

RAD genes, known as double-strand break repair proteins, play a major role in maintaining the genomic integrity of a cell by carrying out essential DNA repair functions via double-strand break repair pathways. Mutations in the RAD class of proteins show high susceptibility to breast and ovarian cancers; however, adequate research on the mutations identified in these genes has not been extensively reported for their deleterious effects. Changes in the folding pattern of RAD proteins play an important role in protein-protein interactions and also functions. Missense mutations identified from four cancer databases, cBioPortal, COSMIC, ClinVar, and gnomAD, cause aberrant conformations, which may lead to faulty DNA repair mechanisms. It is therefore necessary to evaluate the effects of pathogenic mutations of RAD proteins and their subsequent role in breast and ovarian cancers. In this study, we have used eight computational prediction servers to analyze pathogenic mutations and understand their effects on the protein structure and function. A total of 5122 missense mutations were identified from four different cancer databases, of which 1165 were predicted to be pathogenic using at least five pathogenicity prediction servers. These mutations were characterized as high-risk mutations based on their location in the conserved domains and subsequently subjected to structural stability characterization. The mutations included in the present study were selected from clinically relevant mutants in breast cancer pedigrees. Comparative folding patterns and intra-atomic interaction results showed alterations in the structural behavior of RAD proteins, specifically RAD51C triggered by mutations G125V and L138F and RAD51D triggered by mutations S207L and E233G.

Identification of deleterious nsSNPs in human HGF gene: in silico approach

HGF is a protein that binds to the hepatocyte growth factor receptor to regulate cell growth, cell motility and morphogenesis in different cells and tissues. Several bioinformatics tools and in silico methods were used to identify most deleterious nsSNPs that might change the structure and function of HGF protein. The in silico tools such as SIFT, SNP&GO and PolyPhen2 were used to distinguish deleterious nsSNPs from neutral ones. Protein stability is analysed by I-Mutant, MUpro and iStable. The functional and structural effects are predicted by other tools like MutPred2, Maestro, DUET etc. Analysis of structure was performed by HOPE and Mutation3D. SWISS-MODEL. server, was used for wild type and mutant proteins 3-D Modelling. Gene-gene and protein-protein interaction were predicted by GeneMANIA and STRING, respectively. The wildtype HGF protein and these three variants were independently docked with their close interactor protein MET by the use of ClusPro. Our study suggested that out of 392 missense nsSNPs of the HGF gene, five nsSNPs (D358G, G648R, I550N, N175S and R220Q), are the most deleterious in HGF gene. Gene-gene interactions showed relation of HGF with other genes depicting its importance in several pathways and co-expressions. The protein-protein interacting network is composed of 11 nodes. Analysis of protein stability by different tools indicated that the five nsSNPS decreased the stability of the protein. Anyway these nsSNPs need a confirmation analysis by experimental investigation and GWAS studiesCommunicated by Ramaswamy H. Sarma.