In-silico screening and microsecond molecular dynamics simulations to identify single point mutations that destabilize ß-hexosaminidase A causing Tay-Sachs disease.

ß-hexosaminidase A (HexA) protein is responsible for the degradation of GM2 gangliosides in the central and peripheral nervous systems. Tay-Sachs disease occurs when HexA within Hexosaminidase does not properly function and harmful GM2 gangliosides begin to build up within the neurons. In this study, in silico methods such as SIFT, PolyPhen-2, PhD-SNP, and MutPred were utilized to analyze the effects of nonsynonymous single nucleotide polymorphisms (nsSNPs) on HexA in order to identify possible pathogenetic and deleterious variants. Molecular dynamics (MD) simulations showed that two mutants, P25S and W485R, experienced an increase in structural flexibility compared to the native protein. Particularly, there was a decrease in the overall number and frequencies of hydrogen bonds for the mutants compared to the wildtype. MM/GBSA calculations were performed to help assess the change in binding affinity between the wildtype and mutant structures and a mechanism-based inhibitor, NGT, which is known to help increase the residual activity of HexA. Both of the mutants experienced a decrease in the binding affinity from -23.8 kcal/mol in wildtype to -20.9 and -18.7 kcal/mol for the P25S and W485R variants of HexA, respectively.

CoagVDb: a comprehensive database for coagulation factors and their associated SAPs

The current state of the art in medical genetics is to identify and classify the functional (deleterious) or non-functional (neutral) single amino acid substitutions (SAPs), also known as non-synonymous SNPs (nsSNPs). The primary goal is to elucidate the mechanisms through which functional SAPs exert their effects, and ultimately interrogating this information for association with complex phenotypes. This work focuses on coagulation factors involved in the coagulation cascade pathway which plays a vital role in the maintenance of homeostasis in the human system. We developed an integrated coagulation variation database, CoagVDb, which makes use of the biological information from various public databases such as NCBI, OMIM, UniProt, PDB and SAPs (rsIDs/variant). CoagVDb enriched with computational prediction scores classify SAPs as either deleterious or tolerated. Also, various other properties are incorporated such as amino acid composition, secondary structure elements, solvent accessibility, ordered/disordered regions, conservation, and the presence of disulfide bonds. This specialized database provides integration of various prediction scores from different computational methods along with gene, protein, and disease information. We hope our database will act as a useful reference resource for hematologists to reveal protein structure-function relationship and disease genotype-phenotype correlation.

Computational pipeline to identify and characterize functional mutations in ornithine transcarbamylase deficiency.

Ornithine transcarbamylase (OTC) (E.C. 2.1.3.3) is one of the enzymes in the urea cycle, which involves in a sequence of reactions in the liver cells. During protein assimilation in our body surplus nitrogen is made, this open nitrogen is altered into urea and expelled out of the body by kidneys, in this cycle OTC helps in the conversion of free toxic nitrogen into urea. Ornithine transcarbamylase deficiency (OTCD: OMIM#311250) is triggered by mutation in this OTC gene. To date more than 200 mutations have been noted. Mutation in OTC gene indicates alteration in enzyme production, which upsets the ability to carry out the chemical reaction. The computational analysis was initiated to identify the deleterious nsSNPs in OTC gene in causing OTCD using five different computational tools such as SIFT, PolyPhen 2, I-Mutant 3, SNPs&Go, and PhD-SNP. Studies on the molecular basis of OTC gene and OTCD have been done partially till date. Hence, in silico categorization of functional SNPs in OTC gene can provide valuable insight in near future in the diagnosis and treatment of OTCD.

Meta-analysis diagnostic accuracy of SNP-based pathogenicity detection tools: a case of UTG1A1 gene mutations.

Crigler-Najjar syndrome (CNS) type I and type II are usually inherited as autosomal recessive conditions that result from mutations in the UGT1A1 gene. The main objective of the present review is to summarize results of all available evidence on the accuracy of SNP-based pathogenicity detection tools compared to published clinical result for the prediction of in nsSNPs that leads to disease using prediction performance method. A comprehensive search was performed to find all mutations related to CNS. Database searches included dbSNP, SNPdbe, HGMD, Swissvar, ensemble, and OMIM. All the mutation related to CNS was extracted. The pathogenicity prediction was done using SNP-based pathogenicity detection tools include SIFT, PHD-SNP, PolyPhen2, fathmm, Provean, and Mutpred. Overall, 59 different SNPs related to missense mutations in the UGT1A1 gene, were reviewed. Comparing the diagnostic OR, PolyPhen2 and Mutpred have the highest detection 4.983 (95% CI: 1.24 - 20.02) in both, following by SIFT (diagnostic OR: 3.25, 95% CI: 1.07 - 9.83). The highest MCC of SNP-based pathogenicity detection tools, was belong to SIFT (34.19%) followed by Provean, PolyPhen2, and Mutpred (29.99%, 29.89%, and 29.89%, respectively). Hence the highest SNP-based pathogenicity detection tools ACC, was fit to SIFT (62.71%) followed by PolyPhen2, and Mutpred (61.02%, in both). Our results suggest that some of the well-established SNP-based pathogenicity detection tools can appropriately reflect the role of a disease-associated SNP in both local and global structures.