Structural Consequences of IRS-2 nsSNPs and Implication for Insulin Receptor Substrate-2 Protein Stability.

Single-Nucleotide Polymorphisms (SNPs) are common genetic variations implicated in human diseases. The non-synonymous SNPs (nsSNPs) affect the proteins' structures and their molecular interactions with other interacting proteins during the accomplishment of biochemical processes. This ultimately causes proteins functional perturbation and disease phenotypes. The Insulin receptor substrate-2 (IRS-2) protein promotes glucose absorption and participates in the biological regulation of glucose metabolism and energy production. Several IRS-2 SNPs are reported in association with type 2 diabetes and obesity in human populations. However, there are no comprehensive reports about the protein structural consequences of these nsSNPs. Keeping in view the pathophysiological consequences of the IRS-2 nsSNPs, we designed the current study to understand their possible structural impact on coding protein. The prioritized list of the deleterious IRS-2 nsSNPs was acquired from multiple bioinformatics resources, including VEP (SIFT, PolyPhen, and Condel), PROVEAN, SNPs&GO, PMut, and SNAP2. The protein structure stability assessment of these nsSNPs was performed by MuPro and I-Mutant-3.0 servers via structural modeling approaches. The atomic-level structural and molecular dynamics (MD) impact of these nsSNPs were examined using GROMACS 2019.2 software package. The analyses initially predicted 8 high-risk nsSNPs located in the highly conserved regions of IRS-2. The MD simulation analysis eventually prioritized the N232Y, R218C, and R104H nsSNPs that predicted to significantly compromise the structure stability and may affect the biological function of IRS-2. These nsSNPs are predicted as high-risk candidates for diabetes and obesity. The validation of protein structural impact of these shortlisted nsSNPs may provide biochemical insight into the IRS-2-mediated type-2 diabetes.

Do environmentally induced DNA variations mediate adaptation in Aspergillus flavus exposed to chromium stress in tannery sludge?

BACKGROUND: Environmental stress induced genetic polymorphisms have been suggested to arbitrate functional modifications influencing adaptations in microbes. The relationship between the genetic processes and concomitant functional adaptation can now be investigated at a genomic scale with the help of next generation sequencing (NGS) technologies. Using a NGS approach we identified genetic variations putatively underlying chromium tolerance in a strain of Aspergillus flavus isolated from a tannery sludge. Correlation of nsSNPs in the candidate genes (n = 493) were investigated for their influence on protein structure and possible function. Whole genome sequencing of chromium tolerant A. flavus strain (TERIBR1) was done (Illumina HiSeq2000). The alignment of quality trimmed data of TERIBR1 with reference NRRL3357 (accession number EQ963472) strain was performed using Bowtie2 version 2.2.8. SNP with a minimum read depth of 5 and not in vicinity (10 bp) of INDEL were filtered. Candidate genes conferring chromium resistance were selected and SNPs were identified. Protein structure modeling and interpretation for protein-ligand (CrO4- 2) docking for selected proteins harbouring non-synonymous substitutions were done using Phyre2 and PatchDock programs. RESULTS: High rate of nsSNPs (approximately 11/kb) occurred in selected candidate genes for chromium tolerance. Of the 16 candidate genes selected for studying effect of nsSNPs on protein structure and protein-ligand interaction, four proteins belonging to the Major Facilitator Superfamily (MFS) and recG protein families showed significant interaction with chromium ion only in the chromium tolerant A. flavus strain TERIBR1. CONCLUSIONS: Presence of nsSNPs and subsequent amino-acid alterations evidently influenced the 3D structures of the candidate proteins, which could have led to improved interaction with (CrO4- 2) ion. Such structural modifications might have enhanced chromium efflux efficiency of A. flavus (TERIBR1) and thereby offered the adaptation benefits in counteracting chromate stress. Our findings are of fundamental importance to the field of heavy-metal bio-remediation.

Characterization of SNPs in the dopamine-ß-hydroxylase gene providing new insights into its structure-function relationship.

Dopamine-ß-hydroxylase (DBH, EC 1.14.17.1), an oxido-reductase that catalyses the conversion of dopamine to norepinephrine, is largely expressed in sympathetic neurons and adrenal medulla. Several regulatory and structural variants in DBH associated with various neuropsychiatric, cardiovascular diseases and a few that may determine enzyme activity have also been identified. Due to paucity of studies on functional characterization of DBH variants, its structure-function relationship is poorly understood. The purpose of the study was to characterize five non-synonymous (ns) variants that were prioritized either based on previous association studies or Sorting Tolerant From Intolerant (SIFT) algorithm. The DBH ORF with wild type (WT) and site-directed mutagenized variants were transfected into HEK293 cells to generate transient and stable lines expressing these variant enzymes. Activity was determined by UPLC-PDA and corresponding quantity by MRMHR on a TripleTOF 5600 MS respectively of spent media from stable cell lines. Homospecific activity computed for the WT and variant proteins showed a marginal decrease in A318S, W544S and R549C variants. In transient cell lines, differential secretion was observed in the case of L317P, W544S and R549C. Secretory defect in L317P was confirmed by localization in ER. R549C exhibited both decreased homospecific activity and differential secretion. Of note, all the variants were seen to be destabilizing based on in silico folding analysis and molecular dynamics (MD) simulation, lending support to our experimental observations. These novel genotype-phenotype correlations in this gene of considerable pharmacological relevance have implications for dopamine-related disorders.

Structure-Function Mutational Analysis and Prediction of the Potential Impact of High Risk Non-Synonymous Single-Nucleotide Polymorphism on Poliovirus 2A Protease Stability Using Comprehensive Informatics Approaches.

Polio viral proteinase 2A performs several essential functions in genome replication. Its inhibition prevents viral replication, thus making it an excellent substrate for drug development. In this study, the three-dimensional structure of 2A protease was determined and optimized by homology modelling. To predict the molecular basis of the interaction of small molecular agonists, docking simulations were performed on a structurally diverse dataset of poliovirus 2A protease (PV2Apr°) inhibitors. Docking results were employed to identify high risk missense mutations that are highly damaging to the structure, as well as the function, of the protease. Intrinsic disorder regions (IDRs), drug binding sites (DBS), and protein stability changes upon mutations were also identified among them. Our results demonstrated dominant roles for Lys 15, His 20, Cys 55, Cys 57, Cys 64, Asp 108, Cys 109 and Gly 110, indicating the presence of various important drug binding sites of the protein. Upon subjecting these sites to single-nucleotide polymorphism (SNP) analysis, we observed that out of 155 high risk SNPs, 139 residues decrease the protein stability. We conclude that these missense mutations can affect the functionality of the 2A protease, and that identified protein binding sites can be directed for the attachment and inhibition of the target proteins.

In silico analysis of nsSNPs in ABCB1 gene affecting breast cancer associated protein P-glycoprotein (P-gp).

Breast cancer is one of the most common cancers among women and increased expression of some polymorphic genes, which is rare within families, enhances the risk of breast cancer incidence. The correct identification of the functional SNPs of such genes is important for characterizing the functional aspect of these SNPs which can be assessed by evaluating their significant influence on the structure and function of proteins. Since the presence of SNPs in these genes affects the quality of life of a breast cancer patient, thus, the associated diagnostic markers have a reliable potential for assessing the prognosis of breast cancer. ATP-binding cassette (ABC) genes have been shown to obstruct the treatment of breast cancer by providing resistance to malignant cells from anti-cancer drugs. Some allelic variants of ABCG2 and ABCB1 are also associated with occurrence of skin toxicity during the treatment of breast cancer with anti-cancer drugs. The present study has incorporated comprehensive bioinformatics analysis to explore the possible disease-associated mutations of ABCB1 gene, a gene that resulted from gene-environment interaction study, and understand their consequential effect on the structural and functional behavior of P-glycoprotein. Two gene variants (R538S and M701R) of P-glycoprotein were selected as potentially detrimental point mutations, and these variants were modeled. Molecular dynamic simulation (MDS) studies unraveled the atomic interactions and motion trajectories of the native as well as the two mutant (R538S and M701R) structures and were predicted to have a deleterious effect on breast cancer associated P-gp. Thus, the present study may broaden the way to design novel potent drugs for overcoming the problems associated with multidrug resistance (MDR) resulting from a change in protein conformation due to a mutation in ABCB1 gene.

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 (http://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.