Computational Identification and Functional Analysis of Potentially Pathogenic nsSNPs in the NLRP3 Gene Linked to Alzheimer's Disease.

Single Nucleotide Polymorphisms (SNPs) are key in understanding complex diseases. Nonsynonymous single-nucleotide polymorphisms (nsSNPs) occur in protein-coding regions, potentially altering amino acid sequences, protein structure and function. Computational methods are vital for distinguishing deleterious nsSNPs from neutral ones. We investigated the role of NLRP3 gene in neuroinflammation associated with Alzheimer's disease (AD) pathogenesis. A total of 893 missense (nsSNPs) were obtained from the dbSNP database and subjected to rigorous filtering using bioinformatics tools like SIFT, Align GVGD, PolyPhen-2, and PANTHER to identify potentially damaging variants. Of these, 18 nsSNPs were consistently predicted to have deleterious effects across all tools. Notably, 16 of these variants exhibited reduced protein stability, while only 4 were predicted to be buried within the protein structure. Among the identified nsSNPs, rs180177442 (R262L and R262P), rs201875324 (T659I), and rs139814109 (T897M) were classified as high-risk variants due to their significant deleterious impact, probable damaging effects, and association with decreased protein stability. Molecular docking and simulation analyses were conducted utilizing Memantine, a standard drug utilized in AD treatment, to investigate potential interactions with the altered protein structures. Additional clinical and genetic investigations are necessary to elucidate the underlying mechanisms that link NLRP3 polymorphisms with the initiation of AD.

Effects of nonsynonymous single nucleotide polymorphisms of the KIAA1217, SNTA1 and LTBP1 genes on the growth traits of Ujumqin sheep.

Sheep body size can directly reflect the growth rates and fattening rates of sheep and is also an important index for measuring the growth performance of meat sheep. In this study, high-resolution resequencing data from four sheep breeds (Dorper sheep, Suffolk sheep, Ouessant sheep, and Shetland sheep) were analyzed. The nonsynonymous single nucleotide polymorphisms of three candidate genes (KIAA1217, SNTA1, and LTBP1) were also genotyped in 642 healthy Ujumqin sheep using MALDI-TOFMS and the genotyping results were associated with growth traits. The results showed that different genotypes of the KIAA1217 g.24429511T>C locus had significant effects on the chest circumferences of Ujumqin sheep. The SNTA1 g.62222626C>A locus had different effects on the chest depths, shoulder widths and rump widths of Ujumqin sheep. This study showed that these two sites can be used for marker-assisted selection, which will be beneficial for future precision molecular breeding.

Structural impact, ligand-protein interactions, and molecular phenotypic effects of TGF-ß1 gene variants: In silico analysis with implications for idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF) is a chronic interstitial lung disease resulting in progressively deteriorating lung function. Transforming growth factor-ß1 (TGF-ß1) belongs to the TGF superfamily and exerts a profibrotic role in promoting lung fibrosis by facilitating fibroblast infiltration and activity, extracellular matrix deposition, and inhibition of collagen breakdown, thus promoting tissue remodelling and IPF. MATERIALS AND METHODS: We evaluated the link between pathogenic TGF-ß1 SNPs and IPF pathogenesis and the structure-activity functional consequences of those SNPs on the TGF-ß1 protein. Several computational algorithms were merged to address the functional consequences of TGF-ß1 gene mutations to protein stability, putative post-translational modification sites, ligand-protein interactions, and molecular phenotypic effects. These included FATHMM, POLYPHEN2, PROVEAN, and SIFT tools (identifying deleterious nsSNPs in the TGF-ß1 gene), along with Pmut, PhD-SNP, SNAP, MutPred and the related TMHMM, MARCOIL, and DisProt algorithms (predicting structural disorders). INPS-MD was also used to evaluate the mutation-induced TGF-ß1 protein's stability and MODPRED for recognition of post-translational TGF-ß1 modification. RESULTS: In total, 14 major pathogenic variants markedly impact the destabilization of the TGF-ß1 protein, with most of these high-risk mutations associated with decreased stability of the TGF-ß1 protein as per the I-Mutant, MUpro, and INPS-MD tools. R205W, R185W, R180Q, D86Y, and I300T variants were proposed to participate in the post-translational modifications, thus affecting affect protein-ligand interactions. Furthermore, at-risk genetic variants appear to target conserved regions in the alpha helices, random coils, and extracellular loops, resulting in a varied composition of amino acids, charge, hydrophobicity, and spatial architecture. CONCLUSIONS: This study manuscript comprehensively analyzes gene variants within the TGF-ß1 gene, offering novel insights into their structural and functional implications in interacting with target sites. This study is significant for the development of targeted therapeutic strategies and personalized treatment approaches for patients with inflammatory lung diseases such as IPF.

A computational study on structural and functional consequences of nsSNPs in human dopa decarboxylase.

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson's disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches.Communicated by Ramaswamy H. Sarma.

Computational analysis and molecular dynamics simulation of high-risk single nucleotide polymorphisms of the ADAM10 gene.

Polymorphisms of the disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) are linked to pathophysiological changes in lung inflammation, cancer, Alzheimer's disease (AD), encephalopathy, liver fibrosis, and cardiovascular diseases. In this study, we predicted the pathogenicity of ADAM10 non-synonymous single nucleotide polymorphisms (nsSNPs) in a wide array of mutation analyzing bioinformatics tools. We retrieved 423 nsSNPs from dbSNP-NCBI for the analysis, and 13 were predicted deleterious by each of the ten tools: SIFT, PROVEAN, CONDEL, PANTHER-PSEP, SNAP2, SuSPect, PolyPhen-2, Meta-SNP, Mutation Assessor and Predict-SNP. Further assessment of amino acid sequences, homology models, conservation profiles, and inter-atomic interactions identified C222G, G361E and C639Y as the most pathogenic mutations. We validated this prediction through structural stability analysis using DUET, I-Mutant Suite, SNPeffect and Dynamut. Molecular dynamics simulations and principal component analysis also indicated considerable instability of the C222G, G361E and C639Y variants. Therefore, these ADAM10 nsSNPs could be candidates for diagnostic genetic screening and therapeutic molecular targeting.Communicated by Ramaswamy H. Sarma.

In Silico Investigation of AKT2 Gene and Protein Abnormalities Reveals Potential Association with Insulin Resistance and Type 2 Diabetes.

Type 2 diabetes (T2D) develops from insulin resistance (IR) and the dysfunction of pancreatic beta cells. The AKT2 protein is very important for the protein signaling pathway, and the non-synonymous SNP (nsSNPs) in AKT2 gene may be associated with T2D. nsSNPs can result in alterations in protein stability, enzymatic activity, or binding specificity. The objective of this study was to investigate the effect of nsSNPs on the AKT2 protein structure and function that may result in the induction of IR and T2D. The study identified 20 variants that were considered to be the most deleterious based on a range of analytical tools included (SIFT, PolyPhen2, Mut-pred, SNAP2, PANTHER, PhD-SNP, SNP&Go, MUpro, Cosurf, and I-Mut). Two mutations, p.A179T and p.L183Q, were selected for further investigation based on their location within the protein as determined by PyMol. The results indicated that mutations, p.A179T and p.L183Q alter the protein stability and functional characteristics, which could potentially affect its function. In order to conduct a more in-depth analysis of these effects, a molecular dynamics simulation was performed for wildtype AKT2 and the two mutants (p.A179T and p.L183Q). The simulation evaluated various parameters, including temperature, pressure, density, RMSD, RMSF, SASA, and Region, over a period of 100 ps. According to the simulation results, the wildtype AKT2 protein demonstrated higher stability in comparison to the mutant variants. The mutations p.A179T and p.L183Q were found to cause a reduction in both protein stability and functionality. These findings underscore the significance of the effects of nsSNPs (mutations p.A179T and p.L183Q) on the structure and function of AKT2 that may lead to IR and T2D. Nevertheless, they require further verifications in future protein functional, protein-protein interaction, and large-scale case-control studies. When verified, these results will help in the identification and stratification of individuals who are at risk of IR and T2D for the purpose of prevention and treatment.

Predicting the effects of rare genetic variants on oncogenic signaling pathways: A computational analysis of HRAS protein function.

The HRAS gene plays a crucial role in regulating essential cellular processes for life, and this gene's misregulation is linked to the development of various types of cancers. Nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of HRAS can cause detrimental mutations that disrupt wild-type protein function. In the current investigation, we have employed in-silico methodologies to anticipate the consequences of infrequent genetic variations on the functional properties of the HRAS protein. We have discovered a total of 50 nsSNPs, of which 23 were located in the exon region of the HRAS gene and denoting that they were expected to cause harm or be deleterious. Out of these 23, 10 nsSNPs ([G60V], [G60D], [R123P], [D38H], [I46T], [G115R], [R123G], [P11OL], [A59L], and [G13R]) were identified as having the most delterious effect based on results of SIFT analysis and PolyPhen2 scores ranging from 0.53 to 69. The DDG values -3.21 kcal/mol to 0.87 kcal/mol represent the free energy change associated with protein stability upon mutation. Interestingly, we identified that the three mutations (Y4C, T58I, and Y12E) were found to improve the structural stability of the protein. We performed molecular dynamics (MD) simulations to investigate the structural and dynamic effects of HRAS mutations. Our results showed that the stable model of HRAS had a significantly lower energy value of -18756 kj/mol compared to the initial model of -108915 kj/mol. The RMSD value for the wild-type complex was 4.40 Å, and the binding energies for the G60V, G60D, and D38H mutants were -107.09 kcal/mol, -109.42 kcal/mol, and -107.18 kcal/mol, respectively as compared to wild-type HRAS protein had -105.85 kcal/mol. The result of our investigation presents convincing corroboration for the potential functional significance of nsSNPs in augmenting HRAS expression and adding to the activation of malignant oncogenic signalling pathways.

Comprehensive analysis predicting effects of deleterious SNPs of human progesterone receptor gene on its structure and functions: a computational approach.

Progesterone receptor plays a crucial role in the development of the mammary gland and breast cancer. Single nucleotide polymorphisms (SNPs) within its gene, PGR, are associated with the risk of miscarriages and preterm birth as well as many cancers across different populations. The main aim of this work is to investigate the most deleterious SNPs in the PGR gene to identify potential biomarkers for various disease susceptibility and treatments. Both sequence and structure-based computational approaches were adopted and in total 11 nsSNPs have been filtered out of 674 nsSNPs along with seven non-coding SNPs. R740Q, I744T and D746E belonged to a mutation cluster. R740Q, D746E along with S865L altered H-bond interactions within the receptor. The same mutations have been found to be associated with several cancers including uterine and breast cancer among others. It is, therefore, possible that the high-risk SNPs associated with cancers may exert their effect by causing changes in the protein structure, particularly in its bonding patterns, and thus affecting its function. In addition, seven non-coding SNPs that were located in the UTR region created a new miRNA site while three SNPs disrupted a conserved miRNA site. These high-risk SNPs can play an instrumental role in generating a dataset of the PGR gene's SNPs. Thus, the present study may pave the way to design and develop novel therapeutics for overcoming the challenges associated with certain cancers and pregnancy that result from a change in the protein structure and function due to the SNP mutations in the PGR gene.Communicated by Ramaswamy H. Sarma.

The rs1801280 SNP is associated with non-small cell lung carcinoma by exhibiting a highly deleterious effect on N-acetyltransferase 2.

PURPOSE: N-acetyltransferase 2 is an enzyme that is involved in the detoxification of carcinogens in the human body, so any damage to this protein may lead to the emergence of several metabolic dysfunctions. This work was conducted to determine the association between NAT2 polymorphism and non-small cell lung carcinoma (NSCLC) that is increasingly reported in the Iraqi population. METHODS: PCR sequencing was conducted to assess the possible association between genetic variants and NSCLC. Several in silico tools were implemented to investigate the effect of the observed SNPs on the structure, function, and stability of the altered NAT2. RESULTS: Five SNPS of NAT2 (rs1208, rs1041983, rs1799929, rs1799930, and rs1801280) were identified in high frequencies in the amplified fragment. These SNPs showed variable distributions of haplotypes between cases and controls. No significant association of rs1208, rs1041983, rs1799929, and rs1799930 with NSCLC was shown in the investigated population. In contrast, rs1801280: CC genotype showed a highly significant (P = 0.009) association with the NSCLC, and individuals with this genotype had 2.19 more chances for developing NSCLC (OR 2.19; Cl95% 1.21-3.94). Association analysis of rs1801280 SNP distribution among the investigated patients showed that patients with CC genotype showed a significant (P = 0.02, OR 2.65) association with family history, which entailed a high hereditary possibility of this genotype among Iraqi patients. It was predicted that this SNP showed high damaging effects on the activity of NAT2 enzyme, with various deleterious outcomes on enzyme structure, function, and stability. CONCLUSION: Data indicated that rs1801280 SNP exerted a tight association with NSCLC since individuals with CC genotype exhibited the most damaging effects on the NAT2 that may be behind the low acetylation rates of this enzyme in patients with NSCLC.

Identification of the SIRT1 gene's most harmful non-synonymous SNPs and their effects on functional and structural features-an in silico analysis.

Introduction: The sirtuin (Silent mating type information regulation 2 homolog)1(SIRT1) protein plays a vital role in many disorders such as diabetes, cancer, obesity, inflammation, and neurodegenerative and cardiovascular diseases. The objective of this in silico analysis of SIRT1's functional single nucleotide polymorphisms (SNPs) was to gain valuable insight into the harmful effects of non-synonymous SNPs (nsSNPs) on the protein. The objective of the study was to use bioinformatics methods to investigate the genetic variations and modifications that may have an impact on the SIRT1 gene's expression and function. Methods: nsSNPs of SIRT1 protein were collected from the dbSNP site, from its three (3) different protein accession IDs. These were then fed to various bioinformatic tools such as SIFT, Provean, and I- Mutant to find the most deleterious ones. Functional and structural effects were examined using the HOPE server and I-Tasser. Gene interactions were predicted by STRING software. The SIFT, Provean, and I-Mutant tools detected the most deleterious three nsSNPs (rs769519031, rs778184510, and rs199983221). Results: Out of 252 nsSNPs, SIFT analysis showed that 94 were deleterious, Provean listed 67 dangerous, and I-Mutant found 58 nsSNPs resulting in lowered stability of proteins. HOPE modelling of rs199983221 and rs769519031 suggested reduced hydrophobicity due to Ile 4Thr and Ile223Ser resulting in decreased hydrophobic interactions. In contrast, on modelling rs778184510, the mutant protein had a higher hydrophobicity than the wild type. Conclusions: Our study reports that three nsSNPs (D357A, I223S, I4T) are the most damaging mutations of the SIRT1 gene. Mutations may result in altered protein structure and functions. Such altered protein may be the basis for various disorders. Our findings may be a crucial guide in establishing the pathogenesis of various disorders.

In-silico analysis of nonsynonymous genomic variants within CCM2 gene reaffirm the existence of dual cores within typical PTB domain.

PURPOSE: The objective of this study is to validate the existence of dual cores within the typical phosphotyrosine binding (PTB) domain and to identify potentially damaging and pathogenic nonsynonymous coding single nuclear polymorphisms (nsSNPs) in the canonical PTB domain of the CCM2 gene that causes cerebral cavernous malformations (CCMs). METHODS: The nsSNPs within the coding sequence for PTB domain of human CCM2 gene, retrieved from exclusive database searches, were analyzed for their functional and structural impact using a series of bioinformatic tools. The effects of mutations on the tertiary structure of the PTB domain in human CCM2 protein were predicted to examine the effect of nsSNPs on the tertiary structure of PTB Cores. RESULTS: Our mutation analysis, through alignment of protein structures between wildtype CCM2 and mutant, predicted that the structural impacts of pathogenic nsSNPs is biophysically limited to only the spatially adjacent substituted amino acid site with minimal structural influence on the adjacent core of the PTB domain, suggesting both cores are independently functional and essential for proper CCM2 PTB function. CONCLUSION: Utilizing a combination of protein conservation and structure-based analysis, we analyzed the structural effects of inherited pathogenic mutations within the CCM2 PTB domain. Our results predicted that the pathogenic amino acid substitutions lead to only subtle changes locally, confined to the surrounding tertiary structure of the PTB core within which it resides, while no structural disturbance to the neighboring PTB core was observed, reaffirming the presence of independently functional dual cores in the CCM2 typical PTB domain.

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.

More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which remains an unresolved challenge. In the present work, a new computational approach, termed MSRes-MutP, is proposed based on ResNet blocks with multi-scale kernel size to predict disease-associated nsSNPs. By feeding the serial concatenation of the extracted four types of features, the performance of MSRes-MutP does not obviously improve. To address this, a second model FFMSRes-MutP is developed, which utilizes deep feature fusion strategy and multi-scale 2D-ResNet and 1D-ResNet blocks to extract relevant two-dimensional features and physicochemical properties. FFMSRes-MutP with the concatenated features achieves a better performance than that with individual features. The performance of FFMSRes-MutP is benchmarked on five different datasets. It achieves the Matthew's correlation coefficient (MCC) of 0.593 and 0.618 on the PredictSNP and MMP datasets, which are 0.101 and 0.210 higher than that of the existing best method PredictSNP1. When tested on the HumDiv and HumVar datasets, it achieves MCC of 0.9605 and 0.9507, and area under curve (AUC) of 0.9796 and 0.9748, which are 0.1747 and 0.2669, 0.0853 and 0.1335, respectively, higher than the existing best methods PolyPhen-2 and FATHMM (weighted). In addition, on blind test using a third-party dataset, FFMSRes-MutP performs as the second-best predictor (with MCC and AUC of 0.5215 and 0.7633, respectively), when compared with the other four predictors. Extensive benchmarking experiments demonstrate that FFMSRes-MutP achieves effective feature fusion and can be explored as a useful approach for predicting disease-associated nsSNPs. The webserver is freely available at http://csbio.njust.edu.cn/bioinf/ffmsresmutp/ for academic use.

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.

Structure analysis of deleterious nsSNPs in human PALB2 protein for functional inference.

Partner and Localizer of BRCA2 or PALB2 is a typical tumor suppressor protein, that responds to DNA double stranded breaks through homologous recombination repair. Heterozygous mutations in PALB2 are known to contribute to the susceptibility of breast and ovarian cancer. However, there is no comprehensive study characterizing the structural and functional impacts of SNPs located in the PALB2 gene. Therefore, it is of interest to document a comprehensive analysis of coding and non-coding SNPs located at the PALB2 loci using in silico tools. The data for 1455 non-synonymous SNPs (nsSNPs) located in the PALB2 loci were retrieved from the dbSNP database. Comprehensive characterization of the SNPs using a combination of in silico tools such as SIFT, PROVEAN, PolyPhen, PANTHER, PhD-SNP, Pmut, MutPred 2.0 and SNAP-2, identified 28 functionally important SNPs. Among these, 16 nsSNPs were further selected for structural analysis using conservation profile and protein stability. The most deleterious nsSNPs were documented within the WD40 domain of PALB2. A general outline of the structural consequences of each variant was developed using the HOPE project data. These 16 mutant structures were further modelled using SWISS Model and three most damaging mutant models (rs78179744, rs180177123 and rs45525135) were identified. The non-coding SNPs in the 3' UTR region of the PALB2 gene were analyzed for altered miRNA target sites. The comprehensive characterization of the coding and non-coding SNPs in the PALB2 locus has provided a list of damaging SNPs with potential disease association. Further validation through genetic association study will reveal their clinical significance.

Comprehensive in-silico analysis of damage associated SNPs in hOCT1 affecting Imatinib response in chronic myeloid leukemia.

Non-synonymous single nucleotide polymorphisms (nsSNPs) in hOCT1 (encoded by SLC22A1 gene) are expected to affect Imatinib uptake in chronic myeloid leukemia (CML). In this study, sequence homology-based genetic analysis of a set of 270 coding SNPs identified 18 nsSNPs to be putatively damaging/deleterious using eight different algorithms. Subsequently, based on conservation of amino acid residues, stability analysis, posttranscriptional modifications, and solvent accessibility analysis, the possible structural-functional relationship was established for high-confidence nsSNPs. Furthermore, based on the modeling results, some dissimilarities of mutant type amino acids from wild-type amino acids such as size, charge, interaction and hydrophobicity were revealed. Three highly deleterious mutations consisting of P283L, G401S and R402G in SLC22A1 gene that may alter the protein structure, function and stability were identified. These results provide a filtered data to explore the effect of uncharacterized nsSNP and find their association with Imatinib resistance in CML.

The autophagy gene ATG16L1 (T300A) variant is associated with the risk and progression of HBV infection.

Autophagy pathway genes variants that play crucial roles in immune responses are involved in many diseases but their role in viral diseases is ill-defined. ATG16L1 gene plays a crucial role in the autophagy process. In this study, we have investigated the role of ATG16L1 variant T300A in the risk of HBV infection. rs2241880 (T300A) variant in 551 HBV infected patients (at various stages of infection) and 247 healthy controls were genotyped applying PCR-RFLP. Data analysis revealed that mutant allele G contributes to the risk of hepatitis B infection. Mutant alleles were significantly associated the HBV risk in allelic (OR = 1.31; 95%CI = 1.06-1.63, p = .01) and homozygous (OR = 1.87; 95%CI = 1.17-2.99, p = .009) models. On stratifying HBV infected individuals according to the stage of infection, a significant association was observed in asymptomatic (allelic; OR = 1.52; 95%CI = 1.10-2.09, p = .01 and homozygous; OR = 2.30; 95%CI = 1.22-4.36, p = .01) and chronic (allelic; OR = 1.36; 95%CI = 1.07-1.73, p = .01 and homozygous; OR = 2.07; 95%CI = 1.22-3.53, p = .008) stages of infection. High HBV DNA levels were associated with mutant genotype GG in asymptomatic and chronic carriers. Significantly higher ALT levels were observed in the liver cirrhosis patients with mutant genotypes. In conclusion, our data suggest that rs2241880 mutant allele carriers (allelic and homozygous models) were associated with increased risk of hepatitis B virus infection in North Indian population.

In Silico Prediction of the Effects of Nonsynonymous Single Nucleotide Polymorphisms in the Human Catechol-O-Methyltransferase (COMT) Gene.

Catechol-O-methyltransferase (COMT) enzyme performs transfer of methyl group to endogenous and exogenous catechol substrates. The COMT enzyme draws interest because of its association with psychiatric, neurological and cardiovascular diseases, and several cancers. Moreover, many prescribed drugs, supplements, and their metabolites are used as substrates of COMT enzyme. The human COMT gene has 226 nonsynonymous single nucleotide polymorphisms (nsSNPs) according to public databases. Uncovering of the molecular impacts of nsSNPs on COMT enzyme function and structure may provide standpoint on how COMT nsSNPs affect enzyme activity and contribute to disease development. Therefore, we aimed in this study to predict possible structural and functional damaging effects of all knowns nsSNPs in COMT gene by applying various bioinformatics tools. Two hundred and twenty-six nsSNPs were obtained from Ensembl, HGMD, ClinVar, and dbSNP databases. Twenty-eight nsSNPs were found to be high-risk changes for protein structure. Some of them were detected in extremely conserved sequences have functional and structural properties. Besides, high-risk nsSNPs were also uncovered to change properties of native COMT protein. Our findings demonstrated the significance of COMT high-risk nsSNPs on protein structure and function. We expect that our results will be helpful in future studies concerning experimental evaluation of the COMT gene polymorphisms and/or the association between COMT polymorphisms and disease development.

Risk prediction and marker selection in nonsynonymous single nucleotide polymorphisms using whole genome sequencing data.

Despite the various existing studies about nonsynonymous single nucleotide polymorphisms (nsSNPs), genome-wide studies based on nsSNPs are rare. NsSNPs alter amino acid sequences, affect protein structure and function, and have deleterious effects. By predicting the deleterious effect of nsSNPs, we determined the total risk score per individual. Additionally, the machine learning technique was utilized to find an optimal nsSNP subset that best explains the complete nsSNP effect. A total of 16,100 nsSNPs were selected as the best representatives among 89,519 regressed nsSNPs. In the gene ontology analysis encompassing the 16,100 nsSNPs, DNA metabolic process, chemokine- and immune-related, and reproduction were the most enriched terms. We expect that our risk score prediction and nsSNP marker selection will contribute to future development of extant genome-wide association studies and breeding science more broadly.

NBN Gene Analysis and it's Impact on Breast Cancer.

Single Nucleotide Polymorphism (SNP) researches have become essential in finding out the congenital relationship of structural deviations with quantitative traits, heritable diseases and physical responsiveness to different medicines. NBN is a protein coding gene (Breast Cancer); Nibrin is used to fix and rebuild the body from damages caused because of strand breaks (both singular and double) associated with protein nibrin. NBN gene was retrieved from dbSNP/NCBI database and investigated using computational SNP analysis tools. The encrypted region in SNPs (exonal SNPs) were analyzed using software tools, SIFT, Provean, Polyphen, INPS, SNAP and Phd-SNP. The 3'ends of SNPs in un-translated region were also investigated to determine the impact of binding. The association of NBN gene polymorphism leads to several diseases was studied. Four SNPs were predicted to be highly damaged in coding regions which are responsible for the diseases such as, Aplastic Anemia, Nijmegan breakage syndrome, Microsephaly normal intelligence, immune deficiency and hereditary cancer predisposing syndrome (clivar). The present study will be helpful in finding the suitable drugs in future for various diseases especially for breast cancer.

Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene.

In SCF (Skp, Cullin, F-box) ubiquitin-protein ligase complexes, S-phase kinase 2 (SKP2) is one of the major players of F-box family, that is responsible for the degradation of several important cell regulators and tumor suppressor proteins. Despite of having significant evidence for the role of SKP2 on tumorgenesis, there is a lack of available data regarding the effect of non-synonymous polymorphisms. In this communication, the structural and functional consequences of non-synonymous single nucleotide polymorphisms (nsSNPs) of SKP2 have been reported by employing various computational approaches and molecular dynamics simulation. Initially, several computational tools like SIFT, PolyPhen-2, PredictSNP, I-Mutant 2.0 and ConSurf have been implicated in this study to explore the damaging SNPs. In total of 172 nsSNPs, 5 nsSNPs were identified as deleterious and 3 of them were predicted to be decreased the stability of protein. Guided from ConSurf analysis, P101L (rs761253702) and Y346C (rs755010517) were categorized as the highly conserved and functional disrupting mutations. Therefore, these mutations were subjected to three dimensional model building and molecular dynamics simulation study for the detailed structural consequences upon the mutations. The study revealed that P101L and Y346C mutations increased the flexibility and changed the structural dynamics. As both these mutations are located in the most functional regions of SKP2 protein, these computational insights might be helpful to consider these nsSNPs for wet-lab confirmatory analysis as well as in rationalizing future population based studies and structure based drug design against SKP2.