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Functional and structural analysis of non-synonymous single nucleotide polymorphisms (nsSNPs) in the MYB oncoproteins associated with human cancer.

ABSTRACT: MYB proteins are highly conserved DNA-binding domains (DBD) and mutations in MYB oncoproteins have been reported to cause aberrant and augmented cancer progression. Identification of MYB molecular biomarkers predictive of cancer progression can be used for improving cancer management. To address this, a biomarker discovery pipeline was employed in investigating deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in predicting damaging and potential alterations on the properties of proteins. The nsSNP of the MYB family; MYB, MYBL1, and MYBL2 was extracted from the NCBI database. Five in silico tools (PROVEAN, SIFT, PolyPhen-2, SNPs&GO and PhD-SNP) were utilized to investigate the outcomes of nsSNPs. A total of 45 nsSNPs were predicted as high-risk and damaging, and were subjected to PMut and I-Mutant 2.0 for protein stability analysis. This resulted in 32 nsSNPs with decreased stability with a DDG score lower than - 0.5, indicating damaging effect. G111S, N183S, G122S, and S178C located within the helix-turn-helix (HTH) domain were predicted to be conserved, further posttranslational modifications and 3-D protein analysis indicated these nsSNPs to shift DNA-binding specificity of the protein thus altering the protein function. Findings from this study would help in the field of pharmacogenomic and cancer therapy towards better intervention and management of cancer.

SUBMITTER: Lim SW

PROVIDER: S-EPMC8683427 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Mutation analysis of pathogenic non-synonymous single nucleotide polymorphisms (nsSNPs) in WFS1 gene through computational approaches.

Project description:A single base changes causing a change to the amino acid sequence of the encoded protein, which is defined as non-synonymous single nucleotide polymorphisms (nsSNPs). Many of the nsSNPs can cause disease, and these nsSNPs are considered as pathogenic mutations. In the study, the high-risk nsSNPs of WFS1 and their influence on the structure and function of wolframin protein were predicted by multiple bioinformatics software. We obtained 13 high-risk nsSNPs of WFS1. All the 13 high-risk nsSNPs are highly conserved residues with a conservative score of 9 or 8 and mostly may cause a decrease in protein stability. The high-risk nsSNPs have an important effect on not only amino acid size, charge and hydrophobicity, but also protein's spatial structure. Among these, 11 nsSNPs had been previously published or cited and 2 nsSNPs (G695S and E776K) had not been reported to date. The two novel variants increased or decreased hydrogen bonds. In conclusion, through different computational tools, it is presumed that the mechanism of pathogenic WFS1 nsSNPs should include the changes of physicochemical properties, significant structural changes and abnormal binding with functional partners. We accomplished the computational-based screening and analysis for deleterious nsSNPs in WFS1, which had important reference value and could contribute to further studies of the mechanism of WFS1 related disease. The computational analysis has many advantages, but the results should be identified by further experimental studies in vivo and in vitro.

| S-EPMC10130013 | biostudies-literature

Computational and Mass Spectrometry-Based Approach Identify Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in JMJD6.

Project description:The jumonji domain-containing protein 6 (JMJD6) gene catalyzes the arginine demethylation and lysine hydroxylation of histone and a growing list of its known substrate molecules, including p53 and U2AF65, suggesting a possible role in mRNA splicing and transcription in cancer progression. Mass spectrometry-based technology offers the opportunity to detect SNP variants accurately and effectively. In our study, we conducted a combined computational and filtration workflow to predict the nonsynonymous single nucleotide polymorphisms (nsSNPs) present in JMJD6, followed by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and validation. The computational approaches SIFT, PolyPhen-2, SNAP, I-Mutant 2.0, PhD-SNP, PANTHER, and SNPS&GO were integrated to screen out the predicted damaging/deleterious nsSNPs. Through the three-dimensional structure of JMJD6, H187R (rs1159480887) was selected as a candidate for validation. The validation experiments showed that the mutation of this nsSNP in JMJD6 obviously affected mRNA splicing or the transcription of downstream genes through the reduced lysyl-hydroxylase activity of its substrates, U2AF65 and p53, further indicating the accuracy of this prediction method. This research provides an effective computational workflow for researchers with an opportunity to select prominent deleterious nsSNPs and, thus, remains promising for examining the dysfunction of proteins.

| S-EPMC8347302 | biostudies-literature

In silico analysis of non-synonymous single nucleotide polymorphisms (nsSNPs) in the human GJA3 gene associated with congenital cataract.

Project description:BACKGROUND:Gap junction protein alpha 3 (GJA3), an important pathogenic gene of congenital cataracts, encodes the transmembrane protein connexin46, which functions as an intercellular channel for voltage and chemical gating by forming dodecamers. This study systematically collected nsSNP information for the GJA3 gene from SNP databases and literature and screened for nsSNPs with high risks of pathogenicity. RESULTS:A total of 379 nsSNPs of GJA3 were identified. A total of 88 high-risk pathogenic GJA3 nsSNPs were found, including 31 published nsSNPs associated with congenital cataracts and 57 novel nsSNPs predicted by all eight online tools. The 88 high-risk pathogenic mutations, which are related to 67 amino acids in the wild-type sequences, cause a decrease in protein stability according to I-Mutant 3.0, MUpro and INPS. G2 and R33 were predicted to participate in post-translational modification and ligand binding by ModPred, RaptorX Binding and COACH. Additionally, high-risk mutations were likely to involve highly conserved sites, random coils, alpha helixes, and extracellular loops and were accompanied by changes in amino acid size, charge, hydrophobicity and spatial structure. CONCLUSIONS:Eighty-eight high-risk pathogenic nsSNPs of GJA3 were screened out in the study, 57 of which were newly reported. The combination of multiple in silico tools is highly efficient for targeting pathogenic sites.

| S-EPMC7060521 | biostudies-literature

Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene.

Project description:Here we report an in-silico approach for identification, characterization and validation of deleterious non-synonymous SNPs (nsSNPs) in the interleukin-8 gene using three steps. In first step, sequence homology-based genetic analysis of a set of 50 coding SNPs associated with 41 rsIDs using SIFT (Sorting Intolerant from Tolerant) and PROVEAN (Protein Variation Effect Analyzer) identified 23 nsSNPs to be putatively damaging/deleterious in at least one of the two tools used. Subsequently, structure-homology based PolyPhen-2 (Polymorphism Phenotyping) analysis predicted 9 of 23 nsSNPs (K4T, E31A, E31K, S41Y, I55N, P59L, P59S, L70P and V88D) to be damaging. According to the conditional hypothesis for the study, only nsSNPs that score damaging/deleterious prediction in both sequence and structural homology-based approach will be considered as 'high-confidence' nsSNPs. In step 2, based on conservation of amino acid residues, stability analysis, structural superimposition, RSMD and docking analysis, the possible structural-functional relationship was ascertained for high-confidence nsSNPs. Finally, in a separate analysis (step 3), the IL-8 deregulation has also appeared to be an important prognostic marker for detection of patients with gastric and lung cancer. This study, for the first time, provided in-depth insights on the effects of amino acid substitutions on IL-8 protein structure, function and disease association.

| S-EPMC5529537 | biostudies-literature

Ranking non-synonymous single nucleotide polymorphisms based on disease concepts.

Project description:As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are 'disease agnostic' but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk.

| S-EPMC4083756 | biostudies-literature

Evaluation of non-synonymous NPPA single nucleotide polymorphisms in atrial fibrillation.

Project description:Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is an important cause of morbidity and mortality. A genetic mutation in the NPPA gene, which encodes the atrial natriuretic peptide, has been identified as the putative causative factor in a family with an autosomal dominant pattern of inheritance for AF. Two common single nucleotide polymorphisms (SNPs) in NPPA, rs5063 and rs5065, result in amino acid changes of the primary peptide and have been previously implicated in conditions associated with AF, including stroke and hypertension. Recently, the rs5063 SNP has been reported to confer an increased risk of AF development in a Chinese population. We sought to examine the associations of both rs5063 and rs5065 with AF in two separate North American cohorts of European ancestry.Patients with early-onset AF, along with healthy controls, were recruited at the University of Ottawa Heart Institute (UOHI) and the Massachusetts General Hospital (MGH). Study participants were genotyped for rs5063 and rs5065 using a combination of restriction fragment length polymorphism analysis and DNA microarrays. The study genotyped a total of 620 AF cases and 2446 healthy controls. The UOHI arm of the study identified an odds ratio (OR) of 0.72 [95% confidence interval (CI): 0.42-1.24] for rs5063, whereas an OR of 1.33 (95% CI: 0.80-2.21) was observed in the MGH arm. The combined OR approximated unity (OR 0.99; 95% CI: 0.54-1.80). Analysis of rs5065 revealed an OR of 1.12 (95% CI: 0.84-1.48) in UOHI, 1.08 (95% CI 0.80-1.45) in MGH, and 1.10 (95% CI 0.90-1.35) when combined.Common non-synonymous genetic variants within NPPA in these two large North American cohorts of European ancestry are not associated with the development of AF.

| S-EPMC2910603 | biostudies-literature

Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.

Project description:Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs). By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysing disease-associated nsSNPs use primarily sequence and evolutionary information, while structural information is relatively less exploited. In order to explore the potential of such information, we developed a structure-based approach, Bongo (Bonds ON Graph), to predict structural effects of nsSNPs. Bongo considers protein structures as residue-residue interaction networks and applies graph theoretical measures to identify the residues that are critical for maintaining structural stability by assessing the consequences on the interaction network of single point mutations. Our results show that Bongo is able to identify mutations that cause both local and global structural effects, with a remarkably low false positive rate. Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5%) similar to that of PolyPhen (PPV, 77.2%) and PANTHER (PPV, 72.2%). As the Bongo method is solely structure-based, our results indicate that the structural changes resulting from nsSNPs are closely associated to their pathological consequences.

| S-EPMC2447880 | biostudies-literature

Genetic association analyses of non-synonymous single nucleotide polymorphisms in diabetic nephropathy.

Project description:Aims/hypothesisDiabetic nephropathy, characterised by persistent proteinuria, hypertension and progressive kidney failure, affects a subset of susceptible individuals with diabetes. It is also a leading cause of end-stage renal disease (ESRD). Non-synonymous (ns) single nucleotide polymorphisms (SNPs) have been reported to contribute to genetic susceptibility in both monogenic disorders and common complex diseases. The objective of this study was to investigate whether nsSNPs are involved in susceptibility to diabetic nephropathy using a case-control design.MethodsWhite type 1 diabetic patients with (cases) and without (controls) nephropathy from eight centres in the UK and Ireland were genotyped for a selected subset of nsSNPs using Illumina's GoldenGate BeadArray assay. A chi (2) test for trend, stratified by centre, was used to assess differences in genotype distribution between cases and controls. Genomic control was used to adjust for possible inflation of test statistics, and the False Discovery Rate method was used to account for multiple testing.ResultsWe assessed 1,111 nsSNPs for association with diabetic nephropathy in 1,711 individuals with type 1 diabetes (894 cases, 817 controls). A number of SNPs demonstrated a significant difference in genotype distribution between groups before but not after correction for multiple testing. Furthermore, neither subgroup analysis (diabetic nephropathy with ESRD or diabetic nephropathy without ESRD) nor stratification by duration of diabetes revealed any significant differences between groups.Conclusions/interpretationThe nsSNPs investigated in this study do not appear to contribute significantly to the development of diabetic nephropathy in patients with type 1 diabetes.

| S-EPMC2687720 | biostudies-literature

Structural modeling and in silico analysis of non-synonymous single nucleotide polymorphisms of human 3β-hydroxysteroid dehydrogenase type 2.

Project description:Single-nucleotide polymorphisms (SNPs), a most common type of genetic mutations, result from single base pair alterations. Non-synonymous SNPs (nsSNP) occur in the coding regions of a gene and result in single amino acid substitution which might have the potential to affect the function as well as structure of the corresponding protein. In human the 3β-hydroxysteroid dehydrogenases/Δ(4,5)-isomerase type 2 (HSD3B2) is an important membrane-bound enzyme involved in the dehydrogenation and Δ(4,5)-isomerization of the Δ(5)-steroid precursors into their respective Δ(4)-ketosteroids in the biosynthesis of steroid hormones such as glucocorticoids, mineralocorticoids, progesterone, androgens, and estrogens in tissues such as adrenal gland, ovary, and testis. Most of the nsSNPs of HSD3B2 are still uncharacterized in terms of their disease causing potential. So, this study has been undertaken to explore and extend the knowledge related to the effect of nsSNPs on the stability and function of the HSD3B2. In this study sixteen nsSNP of HSD3B2 were subjected to in silico analysis using nine different algorithms: SIFT, PROVEAN, PolyPhen, MutPred, SNPeffect, nsSNP Analyzer, PhD SNP, stSNP, and I Mutant 2.0. The results obtained from the analysis revealed that the prioritization of diseases associated amino acid substitution as evident from possible alteration in structure-function relationship. Structural phylogenetic analysis using ConSurf revealed that the functional residues are highly conserved in human HSD3B2; and most of the disease associated nsSNPs are within these conserved residues. Structural theoritical models of HSD3B2 were created using HHPred, Phyre2 and RaptorX server. The predicted models were evaluated to get the best one for structural understanding of amino acid substitutions in three dimensional spaces.

| S-EPMC4539073 | biostudies-literature

Prediction of deleterious non-synonymous single-nucleotide polymorphisms of human uridine diphosphate glucuronosyltransferase genes.

Project description:UDP glucuronosyltransferases (UGTs) are an important class of Phase II enzymes involved in the metabolism and detoxification of numerous xenobiotics including therapeutic drugs and endogenous compounds (e.g. bilirubin). To date, there are 21 human UGT genes identified, and most of them contain single-nucleotide polymorphisms (SNPs). Non-synonymous SNPs (nsSNPs) of the human UGT genes may cause absent or reduced enzyme activity and polymorphisms of UGT have been found to be closely related to altered drug clearance and/or drug response, hyperbilirubinemia, Gilbert's syndrome, and Crigler-Najjar syndrome. However, it is unlikely to study the functional impact of all identified nsSNPs in humans using laboratory approach due to its giant number. We have investigated the potential for bioinformatics approach for the prediction of phenotype based on known nsSNPs. We have identified a total of 248 nsSNPs from human UGT genes. The two algorithms tools, sorting intolerant from tolerant (SIFT) and polymorphism phenotyping (PolyPhen), were used to predict the impact of these nsSNPs on protein function. SIFT classified 35.5% of the UGT nsSNPs as "deleterious"; while PolyPhen identified 46.0% of the UGT nsSNPs as "potentially damaging" and "damaging". The results from the two algorithms were highly associated. Among 63 functionally characterized nsSNPs in the UGTs, 24 showed altered enzyme expression/activities and 45 were associated with disease susceptibility. SIFT and Polyphen had a correct prediction rate of 57.1% and 66.7%, respectively. These findings demonstrate the potential use of bioinformatics techniques to predict genotype-phenotype relationships which may constitute the basis for future functional studies.

| S-EPMC2758119 | biostudies-literature

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