In silico characterization of structural and functional impact of the deleterious SNPs on FSHR gene

492-499FSHR is an important gene which plays a major role in the development of secondary sex characteristics and influences the female reproductive cycle by regulating the Follicle Stimulating Hormone. Though this gene and its protein are extensively studied, no attempts have been made yet to methodically analyze the variants in this gene. One of the chief objectives during the analysis of human genetic variation is to distinguish between the Single Nucleotide Polymorphisms (SNPs) that are functionally neutral from those that contribute to the disorder. To predict the possible impact of SNPs on the FSHR structure and function, data were obtained from NCBI (dbSNP and dbVar) and validated manually. Various bioinformatics tools were used to predict the alterations at transcriptional, post transcriptional stages and protein interaction. Around 38 variants reported by NCBI Variation Viewer were sorted by SIFT and 14 of them were reported damaging, 13 were reported to be either benign or damaging by PROVEAN and Panther. From these 13 SNPs, the most damaging (11 SNPs) were modeled using Pymol and the energy difference between the native and mutated structure was calculated by Swiss PDB – Viewer. Based on our analysis, we have reported potential candidate SNPs for the FSHR gene involved in the regulation of ovarian pathophysiology

In silico characterization of structural and functional impact of the deleterious SNPs on FSHR gene

FSHR is an important gene which plays a major role in the development of secondary sex characteristics and influences the female reproductive cycle by regulating the Follicle Stimulating Hormone. Though this gene and its protein are extensively studied, no attempts have been made yet to methodically analyze the variants in this gene. One of the chief objectives during the analysis of human genetic variation is to distinguish between the Single Nucleotide Polymorphisms (SNPs) that are functionally neutral from those that contribute to the disorder. To predict the possible impact of SNPs on the FSHR structure and function, data were obtained from NCBI (dbSNP and dbVar) and validated manually. Various bioinformatics tools were used to predict the alterations at transcriptional, post transcriptional stages and protein interaction. Around 38 variants reported by NCBI Variation Viewer were sorted by SIFT and 14 of them were reported damaging, 13 were reported to be either benign or damaging by PROVEAN and Panther. From these 13 SNPs, the most damaging (11 SNPs) were modeled using Pymol and the energy difference between the native and mutated structure was calculated by Swiss PDB – Viewer. Based on our analysis, we have reported potential candidate SNPs for the FSHR gene involved in the regulation of ovarian pathophysiology

Computational Refinement of Functional Single Nucleotide Polymorphisms Associated with ATM Gene

gene are the most common forms of genetic variations that account for various forms of cancer. However, the extent to which SNPs interferes with the gene regulation and affects cancer susceptibility remains largely unknown. gene. gene function can aid in better understanding of genetic differences in disease susceptibility

In Silico Analysis of Single Nucleotide Polymorphism (SNPs) in Human β-Globin Gene

Single amino acid substitutions in the globin chain are the most common forms of genetic variations that produce hemoglobinopathies- the most widespread inherited disorders worldwide. Several hemoglobinopathies result from homozygosity or compound heterozygosity to beta-globin (HBB) gene mutations, such as that producing sickle cell hemoglobin (HbS), HbC, HbD and HbE. Several of these mutations are deleterious and result in moderate to severe hemolytic anemia, with associated complications, requiring lifelong care and management. Even though many hemoglobinopathies result from single amino acid changes producing similar structural abnormalities, there are functional differences in the generated variants. Using in silico methods, we examined the genetic variations that can alter the expression and function of the HBB gene. Using a sequence homology-based Sorting Intolerant from Tolerant (SIFT) server we have searched for the SNPs, which showed that 200 (80%) non-synonymous polymorphism were found to be deleterious. The structure-based method via PolyPhen server indicated that 135 (40%) non-synonymous polymorphism may modify protein function and structure. The Pupa Suite software showed that the SNPs will have a phenotypic consequence on the structure and function of the altered protein. Structure analysis was performed on the key mutations that occur in the native protein coded by the HBB gene that causes hemoglobinopathies such as: HbC (E→K), HbD (E→Q), HbE (E→K) and HbS (E→V). Atomic Non-Local Environment Assessment (ANOLEA), Yet Another Scientific Artificial Reality Application (YASARA), CHARMM-GUI webserver for macromolecular dynamics and mechanics, and Normal Mode Analysis, Deformation and Refinement (NOMAD-Ref) of Gromacs server were used to perform molecular dynamics simulations and energy minimization calculations on β-Chain residue of the HBB gene before and after mutation. Furthermore, in the native and altered protein models, amino acid residues were determined and secondary structures were observed for solvent accessibility to confirm the protein stability. The functional study in this investigation may be a good model for additional future studies

In Silico Analysis Shows That Single Aminoacid Variations In Rhesus Macacque Fcγreceptor Affect Protein Stability And Binding Affinity To IgG1

Rhesus macaques are a widely used animal model of human diseases and related immune responses. Fc receptors (FcRs) mediate the interaction between antibody molecules and innate killing mechanisms, consequently eliminating the pathogen. In rhesus macaques, FcRs are highly polymorphic. To evaluate the potential influence of FcgR polymorphisms on the interaction with antibody molecules, we performed in silico analysis using SIFT, Provean, nsSNPAnalyzer, I-Mutant, MuSTAB and iPTREE-STAB web servers. V20G in FcγRI, I137K in FcγRII and I233V in FcγRIII were further analyzed structurally using FOLD-X, AMMP and Chimera to calculate changes in folding and interaction energy and for structure visualization. Results from our analysis suggest that the selected variations destabilize protein structure. Additionally, Q32R increases the binding affinity of FcγRI, whereas A131T decreases the binding affinity of FcγRII towards IgG1. Together, our results indicate that these substitutions might influence effector and regulatory mechanisms resulting from antibody/FcR interactions

Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in the Human Interleukin 12B Gene: Identification and Structural Characterization

Background: Interleukin -12B (IL12B) polymorphism has been identified as a factor in the development of various Immunological disorders and cancer. The objective of this study was to identify the non-synonymous SNPs (nsSNPs) with the strongest predicted negative impact on the function of the IL12B protein.Methods: We employed a variety of computational methods, including SIFT, PolyPhen2, PROVEAN, SNAP2 to determine the functional impact of nsSNPs. Also, In order to investigate the potential association of nsSNPs in the IL12B gene with disease, a computational analysis was conducted using PhD-SNP, SNP&GO, and Pmut. Additionally, I-mutant and MuPro were employed to predict protein stability, while ConSurf was used to identify functional domains and conserved amino acid residues within the protein. Furthermore, SOPMA was used in combination with Project Hope and MutPred2 to predict the impact of mutations on both the structure and function of proteins. Finally, we used GeneMania to analyze the gene-gene interactions of the IL12B gene with other genes.Results: Our results indicate that nine nsSNPs (G72C, G86C, C90R, C131S, Y136D, P235L, V254G, Y258H and P259S) were found to be potentially deleterious in the IL12B gene.Conclusion: Our study emphasizes the significance of identifying functional and structural polymorphisms in the IL12B gene, as they may reveal potential therapeutic targets and provide insight into the underlying mechanisms of related diseases. Further experimental investigation is necessary to fully explore the role of these nsSNPs in disease pathogenesis.Keywords: Interleukin 12B; deleterious nsSNPs; Polymorphisms.; Computational analysis

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

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

Modelling functional and structural impact of non-synonymous single nucleotide polymorphisms of the DQA1 gene of three Nigerian goat breeds

The DQA1 gene is a member of the highly polymorphic MHC class II locus that is responsible for the differences among individuals in immune response to infectious agents. In this study, the authors performed a comprehensive computational analysis of the functional and structural impact of non-synonymous or amino acid-changing single nucleotide polymorphisms (SNPs) (nsSNPs) that are deleterious to the DQA1 protein in Nigerian goats. A 310-bp fragment of exon 2 of the DQA1 gene was amplified and sequenced in 27 unrelated animals that are representative of three major Nigerian goat breeds (nine each of West African Dwarf, Red Sokoto, and Sahel of both sexes) using genomic DNA. Forty-two nsSNPs were identified from the alignment of the deduced amino acid sequences. Based on the PANTHER, PROVEAN and PolyPhen-2 algorithms, there was consensus in identifying the mutants I26D, E114V and V115F as being deleterious. Further, differences between the native and the mutant proteins in the subsequent molecular trajectory analysis (stabilizing and flexible residue composition, total grid energy, solvation energy, coulombic energy, solvent accessibility, and protein-protein interaction properties) revealed E114V and V115F to be highly deleterious. Combined mutational analysis comparing the amutant (I26D, E114V and V115F mutations collectively) with the native protein also showed changes that could affect protein function and structure. Further wet-lab confirmatory analysis in a pathological association study involving a larger population of goats is required at the DQA1 locus. This would lay a sound foundation for breeding disease-resistant individuals in the future. Keywords: Goats, in silico, mutants, protein, tropic

In Silico Analysis of the Structural and Functional Consequences of Polymorphic Amino Acid Substitutions in the Cattle HSF1 Protein

Heat stress causes a decrease in the productivity of livestock by negatively affecting some important economic features such as fertility, growth and milk production. The heat shock transcription factor 1 (HSF1) gene plays a key role in the regulation of the stress response. Therefore, the present study aimed to predict the most deleterious non-synonymous single nucleotide polymorphisms (nsSNP) on the cattle HSF1 gene via in silico analyses. Out of 170 nsSNPs in the HSF1 gene, 14 SNPs were predicted as deleterious by all the nine the vast majority of SNPs predicted to be deleterious were evolutionary conserved. Protein structural analyses were performed I-Mutant, Mupro, Hope Project server, RaptorX and Swiss Model server. The 12 amino acid substitutions (V15G, F18L, L19R, K21M, I35T, V46E, V56G, F61L, A67D, Y76D, V81G, L112P) in the DNA binding region of the cattle HSF1 protein were predicted to be highly deleterious. The P112 variant was predicted to disrupt an a-helix structure. It was determined that the two amino acid changes (K21M, Y76D) on the surface of the protein were different in terms of hydrophobicity, charge, and size. These variants (M21, D76) might hamper the protein's interaction with the heat shock elements

Assessing the relationship between the in silico predicted consequences of 97 missense mutations mapping to 68 genes related to lipid metabolism and their association with porcine fatness traits

In general, the relationship between the predicted functional consequences of missense mutations mapping to genes known to be involved in human diseases and the severity of disease manifestations is weak. In this study, we tested in pigs whether missense single nucleotide polymorphisms (SNPs), predicted to have consequences on the function of genes related to lipid metabolism are associated with lipid phenotypes. Association analysis demonstrated that nine out of 72 nominally associated SNPs were classified as “highly” or “very highly consistent” in silico-predicted functional mutations and did not show association with lipid traits expected to be affected by inactivation of the corresponding gene. Although the lack of endophenotypes and the limited sample size of certain genotypic classes might have limited to some extent the reach of the current study, our data indicate that present-day bioinformatic tools have a modest ability to predict the impact of missense mutations on complex phenotypes.info:eu-repo/semantics/publishedVersio