In silico analysis of nonsynonymous single nucleotide polymorphisms of the human adiponectin receptor 2 (ADIPOR2) gene

Computational Analysis of Non-synonymous SNPs in ATM Kinase: Structural Insights, Functional Implications, and Inhibitor Discovery

Ataxia telangiectasia-mutated (ATM) protein kinase, a key player in cellular integrity regulation, is known for its role in DNA damage response. This study investigates the broader impact of ATM on cellular processes and potential clinical manifestations arising from mutations, aiming to expand our understanding of ATM's diverse functions beyond conventional roles. The research employs a comprehensive set of computational techniques for a thorough analysis of ATM mutations. The mutation data are curated from dbSNP and HuVarBase databases. A meticulous assessment is conducted, considering factors such as deleterious effects, protein stability, oncogenic potential, and biophysical characteristics of the identified mutations. Conservation analysis, utilizing diverse computational tools, provides insights into the evolutionary significance of these mutations. Molecular docking and dynamic simulation analyses are carried out for selected mutations, investigating their interactions with Y2080D, AZD0156, and quercetin inhibitors to gauge potential therapeutic implications. Among the 419 mutations scrutinized, five (V1913C, Y2080D, L2656P, C2770G, and C2930G) are identified as both disease causing and protein destabilizing. The study reveals the oncogenic potential of these mutations, supported by findings from the COSMIC database. Notably, Y2080D is associated with haematopoietic and lymphoid cancers, while C2770G shows a correlation with squamous cell carcinomas. Molecular docking and dynamic simulation analyses highlight strong binding affinities of quercetin for Y2080D and AZD0156 for C2770G, suggesting potential therapeutic options. In summary, this computational analysis provides a comprehensive understanding of ATM mutations, revealing their potential implications in cellular integrity and cancer development. The study underscores the significance of Y2080D and C2770G mutations, offering valuable insights for future precision medicine targeting-specific ATM. Despite informative computational analyses, a significant research gap exists, necessitating essential in vitro and in vivo studies to validate the predicted effects of ATM mutations on protein structure and function.

Computational insights into NIMA-related kinase 6: unraveling mutational effects on structure and function

The NEK6 (NIMA-related kinase 6) serine/threonine kinase is a pivotal player in a multitude of cellular processes, including the regulation of the cell cycle and the response to DNA damage. Its significance extends to disease pathogenesis, as changes in NEK6 activity have been linked to the development of cancer. Non-synonymous single nucleotide polymorphisms (nsSNPs) in NEK6 have been linked to cancer as they alter the protein's native structure and function. The association between NEK6 activity and cancer development has prompted researchers to explore the effects of genetic variations within the NEK6 gene. Therefore, we utilized advanced computational tools to analyze 155 high-confidence nsSNPs in the NEK6 gene. From this analysis, 21 nsSNPs were identified as potentially harmful, raising concerns about their impact on NEK6 activity and cancer risk. These 21 mutations were then examined for structural alterations, and eight of nsSNPs (I51M, V76A, I134N, Y152D, R171Q, V186G, L237R, and C285S) were found to destabilize the protein. Among the destabilizing mutations screened, a specific mutation, R171Q, stood out due to its conserved nature. To understand its impact on the protein and conformation, all-atom molecular dynamics simulations (MDS) for 100 ns were performed for both Wildtype NEK6 (WT-NEK6) and R171Q. The simulations revealed that the R171Q variant was unstable and led to significant conformational changes in NEK6. This study provides valuable insights into NEK6 dysfunction caused by single amino acid alterations, offering a novel understanding of the molecular mechanisms underlying NEK6-related cancer progression.

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.

A Simulation Analysis and Screening of Deleterious Nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) in Sheep LEP Gene

Leptin is a polypeptide hormone produced in the adipose tissue and governs many processes in the body. Recently, polymorphisms in the LEP gene revealed a significant change in body weight regulation, energy balance, food intake, and reproductive hormone secretion. This study considers its crucial role in the regulation of the economically important traits of sheep. Several computational tools, including SIFT, Predict SNP2, SNAP2, and PROVEAN, have been used to screen out the deleterious nsSNPs. Following the screening of 11 nsSNPs in the sheep genome, 5 nsSNPs, T86M (C → T), D98N (G → A), N136T (A → C), R142Q (G → A), and P157Q (C → A), were predicted to have a significant deleterious effect on the LEP protein function, leading to phenotypic difference. The analysis of proteins’ stability change due to amino acid substitution using the I-stable, SDM, and DynaMut consistently confirmed that three nsSNPs (T86M (C → T), D98N (G → A), and P157Q (C → A)) increased protein stability. It is suggested that these three nsSNPs may enhance the evolvability of LEP protein, which is vital for the evolutionary adaptation of sheep. Our findings demonstrate that the five nsSNPs reported in this study might be responsible for sheep’s structural and functional modifications of LEP protein. This is the first comprehensive report on the sheep LEP gene. It narrow downs the candidate nsSNPs for in vitro experiments to facilitate the development of reliable molecular markers for associated traits.

In silico analyses of acetylcholinesterase (AChE) and its genetic variants in interaction with the anti-Alzheimer drug Rivastigmine

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Despite causing great social and economic impact, there is currently no cure for AD. The most effective therapy to manage AD symptoms is based on acetylcholinesterase inhibitors (AChEi), from which rivastigmine presented numerous benefits. However, mutations in AChE, which affect approximately 5% of the population, can modify protein structure and function, changing the individual response to Alzheimer's treatment. In this study, we performed computer simulations of AChE wild type and variants R34Q, P135A, V333E, and H353N, identified by one or more genome-wide association studies, to evaluate their effects on protein structure and interaction with rivastigmine. The functional effects of AChE variants were predicted using eight machine learning algorithms, while the evolutionary conservation of AChE residues was analyzed using the ConSurf server. Autodock4.2.6 was used to predict the binding modes for the hAChE-rivastigmine complex, which is still unknown. Molecular dynamics (MD) simulations were performed in triplicates for the AChE wild type and mutants using the GROMACS packages. Among the analyzed variants, P135A was classified as deleterious by all the functional prediction algorithms, in addition to occurring at highly conserved positions, which may have harmful consequences on protein function. The molecular docking results suggested that rivastigmine interacts with hAChE at the upper active-site gorge, which was further confirmed by MD simulations. Our MD findings also suggested that the complex hAChE-rivastigmine remains stable over time. The essential dynamics revealed flexibility alterations at the active-site gorge upon mutations P135A, V333E, and H353N, which may lead to strong and nonintuitive consequences to hAChE binding. Nonetheless, similar binding affinities were registered in the MMPBSA analysis for the hAChE wild type and variants when complexed to rivastigmine. Finally, our findings indicated that the rivastigmine binding to hAChE is an energetically favorable process mainly driven by negatively charged amino acids.

Computational Analysis of the Potential Impact of MTC Complex Missenses SNPs Associated with Male Infertility

Meiotic chromosomes endure rapid prophase movements that ease the formation of interhomologue recombination intermediates that drive synapsis, crossing over, and segregation process. To generate these fast moves, the meiotic telomere complex (MTC) enables telomere-inner nuclear membrane attachment during meiotic prophase I and transfers cytoskeletal signals via another complex: the LINC complex. Furthermore, disruption or mutations of any of the MTC genes (TERB1, TERB2, and MAJIN) alters telomere association with the nuclear envelope leading to impairment of homologous pairing and synapsis, a meiotic arrest, and consequently to male infertility. To decipher the effect of TERB1, TERB2, and MAJIN missense mutations on protein structure, stability, and function, different bioinformatic tools were used in this study including VEP, Mutabind2, Haddock, Prodigy, Ligplot, ConSurf, DUET and MusiteDeep. In total, thirty mutations were predicted to be deleterious using VEP web server: seventeen for TERB1, eleven for TERB2, and two for MAJIN. All these single nucleotide polymorphisms were further analyzed and only 11 SNPs (W8R, G25R, P649A, I624T, C618R, F607V, S604G, C592Y, C592R, G187W, and R53C) were found to be the most damaging by at least six software tools and exert deleterious effect on the TERB1, TERB2, and MAJIN protein structures and likely functions. They revealed high conservation, less stability, and having a role in posttranslational modifications. This in silico approach provides information to gain further insights about variants that might affect stability, change binding affinity, and edit protein-protein interactions to facilitate their identification and functional characterization associated with male infertility.

Identification of deleterious single nucleotide polymorphism (SNP)s in the human TBX5 gene & prediction of their structural & functional consequences: An in silico approach

T-box transcription factor 5 gene (TBX5) encodes the transcription factor TBX5, which plays a crucial role in the development of heart and upper limbs. Damaging single nucleotide variants in this gene alter the protein structure, disturb the functions of TBX5, and ultimately cause Holt-Oram Syndrome (HOS). By analyzing the available single nucleotide polymorphism information in the dbSNP database, this study was designed to identify the most deleterious TBX5 SNPs through insilico approaches and predict their structural and functional consequences.

Fifty-eight missense substitutions were found damaging by sequence homology-based tools: SIFT and PROVEAN, and structure homology-based tool PolyPhen-2. Various disease association meta-predictors further scrutinized these SNPs. Additionally, conservation profile of the amino acid residues, their surface accessibility, stability, and structural integrity of the native protein upon mutations were assessed. From these analyses, finally 5 SNPs were detected as the most damaging ones: [rs1565941579 (P85S), rs1269970792 (W121R), rs772248871 (V153D), rs769113870 (E208D), and rs1318021626 (I222N)]. Analyses of stop-lost, nonsense, UTR, and splice site SNPs were also conducted.

Through integrative bioinformatics analyses, this study has identified the SNPs that are deleterious to the TBX5 protein structure and have the potential to cause HOS. Further wet-lab experiments can validate these findings.

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Deleterious SNPs in the human TBX5 gene responsible for Holt-Oram Syndrome have been identified.

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58 missense and 2 nonsense SNPs were identified as deleterious.

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86 3′ UTR SNPs were predicted to be located on miRNA target sites.

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Possible effects of missense SNPs on the TBX5 protein structure have been studied.

In Silico Analysis of High-Risk Missense Variants in Human ACE2 Gene and Susceptibility to SARS-CoV-2 Infection

SARS-CoV-2 coronavirus uses for entry to human host cells a SARS-CoV receptor of the angiotensin-converting enzyme (ACE2) that catalyzes the conversion of angiotensin II into angiotensin (1-7). To understand the effect of ACE2 missense variants on protein structure, stability, and function, various bioinformatics tools were used including SIFT, PANTHER, PROVEAN, PolyPhen2.0, I. Mutant Suite, MUpro, SWISS-MODEL, Project HOPE, ModPred, QMEAN, ConSurf, and STRING. All twelve ACE2 nsSNPs were analyzed. Six ACE2 high-risk pathogenic nsSNPs (D427Y, R514G, R708W, R710C, R716C, and R768W) were found to be the most damaging by at least six software tools (cumulative score between 6 and 7) and exert deleterious effect on the ACE2 protein structure and likely function. Additionally, they revealed high conservation, less stability, and having a role in posttranslation modifications such a proteolytic cleavage or ADP-ribosylation. This in silico analysis provides information about functional nucleotide variants that have an impact on the ACE2 protein structure and function and therefore susceptibility to SARS-CoV-2.

Computational prediction of the effects of non-synonymous single nucleotide polymorphisms on the GPI-anchor transamidase subunit GPI8p of Plasmodium falciparum

Drug resistance is increasingly evolving in malaria parasites; hence, it is important to discover and establish alternative drug targets. In this context, GPI-anchor transamidase (GPI-T) is a potential drug target primarily of its crucial role in the development and survival of the parasite in the GPI anchor biosynthesis pathway. The present investigation was undertaken to explore the plausible effects of nsSNP on the structure and functions of GPI-T subunit GPI8p of Plasmodium falciparum. The GPI8p (PF3D7_1128700) was analyzed using various sequence-based and structure-based computational tools such as SIFT, PROVEAN, PredictSNP, SNAP2, I-Mutant, MuPro, ConSurf, NetSurfP, MUSTER, COACH server and STRING server. Of the 34 nsSNPs submitted for functional analysis, 18 nsSNPs (R124 L, N143 K, Y145 F, V157I, T195S, K379E, I392 K, I437 T, Y438H, N439D, Y441H, N442D, N448D, N451D, D457A, D457Y, I458 L and N460 K) were predicted to have deleterious effects on the protein GPI8p. Additionally, I-Mutant 2.0 and MuPro both showed a decrease in stability after mutation as a result of these nsSNPs, suggesting the destabilization of protein. ConSurf findings suggest that most of the regions were highly conserved. In addition, COACH server was used to predict the ligand binding sites. It was found that no mutation was present at the predicted ligand binding site. The results of the STRING database showed that the protein GPI8p interacts with those proteins which either involve the biosynthetic process of attaching GPI anchor to protein or GPI anchor. The present study suggested that the GPI8p could be a novel target for anti-malarial drugs, which provides significant details for further experimentation.

Computational studies on bacterial secondary metabolites against breast cancer

Microbes exist in the human body provide more benefits by modulating metabolic processes, immunity, and signal transduction. However, microbial dysbiosis with harmful bacterial species can cause chronic inflammation and cancers. Hence human probiotics were recently paid more attention to immune responses, therapy, and diagnosis. Breast cancer is the second leading cancer worldwide and causes more death in women. The role of breast microbiome secondary metabolites in breast cancer is poorly studied. Research shows that breast has a specific microbiome inhabited with particular bacterial species. More significantly probiotics produced from breast microbiomes may act as a potential biomarker for breast cancer diagnosis. Hence this computational research aimed at the effect of chosen metabolites on breast cancer cell receptor G-protein-coupled bile acid receptor, Gpbar1 (TGR5). The current research suggested that cadaverine, succinate, p-cresol, and its derivatives could be used as a molecular marker in the diagnosis of breast cancer.

Evaluating the effect of BDNF Val66Met polymorphism on complex formation with HAP1 and Sortilin1 via structural modeling

Brain derived neurotrophic factor (BDNF) has a critical role in the neurogenesis, differentiation, survival of the neurons, regulation of the appetite, and energy homeostasis. Two key proteins, Huntingtin associated protein-1 (HAP1) and sortilin1, regulate the intracellular trafficking and stabilization of the precursor proBDNF through interaction with its prodomain region and mark it for secretion. Evidence suggests that the most frequent single nucleotide polymorphism (SNP) of BDNF gene (rs6265) has been associated with different psychiatric, neurodegenerative and eating disorders. In this study, structural bioinformatics and molecular dynamics (MD) simulations were applied, in order to get precise insights into the impact of Val66Met polymorphism on the proBDNF structure and its interaction with HAP1 and Sortilin1. Homology modeling, structure validation, refinement and also protein-protein docking were performed using appropriate servers. The stability, the fluctuations and the compactness of protein complexes were measured by MD simulation parameters including root mean square deviation (RMSD), root mean square fluctuation (RMSF) and Radius of gyration (Rg), respectively. The mutant proBDNF complexes with HAP1 and Sortilin1 revealed higher RMSD and RMSF values and also variable R_g over time compared with wild-type proBDNF. These computational results indicated that, wild-type proBDNF possessed more stable structure in binding with HAP1 and Sortilin1 compared with its mutant form. Therefore, Val66Met SNP could be deleterious due to making structural changes. It may cause a decrease in proBDNF secretion, which could possibly lead to different psychiatric, neurodegenerative and eating disorders. Further experimental lab studies are required for a more accurate conclusion.

Comprehensive Computational Analysis of Protein Phenotype Changes Due to Plausible Deleterious Variants of Human SPTLC1 Gene

Genetic variations found in the coding and non-coding regions of a geneare known to influence the structure as well as the function of proteins. Serine palmitoyltransferase long chain subunit 1 a member of α-oxoamine synthase family is encoded by SPTLC1 gene which is a subunit of enzyme serine palmitoyltransferase (SPT). Mutations in SPTLC1 have been associated with hereditary sensory and autonomic neuropathy type I (HSAN-I). The exact mechanism through which these mutations elicit protein phenotype changes in terms of structure, stability and interaction with other molecules is unknown. Thus, we aimed to perform a comprehensive computational analysis of single nucleotide polymorphisms (SNPs) of SPTLC1 to prioritize a list of potential deleterious SNPs and to investigate the protein phenotype change due to functional polymorphisms. In this study, a diverse set of SPTLC1 SNPs were collected and scrutinized to categorize the potential deleterious variants. Our study concordantly identified 21 non- synonymous SNPs as pathogenic and deleterious that might induce alterations in protein structure, flexibility and stability. Moreover, evaluation of frameshift, 3’ and 5’ UTR variants shows c.*1302T> Gas effective. This comprehensive in silico analysis of systematically characterized list of potential deleterious variants could open avenues as primary filter to substantiate plausible pathogenic structural and functional impact of variants.

In silico analysis of SLC3A1 and SLC7A9 mutations in Iranian patients with Cystinuria

Cystinuria is an autosomal recessive defect in reabsorptive transport of cystine and the dibasic amino acids ornithine, arginine, and lysine from renal tubule and small intestine. Mutations in two genes: SLC3A1, encoding the heavy chain rbAT of the renal cystine transport system and SLC7A9, the gene of its light chain b^{0, +} AT have a crucial role in the diseases. In our previous studies from Iranian populations with Cystinuria totally six and eleven novel mutations respectively identified in SLC3A1 and SLC7A9 genes. In this study, we conducted an in silico functional analysis to explore the possible association between these genetic mutations and Cystinuria. MutationTaster, PolyPhen-2, PANTHER, FATHMM. PhDSNP and MutPred was applied to predict the degree of pathogenicity for the missense mutations. Furthermore, Residue Interaction Network (RIN) and Intron variant analyses was performed using Cytoscape and Human Slicing Finder softwares. These genetic variants can provide a better understanding of genotype-phenotype relationships in patients with Cystinuria. In the future, the findings may also facilitate the development of new molecular diagnostic markers for the diseases.

In Silico Analyses Reveal the Relationship Between SIX1/EYA1 Mutations and Conotruncal Heart Defects

Conotruncal heart defects (CTDs) represent a group of severe and complicated congenital cardiovascular malformations and require opportune clinical interventions once diagnosed. Occurrence of CTD is related to the functional abnormality of the second heart field (SHF), and variants of genes which regulate the development of the second heart field have been recognized as the main genetic factors leading to CTDs. Previous studies indicated that transcriptional complex SIX1/EYA1 may contribute to SHF development, and SIX1/EYA1 knockout mice exhibited a series of conotruncal malformations. Here, we recruited and sequenced 600 Chinese conotruncal heart defect patients and 300 controls. We screened out one novel SIX1 mutation (SIX1-K134R) and four EYA1 rare mutations (EYA1-A227T, EYA1-R296H, EYA1-Q397R, EYA1-G426S), all variants were present only in the case cohort, and the mutated sites were highly conserved. We then analyzed mutations by software including Sift, PolyPhen-2, PROVEAN, Mutation Taster, HOPE, and SWISS-PdbViewer. The results showed that the mutations had varying degrees of pathogenic risk, protein properties, spatial conformations, and domain functions which might be altered or influenced. Through biological and in silico analyses, our study suggests an association between SIX1/EYA1 mutations and cardiovascular malformations, SIX1/EYA1 mutations might be partially responsible for CTDs.