Deleterious Non-synonymous Single Nucleotide Polymorphisms Research Articles

Computational Analysis of Deleterious nsSNPs in INS Gene Associated with Permanent Neonatal Diabetes Mellitus.

Insulin gene mutations affect the structure of insulin and are considered a leading cause of neonatal diabetes and permanent neonatal diabetes mellitus PNDM. These mutations can affect the production and secretion of insulin, resulting in inadequate insulin levels and subsequent hyperglycemia. Early discovery or prediction of PNDM can aid in better management and treatment. The current study identified potential deleterious non-synonymous single nucleotide polymorphisms nsSNPs in the INS gene. The analysis of the nsSNPs in the INS gene was conducted using bioinformatics tools by implementing computational algorithms including SIFT, PolyPhen2, SNAP2, SNPs & GO, PhD-SNP, MutPred2, I-Mutant, MuPro, and HOPE tools to investigate the prediction of the potential association between nsSNPs in the INS gene and PNDM. Three mutations, C96Y, P52R, and C96R, were shown to potentially reduce the stability and function of the INS protein. These mutants were subjected to MDSs for structural analysis. Results suggested that these three potential pathogenic mutations may affect the stability and functionality of the insulin protein encoded by the INS gene. Therefore, these changes may influence the development of PNDM. Further researches are required to fully understand the various effects of mutations in the INS gene on insulin synthesis and function. These data can aid in genetic testing for PNDM to evaluate its risk and create treatment and prevention strategies in personalized medicine.

Identification of Damaging and Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in the Orang Asli, Jakun

The study of genetics and its relation to different disorders has recently been getting remarkable attention worldwide. Non-synonymous single-nucleotide polymorphisms (nsSNPs) are coding variants that introduce amino acid alterations to proteins. Since nsSNPs have various effects on protein structure and function, these changes can significantly impact human health. The primary purpose of this research is to examine the relationship between diseases and Jakun nsSNP mutations. A total of 10 samples with 24,604 nsSNPs were used in the study. Several in silico bioinformatics tools were used to classify the high-risk nsSNPs that might affect the structure and function of proteins in the Jakun tribe. The in silico tools PROVEAN, SIFT and PolyPhen2 identified 1395 high-risk nsSNPs. Further analysis with the DAVID annotation tool revealed that these related genes are associated with cardiovascular diseases, myocardial infarction, Alzheimer's and obesity. Other bioinformatics tools were then used to infer the protein structures of these high-risk nsSNPs. From SNP&GO and PhD-SNPs, the results conclude that there are four damaging nsSNPs in the Jakun tribe. The damaging nsSNPs and the mutation positions are MTHFR (A222V), ESR2 (G445S), ACSM3 (G460R) and UGT3A1 with two mutation sites, C121G and C67G. This study using in silico tools can be the basis for increasing medical discoveries, reducing lab costs and helping with clinical trials for further studies by providing genetically related information.

Identification of molecular signatures and molecular dynamics simulation of highly deleterious missense variants of key autophagy regulator beclin 1: a computational based approach

Beclin 1 is a key autophagy regulator that also plays significant roles in other intracellular processes such as vacuolar protein sorting. Beclin 1 protein functions as a scaffold in the formation of a multiprotein assemblage during autophagy. Beclin 1 is involved in various diseases such as cancers, neurodegenerative and autophagy-related disorders. In this study, we have used various in silico tools to scan beclin 1 at the molecular level to find its molecular signatures. We have predicted and analysed deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of beclin 1 causing alterations in its structure and also affecting its interactions with other proteins. In total, twelve coding region deleterious variants were predicted using sequence-based tools and nine were predicted using various structure-based tools. The molecular dynamics (MD) simulations revealed an altered stability of the native structure due to the introduction of mutations. Destabilization of beclin 1 ECD domain was observed due to nsSNPs W300R and E302K. Beclin 1 deleterious nsSNPs were predicted to show significant effects on beclin 1 interactions with ATG14L1, UVRAG and VPS34 proteins and were also predicted to alter the protein-protein interface of beclin 1 complexes. Additionally, beclin 1 was predicted to have thirty-one potential phosphorylation and three ubiquitination sites. In conclusion, the molecular details of beclin 1 could help in the better understanding of its functioning. The study of nsSNPs and their effect on beclin 1 and its interactions might aid in understanding the basis of anomalies caused due to beclin 1 dysfunction. Communicated by Ramaswamy H. Sarma

Prediction of Deleterious Non-Synonymous Single Nucleotide Polymorphism of Cathelicidin

Background: Cathelicidin, a human host defense peptide, plays a salubrious role in innate host defense against human pathogens. Despite the extensive studies on the antimicrobial function of Cathelicidin, there is a lack of information on this peptide's deleterious single nucleotide polymorphisms (SNPs) that potentially alter the disease susceptibility and hence the current study. Objective: To predict Cathelicidin's structural and functional deleterious non-synonymous single nucleotide polymorphisms. Methods: The non-synonymous SNPs of Cathelicidin were investigated using computational prediction tools like SIFT, Polyphen, PROVEAN, MusiteDeep, I-Mutant, and STRING. Results: The present study predicted 23 potentially harmful nsSNP of Cathelicidin. Among these, 14 were highly conserved, 8 were average conserved, and 1 alone was variable. Phosphorylation was observed in serine and threonine residues using post-translational modification. Further mutation 3D predicted 11 clustered and 13 covered mutations in cathelicidin variants. The structural distribution of high-risk nsSNPs predicted 80 alpha helixes, 0 random coils, 19 extended strands, and 4 beta turns. Among 23 predicted deleterious SNPs, 9 nsSNPs alone showed mutation effect based on the HOPE structural and functional analysis. The direct functional interaction pattern of Cathelicidin with other proteins, FPR2, PRTN3, TLR9, IGF1R, and JUN, was observed. Conclusion: The identified deleterious nsSNPs could help understand the mutation effect of Cathelicidin in disease susceptibility and drug discovery.

In-silico assessment of high-risk non-synonymous SNPs in ADAMTS3 gene associated with Hennekam syndrome and their impact on protein stability and function

Hennekam Lymphangiectasia–Lymphedema Syndrome 3 (HKLLS3) is a rare genetical disorder caused by mutations in a few genes including ADAMTS3. It is characterized by lymphatic dysplasia, intestinal lymphangiectasia, severe lymphedema and distinctive facial appearance. Up till now, no extensive studies have been conducted to elucidate the mechanism of the disease caused by various mutations. As a preliminary investigation of HKLLS3, we sorted out the most deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) that might affect the structure and function of ADAMTS3 protein by using a variety of in silico tools. A total of 919 nsSNPs in the ADAMTS3 gene were identified. 50 nsSNPs were predicted to be deleterious by multiple computational tools. 5 nsSNPs (G298R, C567Y, A370T, C567R and G374S) were found to be the most dangerous and can be associated with the disease as predicted by different bioinformatics tools. Modelling of the protein shows it can be divided into segments 1, 2 and 3, which are connected by short loops. Segment 3 mainly consists of loops without substantial secondary structures. With prediction tools and molecular dynamics simulation, some SNPs were found to significantly destabilize the protein structure and disrupt the secondary structures, especially in segment 2. The deleterious effects of mutations in segment 1 are possibly not from destabilization but from other factors such as the change in phosphorylation as suggested by post-translational modification (PTM) studies. This is the first-ever study of ADAMTS3 gene polymorphism, and the predicted nsSNPs in ADAMST3, some of which have not been reported yet in patients, will serve for diagnostic purposes and further therapeutic implications in Hennekam syndrome, contributing to better diagnosis and treatment.

Computational analysis of non-synonymous single nucleotide polymorphism in the bovine PKLR gene

Pyruvate kinase (PKLR) is a potential candidate gene for milk production traits in cows. The main aim of this work is to investigate the potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in the PKLR gene by using several computational tools. In silico tools including SIFT, Polyphen-2, SNAP2 and Panther indicated only 18 nsSNPs out of 170 were considered deleterious. The analysis of proteins’ stability change due to amino acid substitution performed by the use of the I-mutant, MUpro, CUPSTAT, SDM and Dynamut confirmed that 9 nsSNPs decreased protein stability. ConSurf analysis predicted that all 18 nsSNPs were evolutionary moderately or highly conserved. Two different domains of PKLR protein were revealed by the InterPro tool with 12 nsSNPs positioned in the Pyruvate Kinase barrel domain and 6 nsSNP present in the Pyruvate Kinase C Terminal. The PKLR 3D model was predicted by MODELLER software and validated via Ramachandran plot and Prosa which indicated a good quality model. The analysis of energy minimizations for the native and mutated structures was performed by SWISS PDB viewer with GROMOS 96 program and showed that 3 structural and 4 functional residues had total energy higher than the native model. These findings indicate that these mutant structures (rs441424814, rs449326723, rs476805413, rs472263384, rs474320860, rs475521477, rs441633284) were less stable than the native model. Molecular Dynamics simulations were performed to confirm the impact of nsSNPs on the protein structure and function. The present study provides useful information about functional SNPs that have an impact on PKLR protein in cattle. Communicated by Ramaswamy H. Sarma

An In silico Approach towards Finding the Cancer-Causing Mutations in Human MET Gene.

Mesenchymal-epithelial transition (MET) factor is a proto-oncogene encoding tyrosine kinase receptor with hepatocyte growth factor (HGF) or scatter factor (SF). It is found on the human chromosome number 7 and regulates the diverse cellular mechanisms of the human body. The impact of mutations occurring in the MET gene is demonstrated by their detrimental effects on normal cellular functions. These mutations can change the structure and function of MET leading to different diseases such as lung cancer, neck cancer, colorectal cancer, and many other complex syndromes. Hence, the current study focused on finding deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) and their subsequent impact on the protein's structure and functions, which may contribute to the emergence of cancers. These nsSNPs were first identified utilizing computational tools like SIFT, PROVEAN, PANTHER-PSEP, PolyPhen-2, I-Mutant 2.0, and MUpro. A total of 45359 SNPs of MET gene were accumulated from the database of dbSNP, and among them, 1306 SNPs were identified as non-synonymous or missense variants. Out of all 1306 nsSNPs, 18 were found to be the most deleterious. Moreover, these nsSNPs exhibited substantial effects on structure, binding affinity with ligand, phylogenetic conservation, secondary structure, and post-translational modification sites of MET, which were evaluated using MutPred2, RaptorX, ConSurf, PSIPRED, and MusiteDeep, respectively. Also, these deleterious nsSNPs were accompanied by changes in properties of MET like residue charge, size, and hydrophobicity. These findings along with the docking results are indicating the potency of the identified SNPs to alter the structure and function of the protein, which may lead to the development of cancers. Nonetheless, Genome-wide association study (GWAS) studies and experimental research are required to confirm the analysis of these nsSNPs.

Forecasting most deleterious nsSNPs in human TLR9 gene and their cumulative impact on biophysical features of the protein using in silico approaches

In women, the uterine cervix and corpus uteri are two main suspects, playing a major role in cancer-associated-mortality. Immunologically, Toll-like receptors (TLRs) associated with the innate immune system, can recognize pathogens and induce immune responses against pathogens. Cellularly, TLR9 expression occurs in immune system cells including macrophages, natural killer cells, dendritic cells, and other antigen-presenting cells. TLR9 recognizes and interacts with viral and bacterial DNA comprising cytosine-phosphate-guanine (CpG) dideoxynucleotide motif. The current study is designed to identify the most deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) in the TLR9 gene and to delineate their deleterious effect on the structural and functional features of proteins at the molecular level. Based on the implementation of various computational tools and algorithms eight most deleterious nsSNPs (P139H, R257C, C265Y, L283P, G514D, L544Q, H566Y, and W670R) have been identified in the human TLR9 gene as potentially damaging SNPs. Further, our study suggests highly conserved patterns at deleterious nsSNPs sites could influence protein stability and its functional features. Additionally, this study identifies two nsSNPs (G514D and W670R) associated with the severity of Uterine corpus endometrial carcinoma. In support of our computational findings, the validation of key results using polymerase chain reaction and other experimental methods is warranted in the Indian population. In general, this study might be able to delineate the guideline for identifying the most damaging SNPs and enhances the understating of the risk factors for cancer and disease susceptibilities.

Predicting Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) of HRAS Gene and In Silico Evaluation of Their Structural and Functional Consequences towards Diagnosis and Prognosis of Cancer.

The Harvey rat sarcoma (HRAS) proto-oncogene belongs to the RAS family and is one of the pathogenic genes that cause cancer. Deleterious nsSNPs might have adverse consequences at the protein level. This study aimed to investigate deleterious nsSNPs in the HRAS gene in predicting structural alterations associated with mutants that disrupt normal protein-protein interactions. Functional and structural analysis was employed in analyzing the HRAS nsSNPs. Putative post-translational modification sites and the changes in protein-protein interactions, which included a variety of signal cascades, were also investigated. Five different bioinformatics tools predicted 33 nsSNPs as "pathogenic" or "harmful". Stability analysis predicted rs1554885139, rs770492627, rs1589792804, rs730880460, rs104894227, rs104894227, and rs121917759 as unstable. Protein-protein interaction analysis revealed that HRAS has a hub connecting three clusters consisting of 11 proteins, and changes in HRAS might cause signal cascades to dissociate. Furthermore, Kaplan-Meier bioinformatics analyses indicated that the HRAS gene deregulation affected the overall survival rate of patients with breast cancer, leading to prognostic significance. Thus, based on these analyses, our study suggests that the reported nsSNPs of HRAS may serve as potential targets for different proteomic studies, diagnoses, and therapeutic interventions focusing on cancer.

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 profiling of deleterious non-synonymous single nucleotide polymorphisms of ARSA (arylsulphatase A) for enhanced diagnosis of metachromatic leukodystrophy

Metachromatic Leukodystrophy (MLD) is a metabolic sickness described by inadequate action of the lysosomal protein arylsulfatase A (ARSA), which is encoded by the ARSA gene. ARSA catalyzes the desulfation of sphingolipid sulfatide. Sulfatide is found in high focuses in the myelin sheath of the sensory system because of ARSA lack happens. Sulfatide is amassed in the sensory system influences the oligodendrocytes and results in Neurodegeneration. Single nucleotide polymorphisms (SNPs) are the easiest type of hereditary varieties that happen at a higher recurrence and are indicated as equivalent and non-interchangeable SNPs based on their impacts on the amino acids. The non-synonymous SNPs (ns SNPs) are known to be injurious or sickness-causing varieties since they modify protein arrangement, design and function. This study receives an orderly In-silico way to deal with foresee the malicious SNPs that are related to illness conditions. It is derived that 31 SNPs are profoundly harmful, among which the five potentially deleterious Mutations of ARSA may influence underlying steadiness and change articulation of the protein. Hence, this examination expects to portray a competitor quality from 517 SNPs of ARSA quality which may help in the early finding of Metachromatic Leukodystrophy.

Prediction of Deleterious Non-Synonymous Single Nucleotide Polymorphisms (Nssnps) of Human TLR7 Gene

Toll-like receptors (TLRs) play a key role in innate immune response activation against viruses. TLR7, one of the TLRs family, is potentially important in controlling viral infection and the production of vaccines against the virus. The wide spectrum of discrepancy in response to antiviral drugs among different populations which is emerged by some pandemics like COVID-19 might be due to their different TLR7 single nucleotide polymorphisms (SNPs). The present study aimed to investigate the consequences of 401 non-synonymous missense SNPs (nsSNPs) within TLR7 on its protein structure, stability, and function by using specific bioinformatics tools. Seven bioinformatics tools were used to investigate 401 TLR7 nsSNPs from the dbSNP database. The results showed that the six variations, rs1171508003 (R262H), rs35160120 (F580S), rs968155471 (H587Q), rs202028806 (Y871D), rs1331496205 (W933S), and rs181600414 (R1004W), were found to be extremely deleterious by all of the employed bioinformatics tools. All six variations showed an impact on the protein’s structure, function, and stability. Among them, Y871D (rs202028806) and R1004W (rs181600414) were revealed as the most damaging nsSNPs. This study suggested that the predicted six damaging variants of TLR7 could indirectly or directly destabilize the structure of protein and deviate its function to some extent.

Targeting Adenylate Cyclase Family: New Concept of Targeted Cancer Therapy.

The adenylate cyclase (ADCY) superfamily is a group of glycoproteins regulating intracellular signaling. ADCYs act as key regulators in the cyclic adenosine monophosphate (cAMP) signaling pathway and are related to cell sensitivity to chemotherapy and ionizing radiation. Many members of the superfamily are detectable in most chemoresistance cases despite the complexity and unknownness of the specific mechanism underlying the role of ADCYs in the proliferation and invasion of cancer cells. The overactivation of ADCY, as well as its upstream and downstream regulators, is implicated as a major potential target of novel anticancer therapies and markers of exceptional responders to chemotherapy. The present review focuses on the oncogenic functions of the ADCY family and emphasizes the possibility of the mediating roles of deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) in ADCY as a prognostic therapeutic target in modulating resistance to chemotherapy and immunotherapy. It assesses the mediating roles of ADCY and its counterparts as stress regulators in reprogramming cancer cell metabolism and the tumor microenvironment. Additionally, the well-evaluated inhibitors of ADCY-related signaling, which are under clinical investigation, are highlighted. A better understanding of ADCY-induced signaling and deleterious nsSNPs (p.E1003K and p.R1116C) in ADCY6 provides new opportunities for developing novel therapeutic strategies in personalized oncology and new approaches to enhance chemoimmunotherapy efficacy in treating various cancers.

Prediction and expression analysis of deleterious nonsynonymous SNPs of Arabidopsis ACD11 gene by combining computational algorithms and molecular docking approach.

Accelerated cell death 11 (ACD11) is an autoimmune gene that suppresses pathogen infection in plants by preventing plant cells from becoming infected by any pathogen. This gene is widely known for growth inhibition, premature leaf chlorosis, and defense-related programmed cell death (PCD) in seedlings before flowering in Arabidopsis plant. Specific amino acid changes in the ACD11 protein’s highly conserved domains are linked to autoimmune symptoms including constitutive defensive responses and necrosis without pathogen awareness. The molecular aspect of the aberrant activity of the ACD11 protein is difficult to ascertain. The purpose of our study was to find the most deleterious mutation position in the ACD11 protein and correlate them with their abnormal expression pattern. Using several computational methods, we discovered PCD vulnerable single nucleotide polymorphisms (SNPs) in ACD11. We analysed the RNA-Seq data, identified the detrimental nonsynonymous SNPs (nsSNP), built genetically mutated protein structures and used molecular docking to assess the impact of mutation. Our results demonstrated that the A15T and A39D mutations in the GLTP domain were likely to be extremely detrimental mutations that inhibit the expression of the ACD11 protein domain by destabilizing its composition, as well as disrupt its catalytic effectiveness. When compared to the A15T mutant, the A39D mutant was more likely to destabilize the protein structure. In conclusion, these mutants can aid in the better understanding of the vast pool of PCD susceptibilities connected to ACD11 gene GLTP domain activation.

Selective mGlu1 Potentiation Reverses Cortical Disinhibition and Schizophrenia‐like Social and Cognitive Deficits

There is a critical need to develop novel therapeutic strategies for addressing the negative and cognitive symptom domains in schizophrenia (SCZ). Multiple clinical and preclinical studies support the hypothesis that loss of GABAergic inhibitory transmission in the prefrontal cortex (PFC) and other forebrain regions may play a critical role in the pathophysiological changes underlying cognitive and social behavioral deficits in SCZ patients. Recently, we demonstrated that activation of the mGlu1 subtype of metabotropic glutamate receptor increases inhibitory transmission in the PFC by selective excitation of somatostatin‐expressing GABA interneurons. Genetic studies in humans reveal an association between deleterious non‐synonymous single nucleotide polymorphisms (nsSNPS) in of the human gene encoding mGlu1 (GRM1) and SCZ, raising the possibility that mGlu1 signaling is critical to the function of brain circuits underlying symptoms associated with this disorder.Here, we examine whether novel positive allosteric modulators (PAMs) selective for mGlu1 reverse electrophysiological effects and behavioral deficits in an MK‐801‐induced animal model of SCZ. We used ex vivo whole‐cell patch‐clamp electrophysiology to show that MK‐801 decreased the frequency of spontaneous inhibitory postsynaptic currents onto layer V pyramidal cells of the PFC, and this cortical disinhibition was reversed by mGlu1 activation. Furthermore, acute MK‐801 treatment induces inhibitory deficits onto PFC‐basolateral amygdala (BLA), but not PFC‐nucleus accumbens, projecting layer V pyramidal cells that can be reversed by selective mGlu1 activation. PFC‐BLA projections are known to contribute functionally to social‐approach behavior, while the BLA plays a critical role in object recognition and cognition broadly. We found that the mGlu1 PAM VU6004909 effectively reversed MK801‐induced deficits in social interaction and social novelty preference in a three‐chamber assay. Additionally, VU6004909 restored novel object recognition following MK‐801 treatment. In combination with our previous reports that mGlu1 PAMs reverse deficits in inhibitory transmission in the PFC, the current study provides strong evidence for mGlu1 PAMs as a promising novel approach for the treatment of social and cognitive deficits in SCZ patients.

Presentation of potential genes and deleterious variants associated with non-syndromic hearing loss: a computational approach.

Non-syndromic hearing loss (NSHL) is a common hereditary disorder. Both clinical and genetic heterogeneity has created many obstacles to understanding the causes of NSHL. The present study has attempted to ravel the genetic aetiology in NSHL progression and to screen out potential target genes using computational approaches. The reported NSHL target genes (2009‒2020) have been studied by analyzing different biochemical and signaling pathways, interpretation of their functional association network, and discovery of important regulatory interactions with three previously established miRNAs in the human inner ear as well as in NSHL such as miR-183, miR-182, and miR-96. This study has identified SMAD4 and SNAI2 as the most putative target genes of NSHL. But pathogenic and deleterious non-synonymous single nucleotide polymorphisms discovered within SMAD4 is anticipated to have an impact on NSHL progression. Additionally, the identified deleterious variants in the functional domains of SMAD4 added a supportive clue for further study. Thus, the identified deleterious variant i.e., rs377767367 (G491V) in SMAD4 needs further clinical validation. The present outcomes would provide insights into the genetics of NSHL progression.

Most Deleterious Missense Variants of Angiotensin-converting Enzyme 2 Gene have Extremely Low Frequencies and a Little Impact on the Binding Affinity with the SARS-CoV-2 Spike

Angiotensin-converting enzyme 2 (ACE2) is a well-established functional host receptor for the highly devastating ongoing pandemic of coronavirus disease 2019 (COVID-19). This protein provides the entry point through which COVID-19 hooks and infects human cells. A damaged ACE2 could alter the rate of this viral infection. This study was conducted to predict the most deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) on ACE2, assess their frequency among populations, and evaluate their effect on the binding with the SARS-CoV-2 spike. All sequence-based in silico tools indicated damaging impacts of V184G, A191P, P235R, P263S, G268C, G377E, Y515C, G466W, and L595V. Structure-based tools showed damaging effects of C141Y, Y158H, G173D, Y207C, I233N, Y252C, Y252N, L291K, M376T, G377E, M383T, N397D, E398K, G405E, L418S, N437H, G448E, W461R, V463D, Y515C, I544N, L570S, L585P, F588S, and N599K. All these risky amino acid alterations were found in extremely low-frequency worldwide. Docking showed few effects of these nsSNPs in altering the binding affinity of ACE2 with SARS-CoV-2 spike. G377E and Y515C showed the highest damaging impacts on the biological activity of ACE2. Though Y515C caused higher affinity than other risky SNPs to bind with spike, no remarkable alteration was observed in this interaction. This entails that the risky SNPs of ACE2 exert low-frequency deleterious impacts on this enzyme without being necessarily involved in the interaction with SARS-CoV-2 spike. To the best of our knowledge, this is the first comprehensive computation that predicted the low effectiveness of altering the ACE2-spike interaction due to the distant positions they occupy away from ACE2-spike interactions.

Screening for Deleterious non-synonymous SNPs in Human CCL21 Gene using in-silico analysis

Rheumatoid arthritis (RA) is a chronic, systematic, and progressive inflammatory disorder, causing severe damage to joints and hence increase mortality. The Chemokine (C-C motif) ligand 21 (CCL21), a member cytokines family, is involved in immuno-inflammatory and regulatory processes. Therefore, identifying the important SNPs (single nucleotide polymorphisms) in the CCL21 gene is of key importance to evaluate their structural and functional significance and to discover novel therapeutic targets for immune-related diseases, including RA. In this study, we used in silico approaches for identifying the most damaging non-synonymous SNPs (nsSNPs), playing a significant structural and functional role in CCL21 protein. The primary tools used for this study included PROVEAN, SNPs&GO, SIFT and PolyPhen2. Other tools, its stability, Structure and functional effect as well as the conservation profile, were verified using I-Mutant, MutPred, and ConSurf. The site of post-translational modification also predicted. The 3-D modeling of proteins was carried out using I-TASSER which were then visualized in Chimera v1.11. Furthermore, the gene-gene interactions were predicted using STRING and gene MANIA. It was observed that the nsSNPs D30Y (rs753133670), I62N (rs1170851787), R75C (rs759733358), R75S (rs776954599) and A83V (rs776954599) were the most damaging nsSNPs in the CCL21 gene. These nsSNPs might have a significant role in CCL2 protein’s malfunctioning and possibly causing different autoimmune diseases including RA. Our study concluded that, to study the correlation of the CCL21 gene with certain autoimmune disorders, i.e. Crohn’s Disease (CD), RA and other immune-associated diseases, these SNPs could be the most important ones. In addition, these SNPs need to be studied in animal models and cell cultures in association with certain diseases, to identify if they could be of use for the gene therapy and pharmacogenomics.

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

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.

A comprehensive in silico investigation into the nsSNPs of Drd2 gene predicts significant functional consequences in dopamine signaling and pharmacotherapy

DRD2 is a neuronal cell surface protein involved in brain development and function. Variations in the Drd2 gene have clinical significance since DRD2 is a pharmacotherapeutic target for treating psychiatric disorders like ADHD and schizophrenia. Despite numerous studies on the disease association of single nucleotide polymorphisms (SNPs) in the intronic regions, investigation into the coding regions is surprisingly limited. In this study, we aimed at identifying potential functionally and pharmaco-therapeutically deleterious non-synonymous SNPs of Drd2. A wide array of bioinformatics tools was used to evaluate the impact of nsSNPs on protein structure and functionality. Out of 260 nsSNPs retrieved from the dbSNP database, initially 9 were predicted as deleterious by 15 tools. Upon further assessment of their domain association, conservation profile, homology models and inter-atomic interaction, the mutant F389V was considered as the most impactful. In-depth analysis of F389V through Molecular Docking and Dynamics Simulation revealed a decline in affinity for its native agonist dopamine and an increase in affinity for the antipsychotic drug risperidone. Remarkable alterations in binding interactions and stability of the protein–ligand complex in simulated physiological conditions were also noted. These findings will improve our understanding of the consequence of nsSNPs in disease-susceptibility and therapeutic efficacy.