Deleterious SNPs Research Articles

Investigating the role of keratin proteins and microbial associations in hereditary and pathogenic alopecia.

The purpose of this research was to identify the role of keratin proteins in causing inherited as well as pathogenic alopecia, pinpoint deleterious SNPs, and predict structural changes affecting protein-protein interactions in hair disorders. To elucidate the role of keratin proteins and genetic mutations in alopecia by analyzing protein structures through bioinformatics and identifying a mutation in the LPAR6 gene. It sought to identify the microorganisms linked to alopecia and conducted a comprehensive bioinformatics analysis of proteins with unknown experimental structures and molecular simulation analysis. The study identified a genetic mutation (c.188A > T, p.Asp63Val) in the LPAR6 gene associated with hereditary hair loss. Pathogenic alopecia was identified to be associated with S. aureus and two ic keratinophilic fungi namely M. canis, and T. violaceum. Additionally, among 14 proteins lacking prior structural information, four proteins namely Keratin, type II cuticular Hb3 (KR1), Keratin, type II cuticular Hb6 (KR2), Keratin, type II cytoskeletal 74 (KR3) and Keratin, type II cuticular Hb1 (KR4) exhibited common 'K-head' and 'F' domains. Docking analysis revealed five distinct binding sites (C1-C5) for each protein. The 'K-head' displayed the highest predicted binding affinities with Vina scores of -5.6 for KR2 and - 4.7 for KR4 whereas the 'F' domain showed Vina scores of -6.0 for KR3 and - 5.7 for KR2. This research underscores the crucial role of keratin proteins in both hereditary and pathogenic alopecia, emphasizing their significance for future investigations.

Structure, function and stability analysis on potential deleterious mutation ensemble in glyceraldehyde 3-phosphate dehydrogenase (GAPDH) for early detection of LUAD

AimsLung adenocarcinoma (LUAD) is the most prominent histological subtype among the lung cancer which is a leading cause in the cancer mortality rate. High mutational and glycolytic rates are the major reported alterations in the lung cancer. Here in our study we are elucidating the structural and functional role of key glycolytic enzyme Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and associated SNPs in LUAD progression. Materials and methodsOur gene expression analysis reveals high expression of GAPDH in the LUAD. In silico tools and analysis were used for the identification and characterization of the deleterious SNPs. Molecular Docking and dynamics simulations (MDS) studies characterized the structural consequences of prioritized deleterious mutations. Key findingsThe sequence based analysis to identify SNPs in GAPDH resulted in 28 deleterious SNPs and 6 SNPs among them showed deleterious and damaging effect. The structural based analysis resulted in 2 stabilizing SNPs of rs ids rs11549328 (D39Y) and rs200102749 (S51Y) in the conserved domain. The IDR and PTM analysis of the GAPDH sequence resulted an IDR region from 191 to 194 positions with an IDR score of 0.511, 0.520, 0.517 and 0.503 with the PTM modifications. SignificanceThe identified deleterious SNPs (D39Y and S51Y) fall in the functional and conserved domain of GAPDH. In addition, the existence of PTMs within the IDR region of the GAPDH may contribute to its enhanced glycolytic activity in LUAD. The results of our study provide potential background deleterious mutants the pathological aspect of GAPDH in LUAD progression.

Potential Aromatase Inhibitors from Centella asiatica with Non-synonymous SNPs - A Computational Approach.

Aromatase Inhibitors are used in the treatment of breast cancer as they are effective in decreasing the concentration of estrogen. As SNPs impact the efficacy or toxicity of drugs, evaluating them with mutated conformations would help in identifying potential inhibitors. In recent years, phytocompounds have been under scrutiny for their activity as potential inhibitors. In this study, we have evaluated Centella asiatica compounds for their activity on aromatase with clinically significant SNPs: rs700519, rs78310315 and rs56658716. Using AMDock v.1.5.2, which uses the AutoDock Vina engine, molecular docking simulations were carried out, and the docked complexes were analyzed for their chemical interactions such as Polar contacts using PyMol v2.5. The mutated conformations of the protein and force field energy differences were computationally derived using SwissPDB Viewer. PubChem, dbSNP and ClinVar databases were used to retrieve the compounds and SNPs. ADMET prediction profile was generated using admetSAR v1.0. Docking simulations of the C. asiatica compounds with the native and mutated conformations showed that out of the obtained fourteen phytocompounds, Isoquercetin, Quercetin and9H-Fluorene-2-carboxylic acid were able to dock with best scores in terms of binding affinity (-8.4kcal/mol), Estimated Ki (0.6 µM) values and Polar Contacts in both native and mutated conformations (3EQM, 5JKW, 3S7S). Our computational analyses predict that the deleterious SNPs did not impact the molecular interactions of Isoquercetin, Quercetin and 9H-Fluorene-2-carboxylic acid, providing better lead compounds for further evaluation as potential aromatase inhibitors.

Multi-omics analysis of a case of congenital microtia reveals aldob and oxidative stress associated with microtia etiology.

Microtia is reported to be one of the most common congenital craniofacial malformations. Due to the complex etiology and the ethical barrier of embryonic study, the precise mechanisms of microtia remain unclear. Here we report a rare case of microtia with costal chondrodysplasia based on bioinformatics analysis and further verifications on other sporadic microtia patients. One hundred fourteen deleterious insert and deletion (InDel) and 646 deleterious SNPs were screened out by WES, candidate genes were ranked in descending order according to their relative impact with microtia. Label-free proteomic analysis showed that proteins significantly different between the groups were related with oxidative stress and energy metabolism. By real-time PCR and immunohistochemistry, we further verified the candidate genes between other sporadic microtia and normal ear chondrocytes, which showed threonine aspartase, cadherin-13, aldolase B and adiponectin were significantly upregulated in mRNA levels but were significantly lower in protein levels. ROS detection and mitochondrial membrane potential (∆ Ψ m) detection proved that oxidative stress exists in microtia chondrocytes. Our results not only spot new candidate genes by WES and label-free proteomics, but also speculate for the first time that metabolism and oxidative stress may disturb cartilage development and this might become therapeutic targets and potential biomarkers with clinical usefulness in the future.

Intensified Selection, Elevated Mutations, and Reduced Adaptation Potential in Wild Barley in Response to 28 Years of Global Warming

Many studies have investigated the threat of climate change on wild plants, but few have investigated the genetic responses of crop wild relative populations under threat. We characterized the genetic responses of 10 wild barley (Hordeum spontaneum K. Koch) populations in Israel, sampling them in 1980 and again in 2008, through exome capture and RNA-Seq analyses. Sequencing 48 wild barley samples of these populations representing two collection years generated six million SNPs, and SNP annotations identified 12,926 and 13,361 deleterious SNPs for 1980 and 2008 samples, respectively. The assayed wild barley samples displayed intensified selective sweeps and elevated deleterious mutations across seven chromosomes in response to 28 years of global warming. On average, the 2008 samples had lower individual and population mutational burdens, but the population adaptation potential was estimated to be lower in samples from 2008 than in 1980. These findings highlight the genetic risks of losing wild barley under global warming and support the need to conserve crop wild relatives.

In-silico investigation of porcine X-chromosome reveals deleterious non-synonymous SNPS

in-silico investigation of porcine X-chromosome reveals deleterious non-synonymous SNPS

Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes.

Colorectal cancers are the world's third most commonly diagnosed type of cancer. Currently, there are several diagnostic and treatment options to combat it. However, a delay in detection of the disease is life-threatening. Additionally, a thorough analysis of the exomes of cancers reveals potential variation data that can be used for early disease prognosis. By utilizing a comprehensive computational investigation, the present study aimed to reveal mutations that could potentially predispose to colorectal cancer. Ten colorectal cancer exomes were retrieved. Quality control assessments were performed using FastQC and MultiQC, gapped alignment to the human reference genome (hg19) using Bowtie2 and calling the germline variants using Haplotype caller in the GATK pipeline. The variants were filtered and annotated using SIFT and PolyPhen2 successfully categorized the mutations into synonymous, non-synonymous, start loss and stop gain mutations as well as marked them as possibly damaging, probably damaging and benign. This mutational profile helped in shortlisting frequently occurring mutations and associated genes, for which the downstream multi-dimensional expression analyses were carried out. Our work involved prioritizing the non-synonymous, deleterious SNPs since these polymorphisms bring about a functional alteration to the phenotype. The top variations associated with their genes with the highest frequency of occurrence included LGALS8, CTSB, RAD17, CPNE1, OPRM1, SEMA4D, MUC4, PDE4DIP, ELN and ADRA1A. An in-depth multi-dimensional downstream analysis of all these genes in terms of gene expression profiling and analysis and differential gene expression with regard to various cancer types revealed CTSB and CPNE1 as highly expressed and overregulated genes in colorectal cancer. Our work provides insights into the various alterations that might possibly lead to colorectal cancer and suggests the possibility of utilizing the most important genes identified for wet-lab experimentation.

Determination of Deleterious SNPs in NUDT15 Gene Related to Acute Lymphoblastic Leukemia by using Bioinformatics Tools

Aim: In acute lymphoblastic leukemia (ALL), thiopurine group drugs are the most basic drugs and are included in almost all treatment protocols, especially in maintenance treatment. The mechanism of action of thioguanine nucleotides is to enter the DNA structure in cells, disrupt DNA synthesis, and trigger programmed cell death. The impact of deleterious SNPs on nucleotide triphosphate diphosphatase protein regarding ALL is not yet fully understood. In this study, it was aimed to determine the possible deleterious impacts of missense variants in the NUDT15 gene on protein structure and stabilization that play a significant role in susceptibility to the disease, using modern bioinformatics software. Method: To access SNPs in the NUDT15, it was used National Center for Biotechnology Information (NCBI), Single Nucleotide Polymorphism Database (dbSNP). In bioinformatics tools used in this study included SIFT, PolyPhen-2, PROVEAN, SNAP2, and PANTHER, followed by I-Mutant, HOPE, and STRING. Results: The results of the analysis showed that in a total of 6663 SNPs in the NUDT15, 6 variants have been identified as ‘deleterious’. According to the I-Mutant software, 4 deleterious SNPs decreased protein stability while 2 deleterious SNPs increased protein stability. In the HOPE database analysis, E115G, E57G, F52L, and K33N mutant amino acids were found to be smaller and more hydrophobic than wild-type amino acids, while G53R and G145D mutant amino acids were found to be larger. Thus, all variations resulted in alterations in the net charge on the NUDT15 protein. Conclusion: Data on NUDT15 variants will contribute to the prediction of the patient’s response to thiopurine drugs in future studies, to a better understanding of the patient’s susceptibility to drug interactions, and ultimately to obtaining information about the prognosis.

In silico analysis of deleterious SNPs of human DCDC2 gene and their impacts on subsequent protein-protein interactions

DCDC2 is a clinically significant protein causing a number of neurological disorders and, hence, is an important protein for analysis. In this study, multiple tools were employed to identify missense SNPs that are harmful to the protein itself and destabilize the interaction of this protein with tubulin subunits. After analyzing all 378 missense SNPs retrieved from the dbSNP database, thirteen were found to have harmful effects on the protein, which are L20P, R23L, G25W, G25R, D26E, I36N, G60E, P68S, G83R, T174I, L179R, R186G, V208E. Among these, four SNPs- T174I, L179R, R186G, and V208E were suggested to be significantly destabilizing for the interaction of the C-DC domain with microtubule, and three SNPs- L20P, D26E, and G83R for the interaction of N-DC domain with microtubule. Based on the total ΔΔG value, SNP R186G and L20P seem most destabilizing for the interaction of the C-DC and N-DC domains. These SNPs are found to affect the protein negatively by analysis using several computational tools. Genetic association and protein-protein interaction studies focused on these SNPs can reveal new findings about dyslexia or other neurodevelopmental disorders. J. Bangladesh Acad. Sci. 47(2); 181-193: December 2023

In Silico Evaluation of Coding and Non-Coding nsSNPs in the Thrombopoietin Receptor (MPL) Proto-Oncogene: Assessing Their Influence on Protein Stability, Structure, and Function.

The thrombopoietin receptor (MPL) gene is a critical regulator of hematopoiesis, and any alterations in its structure or function can result in a range of hematological disorders. Non-synonymous single nucleotide polymorphisms (nsSNPs) in MPL have the potential to disrupt normal protein function, prompting our investigation into the most deleterious MPL SNPs and the associated structural changes affecting protein-protein interactions. We employed a comprehensive suite of bioinformatics tools, including PredictSNP, InterPro, ConSurf, I-Mutant2.0, MUpro, Musitedeep, Project HOPE, STRING, RegulomeDB, Mutpred2, CScape, and CScape Somatic, to analyze 635 nsSNPs within the MPL gene. Among the analyzed nsSNPs, PredictSNP identified 28 as significantly pathogenic, revealing three critical functional domains within MPL. Ten of these nsSNPs exhibited high conservation scores, indicating potential effects on protein structure and function, while 14 were found to compromise MPL protein stability. Although the most harmful nsSNPs did not directly impact post-translational modification sites, 13 had the capacity to substantially alter the protein's physicochemical properties. Some mutations posed a risk to vital protein-protein interactions crucial for hematological functions, and three non-coding region nsSNPs displayed significant regulatory potential with potential implications for hematopoiesis. Furthermore, 13 out of 21 nsSNPs evaluated were classified as high-risk pathogenic variants by Mutpred2. Notably, amino acid alterations such as C291S, T293N, D295G, and W435C, while impactful on protein stability and function, were deemed non-oncogenic "passenger" mutations. Our study underscores the substantial impact of missense nsSNPs on MPL protein structure and function. Given MPL's central role in hematopoiesis, these mutations can significantly disrupt hematological processes, potentially leading to a variety of disorders. The identified high-risk pathogenic nsSNPs may hold promise as potential biomarkers or therapeutic targets for hematological diseases. This research lays the foundation for future investigations into the MPL gene's role in the realm of hematological health and diseases.

A computational analysis to evaluate deleterious SNPs of GSK3β, a multifunctional and regulatory protein, for metabolism, wound healing, and migratory processes

This study focused on GSK-3β, a critical serine/threonine kinase with diverse cellular functions. However, there is limited understanding of the impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on its structure and function. Through an exhaustive in-silico investigation 12 harmful nsSNPs were predicted from a pool of 172 acquired from the NCBI dbSNP database using 12 established tools that detects deleterious SNPs. Consistently, these nsSNPs were discovered in locations with high levels of conservation. Notably, the three harmful nsSNPs F67C, A83T, and T138I were situated in the active/binding site of GSK-3β, which may affect the protein's capacity to bind to substrates and other proteins. Molecular dynamics simulations revealed that the F67C and T138I mutants had stable structures, indicating rigidness, whereas the A83T mutant was unstable. Analysis of secondary structures revealed different modifications in all mutant forms, which may affect the stability, functioning, and interactions of the protein. These mutations appear to alter the structural dynamics of GSK-3β, which may have functional ramifications, such as the formation of novel secondary structures and variations in coil-to-helix transitions. In conclusion, this study illuminates the possible structural and functional ramifications of these GSK-3 nsSNPs, revealing how protein compactness, stiffness, and interactions may affect biological activities.

Genetic insights into obesity: in silico identification of pathogenic SNPs in MBOAT4 gene and their structural molecular dynamics consequences

Membrane Bound O-Acyltransferase Domain-Containing 4 (MBOAT4) protein catalyzes ghrelin acylation, leading to prominent ghrelin activity, hence characterizing its role as an anti-obesity target. We extracted 625 exonic SNPs from the ENSEMBL database and one phenotype-based missense mutation associated with obesity (A46T) from the HGMD (Human Gene Mutation Database). These were differentiated on deleterious missense SNPs of the MBOAT4 gene through MAF (minor allele frequency: <0.01) cut-off criteria in relation to some bioinformatics-based supervised machine learning tools. We found 8 rare-coding and harmful missense SNPs. The consensus classifier (PredictSNP) tool predicted that the SNP (G57S, C: rs561065025) was the most pathogenic. Several trained in silico algorithms have predicted decreased protein stability [ΔΔG (kcal/mol)] function in the presence of these rare-coding pathogenic mutations in the MBOAT4 gene. Then, a stereochemical quality check (i.e. validation and assessment) of the 3D model was performed, followed by a blind cavity docking approach, used to search for druggable cavities and molecular interactions with citrus flavonoids of the Rutaceae family, ranked with energetic estimations. Significant interactions with Phloretin 3',5'-Di-C-Glucoside were also observed at R304, W306, N307, A311, L314 and H338 with (iGEMDOCK: −95.82 kcal/mol and AutoDock: −7.80 kcal/mol). The RMSD values and other variables of MD simulation analyses on this protein further validated its significant interactions with the above flavonoids. The MBOAT4 gene and its molecular interactions could serve as an interventional future anti-obesity target. The current study’s findings will benefit future prospects for large population-based studies and drug development, particularly for generating personalized medicine. Communicated by Ramaswamy H. Sarma

In silico screening of non-synonymous SNPs in human TUFT1 gene

BackgroundTuftelin 1 (TUFT1) gene is important in the development and mineralization of dental enamel. The study aimed to identify potential functionally deleterious non-synonymous SNPs (nsSNPs) in the TUFT1 gene by using different in silico tools. The deleterious missense SNPs were identified from SIFT, PolyPhen-2, PROVEAN, SNPs & GO, PANTHER, and SNAP2. The stabilization, conservation, and three-dimensional modeling of mutant proteins were analyzed by I-Mutant 3.0, Consurf, and Project HOPE, respectively. The protein–protein interaction using STRING, GeneMANIA for gene–gene interaction, and DynaMut for evaluating the impact of the mutation on protein stability, conformation, and flexibility. ResultsEight deleterious nsSNPs (E242A, R303W, K182N, K123N, R117W, H289Q, R203W, and Q107R) out of 304 were found to have high-risk damaging effects using six in silico tools. Among them, K182N and K123N alone had increased stability, whereas E242A, R303W, R117W, H289Q, Q107R, and R203W exhibited a decrease in protein stability, based on DDG values. Meanwhile, all the eight deleterious nsSNPs altered the size, charge, hydrophobicity, and spatial organization of the amino acids and predominantly had alpha helix domains. These deleterious variants were located in highly conserved regions except R203W. Protein–protein interaction predicted that TUFT1 interacted with ten proteins that are involved in enamel mineralization and odontogenesis. Gene–gene interaction network showed that TUFT1 is involved in physical interactions, gene co-localization, and pathway interactions. DynaMut ΔΔG values predicted that five nsSNPs were destabilizing the protein, ΔΔG ENCoM values showed a destabilizing effect for all mutants, and seven nsSNPs increased the molecular flexibility of TUFT1. ConclusionOur study predicted eight functional SNPs that had detrimental effects on the structure and function of the TUFT1 gene. This will aid in the development of candidate deleterious markers as a potential target for disease diagnosis and therapeutic interventions.

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.

A computational analysis reveals eight novel high-risk single nucleotide variants of human tumor suppressor LHPP gene

BackgroundLHPP is a tumor suppressor protein associated with various malignancies like liver, oral, pharyngeal, bladder, cervical, and gastric cancers through controlling various pathways. Several single nucleotide variants have been reported to cause cancers. The main objectives of our study were to investigate the impact of the deleterious non-synonymous single nucleotide variants on structure and functions of the LHPP protein.ResultsWe used nine computational tools (SNAP2, PROVEAN, POLYPHEN 2, PREDICT SNP, MAPP, PhD-SNP, SIFT, PANTHER, and PMUT) to find out the deleterious SNPs. These nine computational algorithms predicted 34 nsSNPs to be deleterious as a result of their computational analysis. Using ConSurf, I-Mutant, SDM, MUpro, and Mutpred, we emphasized more how those harmful nsSNPs negatively affect the structure and function of the LHPP protein. Furthermore, we predicted the mutant protein structures and assessed the total energy value deviation in comparison with LHPP original structure and also calculated RMSD values and TM scores. By comparing the result from all these computational approaches, we shortlisted a total eight novel nsSNPs (D214G, D219N, Q224P, L231P, G236W, R234C, R234P, and V233G) that impose high risks to the structure and functions of LHPP protein. To analyze the mutant protein’s behavior in physiological condition, we performed 50 ns molecular dynamic simulation using WebGro online tool and found that the mutants values vary from the wild type in terms of RMSD, RMSF, Rg, SASA, and H-bond numbers. Prognostic significance analysis by Kaplan–Meier plotter showed that abnormal regulation of LHPP can also serve as a prognostic marker for the patient with breast, ovarian, and gastric cancers. Additionally, ligand binding sites analysis revealed the presence of D214G and D219N mutants in the binding site one which means these two nsSNPs can disturb the binding capacity of the LHPP protein. Protein–protein interaction analysis revealed LHPP proteins’ interactions with PPA1, ATP12A, ATP4A, ATP4B, ATP5F1, ATP5J, PPA2, ATP6V0A4, ATP6V0A2, and MT-ATP8 with different degree of connectivity.ConclusionThese results demonstrate a computational understanding of the harmful effect of nsSNPs in LHPP, which may be useful for molecular approaches.

Discovering Deleterious Single Nucleotide Polymorphisms of Human AKT1 Oncogene: An In Silico Study.

AKT1 is a serine/threonine kinase necessary for the mediation of apoptosis, angiogenesis, metabolism, and cell proliferation in both normal and cancerous cells. The mutations in the AKT1 gene have been associated with different types of cancer. Further, the AKT1 gene mutations are also reported to be associated with other diseases such as Proteus syndrome and Cowden syndromes. Hence, this study aims to identify the deleterious AKT1 missense SNPs and predict their effect on the function and structure of the AKT1 protein using various computational tools. Extensive in silico approaches were applied to identify deleterious SNPs of the human AKT1 gene and assessment of their impact on the function and structure of the AKT1 protein. The association of these highly deleterious missense SNPs with different forms of cancers was also analyzed. The in silico approach can help in reducing the cost and time required to identify SNPs associated with diseases. In this study, 12 highly deleterious SNPs were identified which could affect the structure and function of the AKT1 protein. Out of the 12, four SNPs-namely, G157R, G159V, G336D, and H265Y-were predicted to be located at highly conserved residues. G157R could affect the ligand binding to the AKT1 protein. Another highly deleterious SNP, R273Q, was predicted to be associated with liver cancer. This study can be useful for pharmacogenomics, molecular diagnosis of diseases, and developing inhibitors of the AKT1 oncogene.

Identification of deleterious SNPs in bovine HPRT gene by in silico approach

Identification of deleterious SNPs in bovine HPRT gene by in silico approach

In Silico Assessment of Post Translational Modifications Caused by NRAS Gene SNPs in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a blood cancer and a malignant disorder of the bone marrow in which hematopoietic precursors are ceased at an early stage of development, preventing them from differentiating. The NRAS gene plays a vital role in regulating cell division. The mutation in this gene leads to an increased activity of the RAS pathway, increased proliferation and decreased apoptosis rates which causes AML. Objective: To identify the deleterious SNPs involved in AML and to further analyze them using bioinformatics tools. Methods: The missense nsSNPs (Q61H, Q61L, G13V, G13R, and G12A) of NRAS were retrieved from NCBI databases. Results: Using in silico analysis, it was found that these pathogenic SNPs could disrupt the protein stability. These mutations were present in the conserved region and had the potential to significantly alter the protein's secondary structure and impair its functionality. The structural effect of mutations was observed by generating 3D models. Post-translational modifications (PTMs) of proteins refers to the chemical modifications that occur after a protein is formed to make it functionally capable. Analyzing PTMs via in silico analysis revealed that missense mutations affect protein functionality. The level of methylation was significantly high in AML patients. These SNPs might affect additional proteins which are functionally associated. Conclusions: The highlighted SNPs could be suitable targets for future research on proteins, biological markers, and medical diagnosis.

Comprehensive bioinformatics analysis of structural and functional consequences of deleterious missense mutations in the human QDPR gene

Quinonoid dihydropteridine reductase (QDPR) is an enzyme that regulates tetrahydrobiopterin (BH4), a cofactor for enzymes involved in neurotransmitter synthesis and blood pressure regulation. Reduced QDPR activity can cause dihydrobiopterin (BH2) accumulation and BH4 depletion, leading to impaired neurotransmitter synthesis, oxidative stress, and increased risk of Parkinson’s disease. A total of 10,236 SNPs were identified in the QDPR gene, with 217 being missense SNPs. Over 18 different sequence-based and structure-based tools were employed to assess the protein’s biological activity, with several computational tools identifying deleterious SNPs. Additionally, the article provides detailed information about the QDPR gene and protein structure and conservation analysis. The results showed that 10 mutations were harmful and linked to brain and central nervous system disorders, and were predicted to be oncogenic by Dr. Cancer and CScape. Following conservation analysis, the HOPE server was used to analyse the effect of six selected mutations (L14P, V15G, G23S, V54G, M107K, G151S) on the protein structure. Overall, the study provides insights into the biological and functional impact of nsSNPs on QDPR activity and the potential induced pathogenicity and oncogenicity. In the future, research can be conducted to systematically evaluate QDPR gene variation through clinical studies, investigate mutation prevalence across different geographical regions, and validate computational results with conclusive experiments. Communicated by Ramaswamy H. Sarma

In silico identification and characterization of the SNPs in the human ASTL gene and their probable role in female infertility.

Ovastacin (ASTL), a zinc metalloprotease, is released from a fertilized egg during exocytosis of cortical granules which occurs minutes after the sperm and egg fuse. ASTL cleaves ZP2, one of the four primary glycoproteins of human zona pellucida, and this cleavage prevents polyspermy, causes zona pellucida hardening, and also protects the pre-implantation embryo. Any perturbation in the activity of ASTL can thus disturb this process and may lead to infertility without changing the gross morphology of the oocyte. A small amount of ASTL is also released by unfertilized oocytes but its catalytic activity is absent as it is bound by its inhibitor, Fetuin-B (FETUB). Pre-mature release of ASTL when FETUB is absent also causes infertility. To identify and understand the structural and functional effects of deleterious SNPs of ASTL on its interaction with ZP2 and FETUB and hence on fertility, a total of 4,748 SNPs from the dbSNP database were evaluated using a variety of in silico tools. All of the 40 shortlisted nsSNPs were present in the catalytic domain of the protein. Comparison of the wild type with mutants using MutPred2 suggests an alteration in the catalytic activity/zinc binding site in many SNPs. Docking studies show the involvement of hydrophobic interactions and H bonding between ASTL and ZP2 and also between ASTL and FETUB. Four positions in ASTL involved in the hydrophobic interactions (P105 and D200 between ASTL and ZP2; D198 and L278 between ASTL and FETUB) and 5 in H bonding (E75 and R159 between ASTL and ZP2; and K93, R159, and C281 between ASTL and FETUB) have SNP's associated with them validating their importance. Interestingly, a cluster of multiple SNPs was found in the motif 198DRD200, which is also a well-conserved region among several species. Statistical Coupling Analysis (SCA) suggested that the deleterious SNPs were present in the functionally important amino acid positions of ASTL and are evolutionarily coupled. Thus, these results attempt to identify the regions in ASTL, mutations in which can affect its binding with ZP2 or FETUB and cause female infertility.