In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review

Computational Identification and Functional Analysis of Potentially Pathogenic nsSNPs in the NLRP3 Gene Linked to Alzheimer's Disease

Single Nucleotide Polymorphisms (SNPs) are key in understanding complex diseases. Nonsynonymous single-nucleotide polymorphisms (nsSNPs) occur in protein-coding regions, potentially altering amino acid sequences, protein structure and function. Computational methods are vital for distinguishing deleterious nsSNPs from neutral ones. We investigated the role of NLRP3 gene in neuroinflammation associated with Alzheimer's disease (AD) pathogenesis. A total of 893 missense (nsSNPs) were obtained from the dbSNP database and subjected to rigorous filtering using bioinformatics tools like SIFT, Align GVGD, PolyPhen-2, and PANTHER to identify potentially damaging variants. Of these, 18 nsSNPs were consistently predicted to have deleterious effects across all tools. Notably, 16 of these variants exhibited reduced protein stability, while only 4 were predicted to be buried within the protein structure. Among the identified nsSNPs, rs180177442 (R262L and R262P), rs201875324 (T659I), and rs139814109 (T897M) were classified as high-risk variants due to their significant deleterious impact, probable damaging effects, and association with decreased protein stability. Molecular docking and simulation analyses were conducted utilizing Memantine, a standard drug utilized in AD treatment, to investigate potential interactions with the altered protein structures. Additional clinical and genetic investigations are necessary to elucidate the underlying mechanisms that link NLRP3 polymorphisms with the initiation of AD.

DNA Methylation Profiling in Genetically Selected Clarias magur (Hamilton, 1822) Provides Insights into the Epigenetic Regulation of Growth and Development

DNA methylation is an epigenetic alteration that impacts gene expression without changing the DNA sequence affecting an organism's phenotype. This study utilized a reduced representation bisulfite sequencing (RRBS) approach to investigate the patterns of DNA methylation in genetically selected Clarias magur stocks. RRBS generated 249.22 million reads, with an average of 490,120 methylation sites detected in various parts of genes, including exons, introns, and intergenic regions. A total of 896 differentially methylated regions (DMRs) were identified; 356 and 540 were detected as hyper-methylated and hypo-methylated regions, respectively. The DMRs and their association with overlapping genes were explored using whole genome data of magur, which revealed 205 genes in exonic, 210 in intronic, and 480 in intergenic regions. The analysis identified the maximum number of genes enriched in biological processes such as RNA biosynthetic process, response to growth factors, nervous system development, neurogenesis, and anatomical structure morphogenesis. Differentially methylated genes (DMGs) such as myrip, mylk3, mafb, egr3, ndnf, meis2a, foxn3, bmp1a, plxna3, fgf6, sipa1l1, mcu, cnot8, trim55b, and myof were associated with growth and development. The selected DMGs were analyzed using real-time PCR, which showed altered mRNA expression levels. This work offers insights into the epigenetic mechanisms governing growth performance regulation in magur stocks. This work provides a valuable resource of epigenetic data that could be integrated into breeding programs to select high-performing individuals.

Identifying Oncogenic Missense Single Nucleotide Polymorphisms in Human SAT1 Gene Using Computational Algorithms and Molecular Dynamics Tools

BACKGROUND

The human SAT1 gene encodes spermidine/spermine N1-acetyltransferase 1 (SSAT1), a regulatory biological catalyst of polyamine catabolism. Numerous essential biological processes, such as cellular proliferation, differentiation, and survival, depend on polyamines like spermidine and spermine. Thus, SSAT1 is involved in key cellular activities such as proliferation and survival of cells and mediates various diseases including cancer. A plethora of studies established the involvement of missense single nucleotide polymorphisms (SNPs) in numerous pathological conditions due to their ability to adversely affect the structure and subsequent function of the protein.

AIMS

To date, an in silico study to identify the pathogenic missense SNPs of the human SAT1 gene has not been accomplished yet. This study aimed to filter the missense SNPs that were functionally detrimental and pathogenic.

METHODS AND RESULTS

The rs757435207 (I21N) was ascertained to be the most deleterious and pathogenic by all algorithmic tools. Stability and evolutionary conservation analysis tools also stated that I21N variant decreased the stability and was located in the highly conserved residue. Molecular dynamics simulation revealed that I21N caused substantial alterations in the conformational stability and dynamics of the SSAT1 protein. Consequently, the I21N variant could disrupt the native functional roles of the SSAT1 enzyme.

CONCLUSION

Therefore, the I21N variant was identified and concluded to be an oncogenic missense variant of the human SAT1 gene. Overall, the findings of this study would be a great directory of future experimental research to develop personalized medicine.

Understanding Genetic Risks: Computational Exploration of Human β-Synuclein nsSNPs and their Potential Impact on Structural Alteration

Synucleins are pivotal in neurodegenerative conditions. Beta-synuclein (β-synuclein) is part of the synuclein protein family alongside alpha-synuclein (α-synuclein) and gamma-synuclein (γ-synuclein). These proteins, found mainly in brain tissue and cancers, are soluble and unstructured. β-synuclein shares significant similarity with α-synuclein, especially in their N-terminus, with a 90% match. However, their aggregation tendencies differ significantly. While α-synuclein aggregation is believed to be counteracted by β-synuclein, which occurs in conditions like Parkinson's disease, β-synuclein may counteract α-synuclein's toxic effects on the nervous system, offering potential treatment for neurodegenerative diseases. Under normal circumstances, β-synuclein may guard against disease by interacting with α-synuclein. Yet, in pathological environments with heightened levels or toxic substances, it might contribute to disease. Our research aims to explore potential harmful mutations in the β-synuclein using computational tools to predict their destabilizing impact on protein structure. Consensus analysis revealed rs1207608813 (A63P), rs1340051870 (S72F), and rs1581178262 (G36C) as deleterious. These findings highlight the intricate relationship between nsSNPs and protein function, shedding light on their potential implications in disease pathways. Understanding the structural consequences of nsSNPs is crucial for elucidating their role in pathogenesis and developing targeted therapeutic interventions. Our results offer a robust computational framework for identifying neurodegenerative disorder-related mutations from SNP datasets, potentially reducing the costs associated with experimental characterization.

Identification and analysis of oncogenic non-synonymous single nucleotide polymorphisms in the human NRAS gene: An exclusive in silico study

BACKGROUND

N-ras protein is encoded by the NRAS gene and operates as GDP-GTP-controlled on/off switching. N-ras interacts with cellular signaling networks that regulate various cellular activities including cell proliferation and survival. The nonsynonymous single nucleotide polymorphism (nsSNPs)-mediated alteration can substantially disrupt the structure and activity of the corresponding protein. N-ras has been reported to be associated with numerous diseases including cancers due to the nsSNPs. A comprehensive study on the NRAS gene to unveil the potentially damaging and oncogenic nsSNPs is yet to be accomplished. Hence, this extensive in silico study is intended to identify the disease-associated, specifically oncogenic nsSNPs of the NRAS gene.

RESULTS

Out of 140 missense variants, 7 nsSNPs (I55R, G60E, G60R, Y64D, L79F, D119G, and V152F) were identified to be damaging utilizing 10 computational tools that works based on different algorithms with high accuracy. Among those, G60E, G60R, and D119G variants were further filtered considering their location in the highly conserved region and later identified as oncogenic variants. Interestingly, G60E and G60R variants were revealed to be particularly associated with lung adenocarcinoma, rhabdomyosarcoma, and prostate adenocarcinoma. Therefore, D119G could be subjected to detailed investigation for identifying its association with specific cancer.

CONCLUSION

This in silico study identified the deleterious and oncogenic missense variants of the human NRAS gene that could be utilized for designing further experimental investigation. The outcomes of this study would be worthwhile in future research for developing personalized medicine.

Identification of the SIRT1 gene's most harmful non-synonymous SNPs and their effects on functional and structural features-an in silico analysis

Introduction

The sirtuin (Silent mating type information regulation 2 homolog)1(SIRT1) protein plays a vital role in many disorders such as diabetes, cancer, obesity, inflammation, and neurodegenerative and cardiovascular diseases. The objective of this in silico analysis of SIRT1's functional single nucleotide polymorphisms (SNPs) was to gain valuable insight into the harmful effects of non-synonymous SNPs (nsSNPs) on the protein. The objective of the study was to use bioinformatics methods to investigate the genetic variations and modifications that may have an impact on the SIRT1 gene's expression and function.

Methods

nsSNPs of SIRT1 protein were collected from the dbSNP site, from its three (3) different protein accession IDs. These were then fed to various bioinformatic tools such as SIFT, Provean, and I- Mutant to find the most deleterious ones. Functional and structural effects were examined using the HOPE server and I-Tasser. Gene interactions were predicted by STRING software. The SIFT, Provean, and I-Mutant tools detected the most deleterious three nsSNPs (rs769519031, rs778184510, and rs199983221).

Results

Out of 252 nsSNPs, SIFT analysis showed that 94 were deleterious, Provean listed 67 dangerous, and I-Mutant found 58 nsSNPs resulting in lowered stability of proteins. HOPE modelling of rs199983221 and rs769519031 suggested reduced hydrophobicity due to Ile 4Thr and Ile223Ser resulting in decreased hydrophobic interactions. In contrast, on modelling rs778184510, the mutant protein had a higher hydrophobicity than the wild type.

Conclusions

Our study reports that three nsSNPs (D357A, I223S, I4T) are the most damaging mutations of the SIRT1 gene. Mutations may result in altered protein structure and functions. Such altered protein may be the basis for various disorders. Our findings may be a crucial guide in establishing the pathogenesis of various disorders.

Decreased echinocandin susceptibility in Candida parapsilosis causing candidemia and emergence of a pan-echinocandin resistant case in China

Candida parapsilosis is becoming a predominant non-albicans cause of invasive candidiasis (IC). Echinocandins are the preferred choice for IC treatment and prophylaxis. Resistance to echinocandins in C. parapsilosis has emerged in several countries, but little is known about the susceptibility profile in China or about mechanisms of resistance. Here, we investigated the echinocandin susceptibilities of 2523 C. parapsilosis isolates collected from China and further explored the resistance mechanism among echinocandin-resistant isolates. Anidulafungin exhibited the highest MICs (MIC_50/90, 1 and 2 µg/mL; GM, 0.948 µg/mL), while caspofungin showed better activity (0.5 and 1 µg/mL; 0.498 µg/mL). Significantly higher echinocandin MICs were observed among blood-derived isolates compared to others, especially for caspofungin (GM, 1.348 µg/mL vs 0.478 µg/mL). Isolates from ICU and surgical wards also showed higher MICs. Twenty isolates showed intermediate phenotypes for at least one echinocandin. One was resistant to all three echinocandins, fluconazole and voriconazole, which caused breakthrough IC during long-term exposure to micafungin. WGS revealed this isolate carried a mutation S656P in hotspot1 region of Fks1. Bioinformatics analyses suggested that this mutation might lead to an altered protein conformation. CRISPR Cas9-mediated introduction of this mutation into a susceptible reference C. parapsilosis strain increased MICs of all echinocandins 64-fold, with similar results found in the subspecies, C. orthopsilosis and C. metapsilosis. This is the first report of a multi-azole resistant and pan-echinocandin resistant C. parapsilosis isolate, and the identification of a FKS1^S656P conferring pan-echinocandin resistance. Our study underscores the necessity of rigorous management of antifungal use and of monitoring for antifungal susceptibility.

Computational analysis of the functional and structural impact of the most deleterious missense mutations in the human Protein C

Protein C (PC) is a vitamin K-dependent factor that plays a crucial role in controlling anticoagulant processes and acts as a cytoprotective agent to promote cell survival. Several mutations in human PC are associated with decreased protein production or altered protein structure, resulting in PC deficiency. In this study, we conducted a comprehensive analysis of nonsynonymous single nucleotide polymorphisms in human PC to prioritize and confirm the most high-risk mutations predicted to cause disease. Of the 340 missense mutations obtained from the NCBI database, only 26 were classified as high-risk mutations using various bioinformatic tools. Among these, we identified that 12 mutations reduced the stability of protein, and thereby had the greatest potential to disturb protein structure and function. Molecular dynamics simulations revealed moderate alterations in the structural stability, flexibility, and secondary structural organization of the serine protease domain of human PC for five missense mutations (L305R, W342C, G403R, V420E, and W444C) when compared to the native structure that could maybe influence its interaction with other molecules. Protein-protein interaction analyses demonstrated that the occurrence of these five mutations can affect the regular interaction between PC and activated factor V. Therefore, our findings assume that these mutants can be used in the identification and development of therapeutics for diseases associated with PC dysfunction, although assessment the effect of these mutations need to be proofed in in-vitro.

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.

Investigating pathogenic SNP of PKCι in HCV-induced hepatocellular carcinoma

Hepatocellular carcinoma is a leading cause of cancer-related deaths due to its complexity in diagnosis, chemo-resistance, and aggressive nature. Identifying pathogenic single nucleotide polymorphism (SNP) in protein kinase C iota (PKCι) can be a potential biomarker in the prognosis and treatment of HCC. This study investigated the association between a SNP in PRKCI and the Pakistani population's hepatocellular carcinoma (HCC) risk. Obtained samples were first evaluated for ALT measurements and viral load quantification through reverse transcriptase-PCR. The PKCι nsSNP rs1199520604 was evaluated computationally by multiple consensus bioinformatics tools for predicting its potential deleterious effects. Its association with hepatitis C virus- (HCV) mediated HCC was then investigated through ARMS-PCR (Amplification Refractory Mutation System Polymerase Chain Reaction). SNP analysis of rs1199520604 was performed in 100 cases and 100 controls. Variant rs1199520604's homozygous T genotype is a risk factor allele for the HCV-induced HCC (odds ratio: 4.13, relative risk: 2.01, P-value < 0.0001). The heterozygous genotype is determined to protect HCV patients from HCC development (P < 0.001). The study highlighted the disease association of variant rs1199520604 with HCV-induced HCC in the Pakistani populations. This variant, after further validation through high-throughput investigation on a larger cohort, has the potential to be translated at the clinical level.

Pharmacogenomics: Driving Personalized Medicine

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

DARVIC: Dihedral angle-reliant variant impact classifier for functional prediction of missense VUS

BACKGROUND

Of the large number of genetic variants identified, the functional impact for most of them remains unknown. Mutations in DNA damage repair genes such as MUTYH, which is involved in repairing A:8-oxoG mismatches caused by reactive oxygen species, can cause a higher risk of cancer. Mutations happening in other key genes such as TP53 also pose huge health threats and risk of cancer. The interpretation of genetic variants' functional impact is a forefront issue that needs to be addressed. Many different in silico methods based on different principles have been developed and applied in interpreting genetic variants. However, a current challenge is that many existing methods tend to overpredict the pathogenicity of benign variants. A new approach is needed to tackle this issue to improve genetic variant interpretation through the use of in silico methods.

METHODS

In this study, we developed another protein structural-based approach called Dihedral angle-reliant variant impact classifier (DARVIC) to predict the deleterious impact of the coding-changing missense variants. DARVIC uses Ramachandran's principle of protein stereochemistry as the theoretical foundation and uses molecular dynamics simulations coupled with a supervised machine learning algorithm XGBoost to determine the functional impact of missense variants on protein structural stability.

RESULTS

We characterized the features of dihedral angles in dynamic protein structures. We also tested the performance of DARVIC in MUTYH and TP53 missense variants and achieved satisfactory results in reflecting the functional impacts of the variants on protein structure. The method achieved a balanced accuracy of 84% in a functionally validated MUTYH dataset containing both benign and pathogenic missense variants, higher than other existing in silico methods. Along with that, DARVIC was able to predict 119 (47%) deleterious variants from a dataset of 254 MUTYH VUS. Further application of DARVIC to a functionally validated TP53 dataset had a balanced accuracy of 94%, topping other methods, demonstrating DARVIC's robustness.

CONCLUSION

DARVIC provides a valuable tool to predict the functional impacts of missense variants based on their effects on protein structural stability and motion. At its current state, DARVIC performed well in predicting the functional impact of the missense variants both in MUTYH and TP53. We expect its high potential to predict functional impact for the missense variants in other genes.

Prioritization of genes associated with type 2 diabetes mellitus for functional studies

Existing therapies for type 2 diabetes mellitus (T2DM) show limited efficacy or have adverse effects. Numerous genetic variants associated with T2DM have been identified, but progress in translating these findings into potential drug targets has been limited. Here, we describe the tools and platforms available to identify effector genes from T2DM-associated coding and non-coding variants and prioritize them for functional studies. We discuss QSER1 and SLC12A8 as examples of genes that have been identified as possible T2DM candidate genes using these tools and platforms. We suggest further approaches, including the use of sequencing data with increased sample size and ethnic diversity, single-cell omics data for analyses, glycaemic trait associations to predict gene function and, potentially, human induced pluripotent stem cell 'village' cultures, to strengthen current gene functionalization workflows. Effective prioritization of T2DM-associated genes for experimental validation could expedite our understanding of the genetic mechanisms responsible for T2DM to facilitate the use of precision medicine in its treatment.

Effects of the pathological E200K mutation on human prion protein: A computational screening and molecular dynamics approach

The human prion protein gene (PRNP) is mapped to the short arm of chromosome 20 (20pter-12). Prion disease is associated with mutations in the prion protein-encoding gene sequence. Earlier studies found that the mutation G127V in the PRNP increases protein stability. In contrast, the mutation E200K, which has the highest mutation rate in the prion protein, causes Creutzfeldt-Jakob disease (CJD) in humans and induces protein aggregation. We aimed to identify the structural mechanisms of E200k and G127V mutations causing CJD. We used a variety of bioinformatic algorithms, including SIFT, PolyPhen, I-Mutant, PhD-SNP, and SNP& GO, to predict the association of the E200K mutation with prion disease. MD simulation is performed, and graphs for root mean square deviation, root mean square fluctuation, radius of gyration, DSSP, principal component analysis, porcupine, and free energy landscape are generated to confirm and prove the stability of the wild-type and mutant protein structures. The protein is analyzed for aggregation, and the results indicate more fluctuations in the protein structure during the simulation owing to the E200K mutation; however, the G127V mutation makes the protein structure stable against aggregation during the simulation.

Identification of potential therapeutic intervening targets by in-silico analysis of nsSNPs in preterm birth-related genes

Prematurity is the foremost cause of death in children under 5 years of age. Genetics contributes to 25-40% of all preterm births (PTB) yet we still need to identify specific targets for intervention based on genetic pathways. This study involved the effect of region-specific non-synonymous variations and their transcript level mutational impact on protein functioning and stability by various in-silico tools. This investigation identifies potential therapeutic targets to manage the challenge of PTB, corresponding protein cavities and explores their binding interactions with intervening compounds. We searched 20 genes coding 55 PTB proteins from NCBI. Single Nucleotide Polymorphisms (SNPs) of concerned genes were extracted from ENSEMBL, and filtration of exonic variants (non-synonymous) was performed. Several in-silico downstream protein functional effect prediction tools were used to identify damaging variants. Rare coding variants were selected with an allele frequency of ≤1% in 1KGD, further supported by South Asian ALFA frequencies and GTEx gene/tissue expression database. CNN1, COL24A1, IQGAP2 and SLIT2 were identified with 7 rare pathogenic variants found in 17 transcript sequences. The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability (ΔΔG (kcal/mol). After structural protein identification, homology modelling of CNN1, which has been previously reported as a biomarker for the prediction of PTB, was performed, followed by the stereochemical quality checks of the 3D model. Blind docking approach were used to search the binding cavities and molecular interactions with progesterone, ranked with energetic estimations. Molecular interactions of CNN1 with progesterone were investigated through LigPlot 2D. Further, molecular docking experimentation of CNN1 showed the significant interactions at S102, L105, A106, K123, Y124 with five selected PTB-drugs, Allylestrenol (-7.56 kcal/mol), Hydroxyprogesterone caproate (-8.19 kcal/mol), Retosiban (-9.43 kcal/mol), Ritodrine (-7.39 kcal/mol) and Terbutaline (-6.87 kcal/mol). Calponin-1 gene and its molecular interaction analysis could serve as an intervention target for the prevention of PTB.

Predicting the effects of rare genetic variants on oncogenic signaling pathways: A computational analysis of HRAS protein function

The HRAS gene plays a crucial role in regulating essential cellular processes for life, and this gene's misregulation is linked to the development of various types of cancers. Nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of HRAS can cause detrimental mutations that disrupt wild-type protein function. In the current investigation, we have employed in-silico methodologies to anticipate the consequences of infrequent genetic variations on the functional properties of the HRAS protein. We have discovered a total of 50 nsSNPs, of which 23 were located in the exon region of the HRAS gene and denoting that they were expected to cause harm or be deleterious. Out of these 23, 10 nsSNPs ([G60V], [G60D], [R123P], [D38H], [I46T], [G115R], [R123G], [P11OL], [A59L], and [G13R]) were identified as having the most delterious effect based on results of SIFT analysis and PolyPhen2 scores ranging from 0.53 to 69. The DDG values -3.21 kcal/mol to 0.87 kcal/mol represent the free energy change associated with protein stability upon mutation. Interestingly, we identified that the three mutations (Y4C, T58I, and Y12E) were found to improve the structural stability of the protein. We performed molecular dynamics (MD) simulations to investigate the structural and dynamic effects of HRAS mutations. Our results showed that the stable model of HRAS had a significantly lower energy value of -18756 kj/mol compared to the initial model of -108915 kj/mol. The RMSD value for the wild-type complex was 4.40 Å, and the binding energies for the G60V, G60D, and D38H mutants were -107.09 kcal/mol, -109.42 kcal/mol, and -107.18 kcal/mol, respectively as compared to wild-type HRAS protein had -105.85 kcal/mol. The result of our investigation presents convincing corroboration for the potential functional significance of nsSNPs in augmenting HRAS expression and adding to the activation of malignant oncogenic signalling pathways.

Computational approaches for predicting variant impact: An overview from resources, principles to applications

One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Computational algorithmic and molecular dynamics study of functional and structural impacts of non-synonymous single nucleotide polymorphisms in human DHFR gene

Human dihydrofolate reductase (DHFR) is a conserved enzyme that is central to folate metabolism and is widely targeted in pathogenic diseases as well as cancers. Although studies have reported the fact that genetic mutations in DHFR leads to a rare autosomal recessive inborn error of folate metabolism and drug resistance, there is a lack of an extensive study on how the deleterious non-synonymous SNPs (nsSNPs) disrupt its phenotypic effects. In this study, we aim at discovering the structural and functional consequences of nsSNPs in DHFR by employing a combined computational approach consisting of ten recently developed in silico tools for identification of damaging nsSNPs and molecular dynamics (MD) simulation for getting deeper insights into the magnitudes of damaging effects. Our study revealed the presence of 12 most deleterious nsSNPs affecting the native phenotypic effects, with three (R71T, G118D, Y122D) identified in the co-factor and ligand binding active sites. MD simulations also suggested that these three SNPs particularly Y122D, alter the overall structural flexibility and dynamics of the native DHFR protein which can provide more understandings into the crucial roles of these mutants in influencing the loss of DHFR function.

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

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