SNAP: predict effect of non-synonymous polymorphisms on function

Prediction of deleterious non-synonymous SNPs of human MDC1 gene: an in silico approach

MDC1 (Mediator of DNA damage Checkpoint protein 1) functions to facilitate the localization of numerous DNA damage response (DDR) components to DNA double-strand break sites. MDC1 is an integral component in preserving genomic stability and appropriate DDR regulation. There haven't been systematic investigations of MDC1 mutations that induce cancer and genomic instability. Variations in nsSNPs have the potential to modify the protein chemistry and their function. Describing functional SNPs in disease-associated genes presents a significant conundrum for investigators, it is possible to assess potential functional SNPs before conducting larger population examinations. Multiple sequences and structure-based bioinformatics strategies were implemented in the current in-silico investigation to discern potential nsSNPs of the MDC1 genes. The nsSNPs were identified with SIFT, SNAP2, Align GVGD, PolyPhen-2, and PANTHER, and their stability was determined with MUpro. The conservation, solvent accessibility, and structural effects of the mutations were identified with ConSurf, NetSurfP-2.0, and SAAFEC-SEQ respectively. Cancer-related analysis of the nsSNPs was conducted using cBioPortal and TCGA web servers. The present study appraised five nsSNPs (P1426T, P69S, P194R, P203L, and H131Y) as probably mutilating due to their existence in highly conserved regions and propensity to deplete protein stability. The nsSNPs P194R, P203L, and H131Y were concluded as deleterious and possibly damaging from the 5 prediction tools. The functional nsSNP P194R mutation is associated with skin cutaneous melanoma while no significant records were found for other nsSNPs. The present study concludes that the highly deleterious P194R mutations can potentially induce genomic instability and contribute to various cancers' pathogenesis. Developing drugs targeting these mutations can undoubtedly be advantageous in large population-based studies, particularly in the development of precision medicine.

Structural impact, ligand-protein interactions, and molecular phenotypic effects of TGF-β1 gene variants: In silico analysis with implications for idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic Pulmonary Fibrosis (IPF) is a chronic interstitial lung disease resulting in progressively deteriorating lung function. Transforming growth factor-β1 (TGF-β1) belongs to the TGF superfamily and exerts a profibrotic role in promoting lung fibrosis by facilitating fibroblast infiltration and activity, extracellular matrix deposition, and inhibition of collagen breakdown, thus promoting tissue remodelling and IPF.

MATERIALS AND METHODS

We evaluated the link between pathogenic TGF-β1 SNPs and IPF pathogenesis and the structure-activity functional consequences of those SNPs on the TGF-β1 protein. Several computational algorithms were merged to address the functional consequences of TGF-β1 gene mutations to protein stability, putative post-translational modification sites, ligand-protein interactions, and molecular phenotypic effects. These included FATHMM, POLYPHEN2, PROVEAN, and SIFT tools (identifying deleterious nsSNPs in the TGF-β1 gene), along with Pmut, PhD-SNP, SNAP, MutPred and the related TMHMM, MARCOIL, and DisProt algorithms (predicting structural disorders). INPS-MD was also used to evaluate the mutation-induced TGF-β1 protein's stability and MODPRED for recognition of post-translational TGF-β1 modification.

RESULTS

In total, 14 major pathogenic variants markedly impact the destabilization of the TGF-β1 protein, with most of these high-risk mutations associated with decreased stability of the TGF-β1 protein as per the I-Mutant, MUpro, and INPS-MD tools. R205W, R185W, R180Q, D86Y, and I300T variants were proposed to participate in the post-translational modifications, thus affecting affect protein-ligand interactions. Furthermore, at-risk genetic variants appear to target conserved regions in the alpha helices, random coils, and extracellular loops, resulting in a varied composition of amino acids, charge, hydrophobicity, and spatial architecture.

CONCLUSIONS

This study manuscript comprehensively analyzes gene variants within the TGF-β1 gene, offering novel insights into their structural and functional implications in interacting with target sites. This study is significant for the development of targeted therapeutic strategies and personalized treatment approaches for patients with inflammatory lung diseases such as IPF.

In silico analysis of DEL-1 and inflammation-related genes in lung squamous cell carcinoma

AIM

Twenty to thirty percent of non-small cell lung cancers (NSCLC) are caused by lung squamous cell carcinoma (LUSC), especially in smokers and there has been limited study previously evaluating the situation in terms of the genome and gene expression profile, which demonstrates the relationship among DEL-1, leucocyte recruitment, and pro-inflammatory cytokines in LUSC.

MATERIAL AND METHODS

In the current study, the m-RNA expression patterns and mutation profiles of our target genes, such as, pro-inflammatory cytokines, chemoattractant molecules, and DEL-1 genes, in 511 LUSC patients. To find the harmful mutations, the PolyPhen-2 and SNAP programs were employed. Not only gene expression was detected, but also survival analysis and correlation between DEL-1 and other target genes' expression levels were explored too.

RESULTS

Target genes such as, DEL-1, TNF, IL-18, IL-1, CXCL8, CXCL13, and IL-6 were found to have a total genetic anomaly carrying rate of 16.4%. Seven mutations were found, and two of those mutations have a pathogenic aspect. Deep deletion and gene amplification of the genetic anomalies were also observed. According to gene expression analysis results in the LUSC patient group; DEL-1 and IL-6 levels were significantly lower than those of the control group, whereas the CXCL13 level was found to be higher.

CONCLUSION

Findings of the current study revealed that, there is a significant role of DEL-1 in LUSC pathogenesis. Since present study is an in silico-centered study, this approach can give more insight on experimental studies. These events may support that one of the cancer improvement mechanisms depending on DEL-1 gene at the molecular level.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

G protein-coupled receptor (GPCR) pharmacogenomics

The field of pharmacogenetics, the investigation of the influence of one or more sequence variants on drug response phenotypes, is a special case of pharmacogenomics, a discipline that takes a genome-wide approach. Massively parallel, next generation sequencing (NGS), has allowed pharmacogenetics to be subsumed by pharmacogenomics with respect to the identification of variants associated with responders and non-responders, optimal drug response, and adverse drug reactions. A plethora of rare and common naturally-occurring GPCR variants must be considered in the context of signals from across the genome. Many fundamentals of pharmacogenetics were established for G protein-coupled receptor (GPCR) genes because they are primary targets for a large number of therapeutic drugs. Functional studies, demonstrating likely-pathogenic and pathogenic GPCR variants, have been integral to establishing models used for in silico analysis. Variants in GPCR genes include both coding and non-coding single nucleotide variants and insertion or deletions (indels) that affect cell surface expression (trafficking, dimerization, and desensitization/downregulation), ligand binding and G protein coupling, and variants that result in alternate splicing encoding isoforms/variable expression. As the breadth of data on the GPCR genome increases, we may expect an increase in the use of drug labels that note variants that significantly impact the clinical use of GPCR-targeting agents. We discuss the implications of GPCR pharmacogenomic data derived from the genomes available from individuals who have been well-phenotyped for receptor structure and function and receptor-ligand interactions, and the potential benefits to patients of optimized drug selection. Examples discussed include the renin-angiotensin system in SARS-CoV-2 (COVID-19) infection, the probable role of chemokine receptors in the cytokine storm, and potential protease activating receptor (PAR) interventions. Resources dedicated to GPCRs, including publicly available computational tools, are also discussed.

In Silico Functional and Structural Analysis of Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in Human Paired Box 4 Gene

In human genome, members of Paired box (PAX) transcription factor family are highly sequence-specific DNA-binding proteins. Among PAX gene family members, PAX4 gene has significant role in growth, proliferation, differentiation, and insulin secretion of pancreatic β-cells. Single nucleotide polymorphisms (SNPs) in PAX4 gene progress in the pathogenesis of various human diseases. Hence, the molecular mechanism of how these SNPs in PAX4 gene significantly progress diseases pathogenesis needs to be elucidated. For the reason, a series of bioinformatic analyzes were done to identify the SNPs of PAX4 gene that contribute in diseases pathogenesis. From the analyzes, 4145 SNPs (rsIDs) in PAX4 gene were obtained, where, 362 missense (8.73%), 169 synonymous (4.08%), and 2323 intron variants (56.04%). The rest SNPs were unspecified. Among the 362 missense variants, 118 nsSNPs were found as deleterious in SIFT analysis. Among those, 25 nsSNPs were most probably damaging and 23 were deleterious as observed in PolyPhen-2 and PROVEAN analyzes, respectively. Following all analyzes, 14 nsSNPs (rs149708455, rs115887120, rs147279315, rs35155575, rs370095957, rs373939873, rs145468905, rs121917718, rs2233580, rs3824004, rs372751660, rs369459316, rs375472849, rs372497946) were common and observed as deleterious, probably damaging, affective and diseases associated. Following structural analyzes, 11 nsSNPs guided proteins were found as most unstable and highly conserved. Among these, R20W, R39Q, R45Q, R60H, G65D, and A223D mutated proteins were highly harmful. Hence, the results from above-mentioned integrated comprehensive bioinformatic analyzes guide how different nsSNPs in PAX4 gene alter structural and functional characteristics of the protein that might progress diseases pathogenesis in human including type 2 diabetes.

Dark Morph of the Oriental Honey-Buzzard (Pernis ptilorhynchus orientalis) is Attributable to Specific MC1R Haplotypes

A thorough understanding of the development of complex plumages in birds necessitates the acquisition of genetic data pertaining to the mechanism underlying this phenomenon from various avian species. The oriental honey-buzzard (Pernis ptilorhynchus orientalis), a tropical summer migrant to Northeast Asia, including Japan, exemplifies this aspect owing to the diversity of its ventral coloration and intra-feather barring patterns. However, genetic polymorphism responsible for this diversity has not been identified yet. This study aimed to investigate the link between dark-plumed phenotypes of this subspecies and haplotypes of the melanocortin-1-receptor (MC1R) gene. A draft sequence of MC1R was constructed using next generation sequencing and subsequently amplified using designed polymerase chain reaction (PCR) primers. The genome sequences of 32 honey-buzzard individuals were determined using PCR, and 12 MC1R haplotype sequences were obtained. Among these haplotypes, we found that unique haplotypes with nine non-synonymous substitutions and four or five synonymous substitutions in the coding region had a perfect correlation with the dark-plumed phenotype. The lack of correlation between the genotype of ASIP coding region and plumage phenotype reiterated that the dark morph is attributable to specific MC1R haplotypes. The absence of a correlation between genetic polymorphisms of MC1R and the intra-feather barring patterns, as well as the diversity observed within lighter ground color classes (pale and intermediate), implies the involvement of alternative molecular mechanisms in the manifestation of the aforementioned phenotypes.

Low Prevalence of Nirmatrelvir-Ritonavir Resistance-Associated Mutations in SARS-CoV-2 Lineages From Botswana

Background

We evaluated naturally occurring nirmatrelvir-ritonavir (NTV/r) resistance-associated mutations (RAMs) among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains from Botswana, a country with no NTV/r use to date, in order to recommend the usage of the agent for high-risk patients with coronavirus disease 2019 (COVID-19).

Methods

We conducted a retrospective analysis using 5254 complete SARS-CoV-2 sequences from Botswana (September 2020-September 2023). We evaluated the mutational landscape of SARS-CoV-2 3-Chymotrypsin-like protease (3CLpro) relative to the highlighted list of RAMs granted Food and Drug Administration Emergency Use Authorization in 2023.

Results

The sequenced 5254 samples included Beta variants of concerns (VOCs; n = 323), Delta VOCs (n = 1314), and Omicron VOCs (n = 3354). Overall, 77.8% of the sequences exhibited at least 1 polymorphism within 76/306 amino acid positions in the nsp5 gene. NTV/rRAMs were identified in 34/5254 (0.65%; 95% CI, 0.43%-0.87%) and occurred at 5 distinct positions. Among the NTV/r RAMS detected, A191V was the most prevalent (24/34; 70.6%). Notably, T21I mutation had a prevalence of 20.6% (7/34) and coexisted with either K90R (n = 3) polymorphism in Beta sequences with RAMs or P132H (n = 3) polymorphism for Omicron sequences with RAMs. Other NTV/r RAMs detected included P108S, with a prevalence of 5.88% (2/34), and L50F, with a prevalence of 2.94% (1/34). NTV/r RAMs were significantly higher (P < .001) in Delta (24/35) compared with Beta (4/34) and Omicron (6/34) sequences.

Conclusions

The frequency of NTV/r RAMs in Botswana was low. Higher rates were observed in Delta VOCs compared to Omicron and Beta VOCs. As NTV/r use expands globally, continuous surveillance for drug-resistant variants is essential, given the RAMs identified in our study.

Variant Effect Prediction in the Age of Machine Learning

Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Can machines learn the language of life from the unannotated protein sequence data well enough to identify significant errors in the protein "sentences"? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted. We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise.

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.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Polymorphism in the leucine-rich repeats of TLR7 in different breeds of chicken and in silico analysis of its effect on TLR7 structure and function

Chicken toll-like receptor 7 (chTLR7) is a viral sensing pattern recognition receptor and detects ssRNA. The ligand binding site comprises leucine-rich repeats (LRRs) located in the ectodomain of chTLR7. Hence, any polymorphism in the binding site would modify its functional interaction with the ligand, resulting in varied strength of immune response. This study first aimed to compare the single nucleotide polymorphisms (SNPs) associated with the ligand binding site of TLR7 in three indigenous chicken breeds namely Aseel, Kadaknath, Nicobari along with an exotic breed White Leghorn. Four synonymous SNPs (P123P, I171I, N339N and L421L) and four non-synonymous SNPs (I121V, S135T, F356S and S447G) were identified among various breeds. We employed in silico tools to screen the pathogenic nsSNPs and one nsSNP was identified as having potential impact on chTLR7 protein. Moreover, sequence and structure-based methods were used to determine the effect of nsSNPs on protein stability. It revealed I121V, F356S, and S447G as decreasing the stability while S135T increasing the stability of chTLR7. Additionally, docking analysis confirmed that I121V and F356S reduced the binding affinity of ligands (R-848 and polyU) to chTLR7 protein. The results suggest that the nsSNPs found in this study could alter the ligand binding of chTLR7 and modify the immune response between different breeds further contributing to disease susceptibility or resistance. Further, in vitro and in vivo studies are needed to analyze the effect of these SNPs on susceptibility or resistance against various viral diseases in poultry.

De novo structure prediction of meteorin and meteorin-like protein for identification of domains, functional receptor binding regions, and their high-risk missense variants

Meteorin (Metrn) and Meteorin-like (Metrnl) are homologous secreted proteins involved in neural development and metabolic regulation. In this study, we have performed de novo structure prediction and analysis of both Metrn and Metrnl using Alphafold2 (AF2) and RoseTTAfold (RF). Based on the domain and structural homology analysis of the predicted structures, we have identified that these proteins are composed of two functional domains, a CUB domain and an NTR domain, connected by a hinge/loop region. We have identified the receptor binding regions of Metrn and Metrnl using the machine-learning tools ScanNet and Masif. These were further validated by docking Metrnl with its reported KIT receptor, thus establishing the role of each domain in the receptor interaction. Also, we have studied the effect of non-synonymous SNPs on the structure and function of these proteins using an array of bioinformatics tools and selected 16 missense variants in Metrn and 10 in Metrnl that can affect the protein stability. This is the first study to comprehensively characterize the functional domains of Metrn and Metrnl at their structural level and identify the functional domains, and protein binding regions. This study also highlights the interaction mechanism of the KIT receptor and Metrnl. The predicted deleterious SNPs will allow further understanding of the role of these variants in modulating the plasma levels of these proteins in disease conditions such as diabetes.Communicated by Ramaswamy H. Sarma.

An In Silico Analysis of Genetic Variants and Structural Modeling of the Human Frataxin Protein in Friedreich's Ataxia

Friedreich's Ataxia (FRDA) stands out as the most prevalent form of hereditary ataxias, marked by progressive movement ataxia, loss of vibratory sensitivity, and skeletal deformities, severely affecting daily functioning. To date, the only medication available for treating FRDA is Omaveloxolone (Skyclarys®), recently approved by the FDA. Missense mutations within the human frataxin (FXN) gene, responsible for intracellular iron homeostasis regulation, are linked to FRDA development. These mutations induce FXN dysfunction, fostering mitochondrial iron accumulation and heightened oxidative stress, ultimately triggering neuronal cell death pathways. This study amalgamated 226 FXN genetic variants from the literature and database searches, with only 18 previously characterized. Predictive analyses revealed a notable prevalence of detrimental and destabilizing predictions for FXN mutations, predominantly impacting conserved residues crucial for protein function. Additionally, an accurate, comprehensive three-dimensional model of human FXN was constructed, serving as the basis for generating genetic variants I154F and W155R. These variants, selected for their severe clinical implications, underwent molecular dynamics (MD) simulations, unveiling flexibility and essential dynamic alterations in their N-terminal segments, encompassing FXN42, FXN56, and FXN78 domains pivotal for protein maturation. Thus, our findings indicate potential interaction profile disturbances in the FXN42, FXN56, and FXN78 domains induced by I154F and W155R mutations, aligning with the existing literature.

In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene

Single nucleotide polymorphisms (SNPs) are one of the most common determinants and potential biomarkers of human disease pathogenesis. SNPs could alter amino acid residues, leading to the loss of structural and functional integrity of the encoded protein. In humans, members of the minichromosome maintenance (MCM) family play a vital role in cell proliferation and have a significant impact on tumorigenesis. Among the MCM members, the molecular mechanism of how missense SNPs of minichromosome maintenance complex component 6 (MCM6) contribute to DNA replication and tumor pathogenesis is underexplored and needs to be elucidated. Hence, a series of sequence and structure-based computational tools were utilized to determine how mutations affect the corresponding MCM6 protein. From the dbSNP database, among 15,009 SNPs in the MCM6 gene, 642 missense SNPs (4.28%), 291 synonymous SNPs (1.94%), and 12,500 intron SNPs (83.28%) were observed. Out of the 642 missense SNPs, 33 were found to be deleterious during the SIFT analysis. Among these, 11 missense SNPs (I123S, R207C, R222C, L449F, V456M, D463G, H556Y, R602H, R633W, R658C, and P815T) were found as deleterious, probably damaging, affective and disease-associated. Then, I123S, R207C, R222C, V456M, D463G, R602H, R633W, and R658C missense SNPs were found to be highly harmful. Six missense SNPs (I123S, R207C, V456M, D463G, R602H, and R633W) had the potential to destabilize the corresponding protein as predicted by DynaMut2. Interestingly, five high-risk mutations (I123S, V456M, D463G, R602H, and R633W) were distributed in two domains (PF00493 and PF14551). During molecular dynamics simulations analysis, consistent fluctuation in RMSD and RMSF values, high Rg and hydrogen bonds in mutant proteins compared to wild-type revealed that these mutations might alter the protein structure and stability of the corresponding protein. Hence, the results from the analyses guide the exploration of the mechanism by which these missense SNPs of the MCM6 gene alter the structural integrity and functional properties of the protein, which could guide the identification of ways to minimize the harmful effects of these mutations in humans.

SARS-CoV-2-induced phosphorylation and its pharmacotherapy backed by artificial intelligence and machine learning

Aims: To investigate the role of phosphorylation in SARS-CoV-2 infection, potential therapeutic targets and its harmful genetic sequences. Materials & Methods: Data mining techniques were employed to identify upregulated kinases responsible for proteomic changes induced by SARS-CoV-2. Spike and nucleocapsid proteins' sequences were analyzed using predictive tools, including SNAP2, MutPred2, PhD-SNP, SNPs&Go, MetaSNP, Predict-SNP and PolyPhen-2. Missense variants were identified using ensemble-based algorithms and homology/structure-based models like SIFT, PROVEAN, Predict-SNP and MutPred-2. Results: Eight missense variants were identified in viral sequences. Four damaging variants were found, with SNPs&Go and PolyPhen-2. Promising therapeutic candidates, including gilteritinib, pictilisib, sorafenib, RO5126766 and omipalisib, were identified. Conclusion: This research offers insights into SARS-CoV-2 pathogenicity, highlighting potential treatments and harmful variants in viral proteins.

Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning

To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combination with various biochemical and structural features of protein residues to discriminate neutral vs. deleterious mutations. However, the power of these methods is often limited because they either assume known protein structures or treat residues independently without fully considering their interactions. To address the above limitations, we build upon recent progress in machine learning, network analysis, and protein language models, and develop a sequences-based variant site prediction workflow based on the protein residue contact networks: 1. We employ and integrate various methods of building protein residue networks using state-of-the-art coevolution analysis tools (RaptorX, DeepMetaPSICOV, and SPOT-Contact) powered by deep learning. 2. We use machine learning algorithms (Random Forest, Gradient Boosting, and Extreme Gradient Boosting) to optimally combine 20 network centrality scores to jointly predict key residues as hot spots for disease mutations. 3. Using a dataset of 107 proteins rich in disease mutations, we rigorously evaluate the network scores individually and collectively (via machine learning). This work supports a promising strategy of combining an ensemble of network scores based on different coevolution analysis methods (and optionally predictive scores from other methods) via machine learning to predict hotspot sites of disease mutations, which will inform downstream applications of disease diagnosis and targeted drug design.

PemK's Arg24 is a crucial residue for PemIK toxin-antitoxin system to induce the persistence of Weissella cibaria against ciprofloxacin stress

The toxin-antitoxin (TA) system plays a key role in bacteria escaping antibiotic stress with persistence, however, the mechanisms by which persistence is controlled remain poorly understood. Weissella cibaria, a novel probiotic, can enters a persistent state upon encountering ciprofloxacin stress. Conversely, it resumes from the persistence when ciprofloxacin stress is relieved or removed. Here, it was found that PemIK TA system played a role in transitioning between these two states. And the PemIK was consisted of PemK, an endonuclease toxic to mRNA, and antitoxin PemI which neutralized its toxicity. The PemK specifically cleaved the U↓AUU in mRNA encoding enzymes involved in glycolysis, TCA cycle and respiratory chain pathways. This cleavage event subsequently disrupted the crucial cellular processes such as hydrogen transfer, electron transfer, NADH and FADH2 synthesis, ultimately leading to a decrease in ATP levels and an increase in membrane depolarization and persister frequency. Notably, Arg24 was a critical active residue for PemK, its mutation significantly reduced the mRNA cleavage activity and the adverse effects on metabolism. These insights provided a clue to comprehensively understand the mechanism by which PemIK induced the persistence of W. cibaria to escape ciprofloxacin stress, thereby highlighting another novel aspect PemIK respond for antibiotic stress.

Genetic analysis of TMPRSS6 catalytic domain variants in Mexican patients with iron treatment refractoriness

OBJECTIVE

To identify the TMPRSS6 gene variants in Mexican patients with iron treatment refractoriness, to describe hematological and iron profile parameters, and to use bioinformatic prediction and protein modeling tools to assess a possible biological impact for the detected missense variants.

METHODS

Nineteen patients referred with iron treatment refractoriness were studied. Peripheral blood was collected to determine hematic cytometry, iron profile, hemoglobin electrophoresis, and quantification. Molecular screening was carried out for exons 15 through 18 of the TMPRSS6 gene by Sanger sequencing and for frequent thalassemia variants by amplification-refractory mutation system-polymerase chain reaction (PCR) and gap-PCR. The biological impact of the detected missense variants was assessed using bioinformatic prediction and protein modeling tools.

RESULTS

We found 5 genetic variants in the matriptase-2 catalytic domain: 1 at intron-15/exon-16 junction (rs60484081) and 4 exonic, 3 missense (rs377054987, p.Gly626Asp; rs1384127820, p.Ser672Thr; rs855791, p.Val727Ala) and 1 synonymous (rs2235321, p.Tyr730=), with frequencies ranging from 0.18 to 0.53. No significant differences were observed in the hematological parameters or iron profile, considering type and number of variants. Bioinformatic predictions suggested a possible biological impact only for rs377054987.

CONCLUSIONS

The TMPRSS6 variants observed in Mexican patients with oral iron treatment refractoriness have high frequencies; nevertheless, their relationship with hematological and iron profile parameters needs further research. The possible biological impact for rs377054987 is due to size and amino acid hydrophobicity changes and hydrogen bond modifications.

Characterization of missense nonsynonymous single-nucleotide polymorphism of runt-related transcription factor-2 gene - An in silico approach

OBJECTIVES

Single-nucleotide polymorphism (SNP) codes for multiple amino acids, impacting protein functions and disease prognosis. Runt-related transcription factor-2 (RUNX2), a transcription factor linked to osteoblast differentiation, regulates cell proliferation in endothelium and osteoblastic cells. Understanding Runx2's role in nonosseous tissues is rapidly advancing. This study aims to identify harmful SNPs of the RUNX2 gene that may alter disease susceptibility using computational techniques.

METHODS

The study uses various in silico methods to identify nonsynonymous SNPs (nsSNPs) of the RUNX2 gene, which could potentially alter protein structure and functions, with further analyses by I-Mutant, ConSurf, Netsurf 3.0, GeneMANIA, and Have (y)Our Protein Explained.

RESULTS

Six missense nsSNPs were identified as potentially harmful, disease-causing, and damaging. Four were found to be unstable, while five were conserved. All six nsSNPs had a coiled secondary structure. Five nsSNPs were found to be destabilized.

CONCLUSION

The RUNX2 gene's deleterious missense nsSNPs were identified by this study, and they may be exploited in future experimental studies. These high-risk nsSNPs might be considered target molecules in therapeutic and diagnostic therapies in teeth and bone development.

Computational analysis of sodium-dependent phosphate transporter SLC20A1/PiT1 gene identifies missense variations C573F, and T58A as high-risk deleterious SNPs

SLC20A1/PiT1 is a sodium-dependent inorganic phosphate transporter, initially recognized as the retroviral receptor for Gibbon Ape Leukemia Virus in humans. SNPs in SLC20A1 is associated with Combined Pituitary Hormone Deficiency and Sodium Lithium Counter transport. Using in silico techniques, we have screened the nsSNPs for their deleterious effect on the structure and function of SLC20A1. Screening with sequence and structure-based tools on 430 nsSNPs, filtered 17 nsSNPs which are deleterious. To evaluate the role of these SNPs, protein modeling and MD simulations were performed. A comparative analysis of model generated with SWISS-MODEL and AlphaFold shows that many residues are in the disallowed region of Ramachandran plot. Since SWISS-MODEL structure has a 25-residue deletion, the AlphaFold structure was used to perform MD simulation for equilibration and structure refinement. Further, to understand perturbation of energetics, we performed in silico mutagenesis and ΔΔG calculation using FoldX on MD refined structures, which yielded SNPs that are neutral (3), destabilizing (12) and stabilizing (2) on protein structure. Furthermore, to elucidate the impact of SNPs on structure, we performed MD simulations to discern the changes in RMSD, Rg, RMSF and LigPlot of interacting residues. RMSF profiles of representative SNPs revealed that A114V (neutral) and T58A (positive) were more flexible & C573F (negative) was more rigid compared to wild type, which is also reflected in the changes in number of local interacting residues in LigPlot and ΔΔG. Taken together, our results show that SNPs can lead to structural perturbations and impact the function of SLC20A1 with potential implications for disease.Communicated by Ramaswamy H. Sarma.

Exploring the resistome, virulome, and mobilome of multidrug-resistant Klebsiella pneumoniae isolates: deciphering the molecular basis of carbapenem resistance

BACKGROUND

Klebsiella pneumoniae, a notorious pathogen for causing nosocomial infections has become a major cause of neonatal septicemia, leading to high morbidity and mortality worldwide. This opportunistic bacterium has become highly resistant to antibiotics due to the widespread acquisition of genes encoding a variety of enzymes such as extended-spectrum beta-lactamases (ESBLs) and carbapenemases. We collected Klebsiella pneumoniae isolates from a local tertiary care hospital from February 2019-February 2021. To gain molecular insight into the resistome, virulome, and genetic environment of significant genes of multidrug-resistant K. pneumoniae isolates, we performed the short-read whole-genome sequencing of 10 K. pneumoniae isolates recovered from adult patients, neonates, and hospital tap water samples.

RESULTS

The draft genomes of the isolates varied in size, ranging from 5.48 to 5.96 Mbp suggesting the genome plasticity of this pathogen. Various genes conferring resistance to different classes of antibiotics e.g., aminoglycosides, quinolones, sulfonamides, tetracycline, and trimethoprim were identified in all sequenced isolates. The highest resistance was observed towards carbapenems, which has been putatively linked to the presence of both class B and class D carbapenemases, blaNDM, and blaOXA, respectively. Moreover, the biocide resistance gene qacEdelta1 was found in 6/10 of the sequenced strains. The sequenced isolates exhibited a broad range of sequence types and capsular types. The significant antibiotic resistance genes (ARGs) were bracketed by a variety of mobile genetic elements (MGEs). Various spontaneous mutations in genes other than the acquired antibiotic-resistance genes were observed, which play an indirect role in making these bugs resistant to antibiotics. Loss or deficiency of outer membrane porins, combined with ESBL production, played a significant role in carbapenem resistance in our sequenced isolates. Phylogenetic analysis revealed that the study isolates exhibited evolutionary relationships with strains from China, India, and the USA suggesting a shared evolutionary history and potential dissemination of similar genes amongst the isolates of different origins.

CONCLUSIONS

This study provides valuable insight into the presence of multiple mechanisms of carbapenem resistance in K. pneumoniae strains including the acquisition of multiple antibiotic-resistance genes through mobile genetic elements. Identification of rich mobilome yielded insightful information regarding the crucial role of insertion sequences, transposons, and integrons in shaping the genome of bacteria for the transmission of various resistance-associated genes. Multi-drug resistant isolates that had the fewest resistance genes exhibited a significant number of mutations. K. pneumoniae isolate from water source displayed comparable antibiotic resistance determinants to clinical isolates and the highest number of virulence-associated genes suggesting the possible interplay of ARGs amongst bacteria from different sources.

Chromosome-scale genome assembly and annotation of Cotoneaster glaucophyllus

Cotoneaster glaucophyllus is a semi-evergreen plant that blossoms in late summer, producing dense, attractive, fragrant white flowers with significant ornamental and ecological value. Here, a chromosome-scale genome assembly was obtained by integrating PacBio and Illumina sequencing data with the aid of Hi-C technology. The genome assembly was 563.3 Mb in length, with contig N50 and scaffold N50 values of ~6 Mb and ~31 Mb, respectively. Most (95.59%) of the sequences were anchored onto 17 pseudochromosomes (538.4 Mb). We predicted 35,856 protein-coding genes, 1,401 miRNAs, 655 tRNAs, 425 rRNAs, and 795 snRNAs. The functions of 34,967 genes (97.52%) were predicted. The availability of this chromosome-level genome will provide valuable resources for molecular studies of this species, facilitating future research on speciation, functional genomics, and comparative genomics within the Rosaceae family.

Predisposing deleterious variants in the cancer-associated human kinases in the global populations

Human kinases play essential and diverse roles in the cellular activities of maintaining homeostasis and growth. Genetic mutations in the genes encoding the kinases (or phosphotransferases) have been linked with various types of cancers. In this study, we cataloged mutations in 500 kinases genes in >65,000 individuals of global populations from the Human Genetic Diversity Project (HGDP) and ExAC databases, and assessed their potentially deleterious impact by using the in silico tools SIFT, Polyphen2, and CADD. The analysis highlighted 35 deleterious non-synonymous SNVs in the ExAC and 5 SNVs in the HGDP project. Notably, a higher number of deleterious mutations was observed in the Non-Finnish Europeans (26 SNVs), followed by the Africans (14 SNVs), East Asians (13 SNVs), and South Asians (12 SNVs). The gene set enrichment analysis highlighted NTRK1 and FGFR3 being most significantly enriched among the kinases. The gene expression analysis revealed over-expression of NTRK1 in liver cancer, whereas, FGFR3 was found over-expressed in lung, breast, and liver cancers compared to their expression in the respective normal tissues. Also, 13 potential drugs were identified that target the NTRK1 protein, whereas 6 potential drugs for the FGFR3 target were identified. Taken together, the study provides a framework for exploring the predisposing germline mutations in kinases to suggest the underlying pathogenic mechanisms in cancers. The potential drugs are also suggested for personalized cancer management.

Potential new cancer biomarkers revealed by quantum chemistry associated with bioinformatics in the study of selectin polymorphisms

Understanding the complex mechanisms involved in diseases caused by or related to important genetic variants has led to the development of clinically useful biomarkers. However, the increasing number of described variants makes it difficult to identify variants worthy of investigation, and poses challenges to their validation. We combined publicly available datasets and open source robust bioinformatics tools with molecular quantum chemistry methods to investigate the involvement of selectins, important molecules in the cell adhesion process that play a fundamental role in the cancer metastasis process. We applied this strategy to investigate single nucleotide variants (SNPs) in the intronic and UTR regions and missense SNPs with amino acid changes in the SELL, SELP, SELE, and SELPLG genes. We then focused on thyroid cancer, seeking these SNPs potential to identify biomarkers for susceptibility, diagnosis, prognosis, and therapeutic targets. We demonstrated that SELL gene polymorphisms rs2229569, rs1131498, rs4987360, rs4987301 and rs2205849; SELE gene polymorphisms rs1534904 and rs5368; rs3917777, rs2205894 and rs2205893 of SELP gene; and rs7138370, rs7300972 and rs2228315 variants of SELPLG gene may produce important alterations in the DNA structure and consequent changes in the morphology and function of the corresponding proteins. In conclusion, we developed a strategy that may save valuable time and resources in future investigations, as we were able to provide a solid foundation for the selection of selectin gene variants that may become important biomarkers and deserve further investigation in cancer patients. Large-scale clinical studies in different ethnic populations and laboratory experiments are needed to validate our results.

Molecular and in silico investigation of a novel ECHS1 gene mutation in a consanguine family with short-chain enoyl-CoA hydratase deficiency and Mt-DNA depletion: effect on trimer assembly and catalytic activity

Short-chain enoyl-CoA hydratase deficiency (ECHS1D) is a rare congenital metabolic disorder that follows an autosomal recessive inheritance pattern. It is caused by mutations in the ECHS1 gene, which encodes a mitochondrial enzyme involved in the second step of mitochondrial β-oxidation of fatty acids. The main characteristics of the disease are severe developmental delay, regression, seizures, neurodegeneration, high blood lactate, and a brain MRI pattern consistent with Leigh syndrome. Here, we report three patients belonging to a consanguineous family who presented with mitochondrial encephalomyopathy. Whole-exome sequencing revealed a new homozygous mutation c.619G > A (p.Gly207Ser) at the last nucleotide position in exon 5 of the ECHS1 gene. Experimental analysis showed that normal ECHS1 pre-mRNA splicing occurred in all patients compared to controls. Furthermore, three-dimensional models of wild-type and mutant echs1 proteins revealed changes in catalytic site interactions, conformational changes, and intramolecular interactions, potentially disrupting echs1 protein trimerization and affecting its function. Additionally, the quantification of mtDNA copy number variation in blood leukocytes showed severe mtDNA depletion in all probands.

Insights from the SNP analysis of TYMP gene linking MNGIE

TYMP gene, which codes for thymidine phosphorylase (TP) is also known as platelet-derived endothelial cell growth factor (PD-ECGF). TP plays crucial roles in nucleotide metabolism and angiogenesis. Mutations in the TYMP gene can lead to Mitochondrial Neurogastrointestinal Encephalopathy (MNGIE) syndrome, a rare genetic disorder. Our main objective was to evaluate the impact of detrimental non-synonymous single nucleotide polymorphisms (nsSNPs) on TP protein structure and predict harmful variants in untranslated regions (UTR). We employed a combination of predictive algorithms to identify nsSNPs with potential deleterious effects, followed by molecular modeling analysis to understand their effects on protein structure and function. Using 13 algorithms, we identified 119 potentially deleterious nsSNPs, with 82 located in highly conserved regions. Of these, 53 nsSNPs were functional and exposed, while 79 nsSNPs reduced TP protein stability. Further analysis of 18 nsSNPs through 3D protein structure analysis revealed alterations in amino acid interactions, indicating their potential impact on protein function. This will help in the development of faster and more efficient genetic tests for detecting TYMP gene mutations.

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.

The identification of small molecule inhibitors with anthelmintic activities that target conserved proteins among ruminant gastrointestinal nematodes

Gastrointestinal nematode (GIN) infections are a major concern for the ruminant industry worldwide and result in significant production losses. Naturally occurring polyparasitism and increasing drug resistance that potentiate disease outcomes are observed among the most prevalent GINs of veterinary importance. Within the five major taxonomic clades, clade Va represents a group of GINs that predominantly affect the abomasum or small intestine of ruminants. However, the development of effective broad-spectrum anthelmintics against ruminant clade Va GINs has been challenged by a lack of comprehensive druggable genome resources. Here, we first assembled draft genomes for three clade Va species (Cooperia oncophora, Trichostrongylus colubriformis, and Ostertagia ostertagi) and compared them with closely related ruminant GINs. Genome-wide phylogenetic reconstruction showed a relationship among ruminant GINs structured by taxonomic classification. Orthogroup (OG) inference and functional enrichment analyses identified 220 clade Va-specific and Va-conserved OGs, enriched for functions related to cell cycle and cellular senescence. Further transcriptomic analysis identified 61 taxonomically and functionally conserved clade Va OGs that may function as drug targets for new broad-spectrum anthelmintics. Chemogenomic screening identified 11 compounds targeting homologs of these OGs, thus having potential anthelmintic activity. In in vitro phenotypic assays, three kinase inhibitors (digitoxigenin, K-252a, and staurosporine) exhibited broad-spectrum anthelmintic activities against clade Va GINs by obstructing the motility of exsheathed L3 (xL3) or molting of xL3 to L4. These results demonstrate valuable applications of the new ruminant GIN genomes in gaining better insights into their life cycles and offer a contemporary approach to discovering the next generation of anthelmintics.IMPORTANCEGastrointestinal nematode (GIN) infections in ruminants are caused by parasites that inhibit normal function in the digestive tract of cattle, sheep, and goats, thereby causing morbidity and mortality. Coinfection and increasing drug resistance to current therapeutic agents will continue to worsen disease outcomes and impose significant production losses on domestic livestock producers worldwide. In combination with ongoing therapeutic efforts, advancing the discovery of new drugs with novel modes of action is critical for better controlling GIN infections. The significance of this study is in assembling and characterizing new GIN genomes of Cooperia oncophora, Ostertagia ostertagi, and Trichostrongylus colubriformis for facilitating a multi-omics approach to identify novel, biologically conserved drug targets for five major GINs of veterinary importance. With this information, we were then able to demonstrate the potential of commercially available compounds as new anthelmintics.

Computational Tools to Assist in Analyzing Effects of the SERPINA1 Gene Variation on Alpha-1 Antitrypsin (AAT)

In the rapidly advancing field of bioinformatics, the development and application of computational tools to predict the effects of single nucleotide variants (SNVs) are shedding light on the molecular mechanisms underlying disorders. Also, they hold promise for guiding therapeutic interventions and personalized medicine strategies in the future. A comprehensive understanding of the impact of SNVs in the SERPINA1 gene on alpha-1 antitrypsin (AAT) protein structure and function requires integrating bioinformatic approaches. Here, we provide a guide for clinicians to navigate through the field of computational analyses which can be applied to describe a novel genetic variant. Predicting the clinical significance of SERPINA1 variation allows clinicians to tailor treatment options for individuals with alpha-1 antitrypsin deficiency (AATD) and related conditions, ultimately improving the patient's outcome and quality of life. This paper explores the various bioinformatic methodologies and cutting-edge approaches dedicated to the assessment of molecular variants of genes and their product proteins using SERPINA1 and AAT as an example.

Impact of nsSNPs in human AIM2 and IFI16 gene: a comprehensive in silico analysis

AIM2 and IFI16 are the most studied members of AIM2-like receptors (ALRs) in humans and share a common N-Terminal PYD domain and C-terminal HIN domain. The HIN domain binds to dsDNA in response to the invasion of bacterial and viral DNA, and the PYD domain directs apoptosis-associated speck-like protein via protein-protein interactions. Hence, activation of AIM2 and IFI16 is crucial for protection against pathogenic assaults, and any genetic variation in these inflammasomes can dysregulate the human immune system. In this study, different computational tools were used to identify the most deleterious and disease-causing non-synonymous single nucleotide polymorphisms (nsSNPs) in AIM2 and IFI16 proteins. Molecular dynamic simulation was performed for the top damaging nsSNPs to study single amino acid substitution-induced structural alterations in AIM2 and IFI16. The observed results suggest that the variants G13V, C304R, G266R, and G266D for AIM2, and G13E and C356F are deleterious and affect structural integrity. We hope that the suggested deleterious nsSNPs and structural dynamics of AIM2 and IFI16 variants will guide future research to better understand the function of these variants with large-scale studies and may assist in fresher therapeutics focusing on these polymorphisms.Communicated by Ramaswamy H. Sarma.

Structural effect of the H992D/H418D mutation of angiotensin-converting enzyme in the Indian population: implications for health and disease

The Non synonymous SNPs (nsSNPs) of the renin-angiotensin-system (RAS) pathway, unique to the Indian population were investigated in view of its importance as an endocrine system. nsSNPs of the RAS pathway genes were mined from the IndiGenome database. Damaging nsSNPs were predicted using SIFT, PredictSNP, SNP and GO, Snap2 and Protein Variation Effect Analyzer. Loss of function was predicted based on protein stability change using I mutant, PremPS and CONSURF. The structural impact of the nsSNPs was predicted using HOPE and Missense3d followed by modeling, refinement, and energy minimization. Molecular Dynamics studies were carried out using Gromacsv2021.1. 23 Indian nsSNPs of the RAS pathway genes were selected for structural analysis and 8 were predicted to be damaging. Further sequence analysis showed that HEMGH zinc binding motif changes to HEMGD in somatic ACE-C domain (sACE-C) H992D and Testis ACE (tACE) H418D resulted in loss of zinc coordination, which is essential for enzymatic activity in this metalloprotease. There was a loss of internal interactions around the zinc coordination residues in the protein structural network. This was also confirmed by Principal Component Analysis, Free Energy Landscape and residue contact maps. Both mutations lead to broadening of the AngI binding cavity. The H992D mutation in sACE-C is likely to be favorable for cardiovascular health, but may lead to renal abnormalities with secondary impact on the heart. H418D in tACE is potentially associated with male infertility.Communicated by Ramaswamy H. Sarma.

FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework

Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer's self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BACKGROUND

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

RESULTS

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

CONCLUSIONS

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

In vivo functional phenotypes from a computational epistatic model of evolution

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their wild-type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.

A novel homozygous TSGA10 missense variant causes acephalic spermatozoa syndrome in a Pakistani family

BACKGROUND

Acephalic spermatozoa syndrome is a rare type of teratozoospermia causing male infertility due to detachment of the sperm head and flagellum, which precludes fertilization potential. Although loss-of-function variations in several genes, including TSGA10, have been associated with acephalic spermatozoa syndrome, the genetic cause of many cases remains unclear.

RESULTS

We recruited a Pakistani family with two infertile brothers who suffered from acephalic spermatozoa syndrome. Through whole-exome sequencing (WES) followed by Sanger sequencing, we identified a novel missense variant in TSGA10 (c.1112T > C, p. Leu371Pro), which recessively co-segregated with the acephalic spermatozoa syndrome within this family. Ultrastructural analyses of spermatozoa from the patient revealed that 98% of flagellar cross-sections displayed abnormal axonemal ultrastructure, in addition to the head-flagellum detachment. Real-time quantitative PCR analysis revealed almost no detectable TSAG10 mRNA and western blot analysis also failed to detect TSAG10 protein in patient's sperm samples while TSGA10 expression was clearly detected in control samples. Consistently, immunofluorescence analysis demonstrated the presence of TSGA10 signal in the midpiece of sperm from the control but a complete absence of TSGA10 signal in sperm from the patient.

CONCLUSION

Altogether, our study identifies a novel TSGA10 pathogenic variant as a cause of acephalic spermatozoa syndrome in this family and provides information regarding the clinical manifestations associated with TSGA10 variants in human.

Exploring DNA Damage and Repair Mechanisms: A Review with Computational Insights

DNA damage is a critical factor contributing to genetic alterations, directly affecting human health, including developing diseases such as cancer and age-related disorders. DNA repair mechanisms play a pivotal role in safeguarding genetic integrity and preventing the onset of these ailments. Over the past decade, substantial progress and pivotal discoveries have been achieved in DNA damage and repair. This comprehensive review paper consolidates research efforts, focusing on DNA repair mechanisms, computational research methods, and associated databases. Our work is a valuable resource for scientists and researchers engaged in computational DNA research, offering the latest insights into DNA-related proteins, diseases, and cutting-edge methodologies. The review addresses key questions, including the major types of DNA damage, common DNA repair mechanisms, the availability of reliable databases for DNA damage and associated diseases, and the predominant computational research methods for enzymes involved in DNA damage and repair.

A computational study on structural and functional consequences of nsSNPs in human dopa decarboxylase

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson's disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches.Communicated by Ramaswamy H. Sarma.

Colicins and T6SS-based competition systems enhance enterotoxigenic E. coli (ETEC) competitiveness

Diarrheal diseases are still a significant problem for humankind, causing approximately half a million deaths annually. To cause diarrhea, enteric bacterial pathogens must first colonize the gut, which is a niche occupied by the normal bacterial microbiota. Therefore, the ability of pathogenic bacteria to inhibit the growth of other bacteria can facilitate the colonization process. Although enterotoxigenic Escherichia coli (ETEC) is one of the major causative agents of diarrheal diseases, little is known about the competition systems found in and used by ETEC and how they contribute to the ability of ETEC to colonize a host. Here, we collected a set of 94 fully assembled ETEC genomes by performing whole-genome sequencing and mining the NCBI RefSeq database. Using this set, we performed a comprehensive search for delivered bacterial toxins and investigated how these toxins contribute to ETEC competitiveness in vitro. We found that type VI secretion systems (T6SS) were widespread among ETEC (n = 47). In addition, several closely related ETEC strains were found to encode Colicin Ia and T6SS (n = 8). These toxins provide ETEC competitive advantages during in vitro competition against other E. coli, suggesting that the role of T6SS as well as colicins in ETEC biology has until now been underappreciated.

A novel NPHP4 homozygous missense variant identified in infertile brothers with multiple morphological abnormalities of the sperm flagella

PURPOSE

Asthenozoospermia is an important cause of male infertility, and the most serious type is characterized by multiple morphological abnormalities of the sperm flagella (MMAF). However, the precise etiology of MMAF remains unknown. In the current study, we recruited a consanguineous Pakistani family with two infertile brothers suffering from primary infertility due to MMAF without obvious signs of PCD.

METHODS

We performed whole-exome sequencing on DNAs of the patients, their parents, and a fertile brother and identified the homozygous missense variant (c.1490C > G (p.P497R) in NPHP4 as the candidate mutation for male infertility in this family.

RESULTS

Sanger sequencing confirmed that this mutation recessively co-segregated with the MMAF in this family. In silico analysis revealed that the mutation site is conserved across different species, and the identified mutation also causes abnormalities in the structure and hydrophobic interactions of the NPHP4 protein. Different bioinformatics tools predict that NPHP4p.P497R mutation is pathogenic. Furthermore, Papanicolaou staining and scanning electron microscopy of sperm revealed that affected individuals displayed typical MMAF phenotype with a high percentage of coiled, bent, short, absent, and/or irregular flagella. Transmission electron microscopy images of the patient's spermatozoa revealed significant anomalies in the sperm flagella with the absence of a central pair of microtubules (9 + 0) in every section scored.

CONCLUSIONS

Taken together, these results show that the homozygous missense mutation in NPHP4 is associated with MMAF.

INF2 and ROBO2 gene mutation in an Indian family with end stage renal failure and follow-up of renal transplantation

BACKGROUND

Accurate genetic diagnosis of end-stage renal disease patients with a family history of renal dysfunction is very essential. It not only helps in proper prognosis, but becomes crucial in designating donor for live related renal transplant. We here present a case of family with deleterious mutations in INF2 and ROBO2 and its importance of genetic testing before preparing for kidney transplantation.

CASE PRESENTATION

We report the case of a 29-year-female with end-stage renal disease and rapidly progressive renal failure. Mutational analysis revealed an Autosomal Dominant inheritance pattern and mutation in exon 4 of the INF2 gene (p. Thr215Ser) and exon 26 of the ROBO2 gene (p. Arg1371Cys). Her mother was diagnosed for CKD stage 4 with creatinine level of 4.3 mg/dL. Genetic variants (INF2 and ROBO2) identified in proband were tested in her sisters and mother. Her elder sister was positive for both heterozygous variants (INF2 and ROBO2). Her mother was positive for mutation in INF2 gene, and her donor elder sister did not showed mutation in INF2 gene and had mutation in ROBO2 gene without any clinical symptoms.

CONCLUSION

This case report emphasize that familial genetic screening has allowed us in allocating the donor selection in family where family member had history of genetic defect of Chronic Kidney Disease. Information of the causative renal disorder is extremely valuable for risk-assessment and planning of kidney transplantation.

Investigation of pathogenic germline variants in gastric cancer and development of "GasCanBase" database

BACKGROUND

Gastric cancer, which is also known as stomach cancer, can be influenced by both germline and somatic mutations. Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in germline have long been reported to play a pivotal role in cancer progression.

AIM

The aim of this study is to examine the nsSNP in GC-associated genes. The study also aims to develop a database with extensive information regarding the nsSNPs in the GC-associated genes and their impacts.

METHODS AND RESULTS

A total of 34,588 nsSNPs from 1,493,460 SNPs of the 40 genes were extracted from the available SNP database. Drug binding and energy minimization were examined by molecular docking and YASARA. To validate the existence of the germline CDH1 gene mutation (rs34466743) in the isolated blood DNA of gastric cancer (GC) patients, polymerase chain reaction (PCR) and DNA sequencing were performed. According to the results of the gene network analysis, 17 genes may interact with other types of cancer. A total of 11,363 nsSNPs were detected within the 40 GC genes. Among these, 474 nsSNPs were predicted to be damaging and 40 to be the most damaging. The SNPs in domain regions were thought to be strong candidates that alter protein functions. Our findings proposed that most of the selected nsSNPs were within the domains or motif regions. Free Energy Deviation calculation of protein structure pointed toward noteworthy changes in the structure of each protein that can demolish its natural function. Subsequently, drug binding confirmed the structural variation and the ineffectiveness of the drug against the mutant model in individuals with these germline variants. Furthermore, in vitro analysis of the rs34466743 germline variant from the CDH1 gene confirmed the strength and robustness of the pipeline that could expand the somatic alteration for causing cancer. In addition, a comprehensive gastric cancer polymorphism database named "GasCanBase" was developed to make data available to researchers.

CONCLUSION

The findings of this study and the "GasCanBase" database may greatly contribute to our understanding of molecular epidemiology and the development of precise therapeutics for gastric cancer. GasCanBase is available at: https://www.gascanbase.com/.

PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning

PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins frequently altered in various cancer types. The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at https://loschmidt.chemi.muni.cz/predictonco.

Computational study of the motor neuron protein KIF5A to identify nsSNPs, bioactive compounds, and its key regulators

Introduction: Kinesin family member 5A (KIF5A) is a motor neuron protein expressed in neurons and involved in anterograde transportation of organelles, proteins, and RNA. Variations in the KIF5A gene that interfere with axonal transport have emerged as a distinguishing feature in several neurodegenerative disorders, including hereditary spastic paraplegia (HSP10), Charcot-Marie-Tooth disease type 2 (CMT2), and Amyotrophic Lateral Sclerosis (ALS). Methods: In this study, we implemented a computational structural and systems biology approach to uncover the role of KIF5A in ALS. Using the computational structural biology method, we explored the role of non-synonymous Single Nucleotide Polymorphism (nsSNPs) in KIF5A. Further, to identify the potential inhibitory molecule against the highly destabilizing structure variant, we docked 24 plant-derived phytochemicals involved in ALS. Results: We found KIF5AS291F variant showed the most structure destabilizing behavior and the phytocompound "epigallocatechin gallate" showed the highest binding affinity (-9.0 Kcal/mol) as compared to wild KIF5A (-8.4 Kcal/mol). Further, with the systems biology approach, we constructed the KIF5A protein-protein interaction (PPI) network to identify the associated Kinesin Families (KIFs) proteins, modules, and their function. We also constructed a transcriptional and post-transcriptional regulatory network of KIF5A. With the network topological parameters of PPIN (Degree, Bottleneck, Closeness, and MNC) using CytoHubba and computational knock-out experiment using Network Analyzer, we found KIF1A, 5B, and 5C were the significant proteins. The functional modules were highly enriched with microtubule motor activity, chemical synaptic transmission in neurons, GTP binding, and GABA receptor activity. In regulatory network analysis, we found KIF5A post-transcriptionally down-regulated by miR-107 which is further transcriptionally up-regulated by four TFs (HIF1A, PPARA, SREBF1, and TP53) and down-regulated by three TFs (ZEB1, ZEB2, and LIN28A). Discussion: We concluded our study by finding a crucial variant of KIF5A and its potential therapeutic target (epigallocatechin gallate) and KIF5A associated significant genes with important regulators which could decrypt the novel therapeutics in ALS and other neurodegenerative diseases.

In silico SNP prediction of selected protein orthologues in insect models for Alzheimer's, Parkinson's, and Huntington's diseases

Alzheimer's, Parkinson's, and Huntington's are the most common neurodegenerative diseases that are incurable and affect the elderly population. Discovery of effective treatments for these diseases is often difficult, expensive, and serendipitous. Previous comparative studies on different model organisms have revealed that most animals share similar cellular and molecular characteristics. The meta-SNP tool includes four different integrated tools (SIFT, PANTHER, SNAP, and PhD-SNP) was used to identify non synonymous single nucleotide polymorphism (nsSNPs). Prediction of nsSNPs was conducted on three representative proteins for Alzheimer's, Parkinson's, and Huntington's diseases; APPl in Drosophila melanogaster, LRRK1 in Aedes aegypti, and VCPl in Tribolium castaneum. With the possibility of using insect models to investigate neurodegenerative diseases. We conclude from the protein comparative analysis between different insect models and nsSNP analyses that D. melanogaster is the best model for Alzheimer's representing five nsSNPs of the 21 suggested mutations in the APPl protein. Aedes aegypti is the best model for Parkinson's representing three nsSNPs in the LRRK1 protein. Tribolium castaneum is the best model for Huntington's disease representing 13 SNPs of 37 suggested mutations in the VCPl protein. This study aimed to improve human neural health by identifying the best insect to model Alzheimer's, Parkinson's, and Huntington's.

Identification of High-Risk Single Nucleotide Polymorphisms in the Human CYB5R3 Gene Responsible for Recessive Congenital Methemoglobinemia: A Computational Approach

Introduction

NADH-cytochrome b5 reductase deficiency due to pathogenic variants in the CYB5R3 gene causes recessive congenital methemoglobinemia (RCM) type I or type II. In type I, cyanosis from birth is the only major symptom, and the enzyme deficiency is restricted only to erythrocytes. Whereas in type II, cyanosis is associated with severe neurological manifestations, and the enzyme deficiency is generalized to all tissues.

Methods

In this study, several computational methods (SIFT, Polyphen-2, PROVEAN, Mutation Assessor, Panther, Phd-SNP, SNPs&GO, SNAP2, Align, GVGD, MutPred2, I-Mutant 2.0, MUpro, Duet, ConSurf and Netsurf-2.0 tools) were used to find the most deleterious nsSNPs in the CYB5R3 gene. Furthermore, structural analysis by Swiss-PDB viewer, protein-ligand docking using FTSite, and protein-protein interaction using STRING were carried out to evaluate the impact of these nsSNPs on the protein structure and function.

Results

Our in silico analysis suggested that out of 339 nsSNPs of the CYB5R3 gene, 17 (L47H, L47P, R61P, L73R G76D, G76C, P96H, G104C, S128P, G144D, P145S, L149P, Y151H, M177T, I178T, I216N, and G251V), are the most deleterious. Among them, two (P96H and S128P) were reported to be associated with the severe form RCM type II, six are related to RCM type I (G104C, G144D, P145S, L149P, M177T, and I178T), and the remaining nine high-risk nsSNPs have not yet been reported in RCM patients.

Discussion

This study highlighted the potential pathogenic nsSNPs of the CYB5R3 gene. To comprehend how these most harmful nsSNPs contribute to disease, it is crucial to experimentally validate their functional effects.

A repertoire of candidate effector proteins of the fungus Ceratocystis cacaofunesta

The genus Ceratocystis includes many phytopathogenic fungi that affect different plant species. One of these is Ceratocystis cacaofunesta, which is pathogenic to the cocoa tree and causes Ceratocystis wilt, a lethal disease for the crop. However, little is known about how this pathogen interacts with its host. The knowledge and identification of possible genes encoding effector proteins are essential to understanding this pathosystem. The present work aimed to predict genes that code effector proteins of C. cacaofunesta from a comparative analysis of the genomes of five Ceratocystis species available in databases. We performed a new genome annotation through an in-silico analysis. We analyzed the secretome and effectorome of C. cacaofunesta using the characteristics of the peptides, such as the presence of signal peptide for secretion, absence of transmembrane domain, and richness of cysteine residues. We identified 160 candidate effector proteins in the C. cacaofunesta proteome that could be classified as cytoplasmic (102) or apoplastic (58). Of the total number of candidate effector proteins, 146 were expressed, presenting an average of 206.56 transcripts per million. Our database was created using a robust bioinformatics strategy, followed by manual curation, generating information on pathogenicity-related genes involved in plant interactions, including CAZymes, hydrolases, lyases, and oxidoreductases. Comparing proteins already characterized as effectors in Sordariomycetes species revealed five groups of protein sequences homologous to C. cacaofunesta. These data provide a valuable resource for studying the infection mechanisms of these pathogens in their hosts.

ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations

Background

Genomic variations may cause deleterious effects on protein functionality and perturb biological processes. Elucidating the effects of variations is critical for developing novel treatment strategies for diseases of genetic origin. Computational approaches have been aiding the work in this field by modeling and analyzing the mutational landscape. However, new approaches are required, especially for accurate representation and data-centric analysis of sequence variations.

Method

In this study, we propose ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations), a method for the featurization (i.e., quantitative representation) of single amino acid variations (SAVs), which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. ASCARIS utilizes the direct and spatial correspondence between the location of the SAV on the sequence/structure and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.), along with structural features and physicochemical properties. The main novelty of this method lies in constructing reusable numerical representations of SAVs via functional annotations.

Results

We statistically analyzed the relationship between these features and the consequences of variations and found that each carries information in this regard. To investigate potential applications of ASCARIS, we trained variant effect prediction models that utilize our SAV representations as input. We carried out an ablation study and a comparison against the state-of-the-art methods and observed that ASCARIS has a competing and complementary performance against widely-used predictors. ASCARIS can be used alone or in combination with other approaches to represent SAVs from a functional perspective. ASCARIS is available as a programmatic tool at https://github.com/HUBioDataLab/ASCARIS and as a web-service at https://huggingface.co/spaces/HUBioDataLab/ASCARIS.

The eNOS-G894T genetic polymorphism and risk of preeclampsia: A case-control study, an updated meta-analysis, and a bioinformatic assay

OBJECTIVES

Preeclampsia (PE) is a leading cause of maternal death worldwide and involves vascular endothelial dysfunction. The aim of this study was to investigate the association of the G894T polymorphism in the endothelial nitric oxide synthase (eNOS) gene and the risk of preeclampsia in a case-control design in an Iranian population, which was followed by a meta-analysis and an in silico approach.

METHODS

In the case-control study, 300 people including 135 pregnant women with preeclampsia and 165 healthy pregnant women were included. The genotype of G894T polymorphism was determined by the PCR-RFLP method. We searched authoritative scientific databases to find eligible studies for meta-analysis. The odds ratio with a 95% confidence interval was estimated to find the strength of the association of the mentioned polymorphism with the risk of preeclampsia. In addition, the effect of G894T transversion on eNOS gene function was evaluated by some bioinformatics tools.

RESULTS

Our case-control data showed that the G894T polymorphism is associated with an increased risk of preeclampsia. In the meta-analysis, 33 eligible studies were included, and the results showed that the G894T polymorphism is associated with an increased risk of preeclampsia in the overall analysis and some stratified analyses. In addition, the structural analysis showed that the G894T variant can affect the splicing process as well as the protein stability.

CONCLUSIONS

Based on the results, the aforementioned polymorphism may be a risk factor for preeclampsia and could be considered a potential molecular biomarker for screening susceptible individuals.

Bioinformatic analysis of the effect of SNPs in the pig TERT gene on the structural and functional characteristics of the enzyme to develop new genetic markers of productivity traits

BACKGROUND

Telomerase reverse transcriptase (TERT) plays a crucial role in synthesizing telomeric repeats that safeguard chromosomes from damage and fusion, thereby maintaining genome stability. Mutations in the TERT gene can lead to a deviation in gene expression, impaired enzyme activity, and, as a result, abnormal telomere shortening. Genetic markers of productivity traits in livestock can be developed based on the TERT gene polymorphism for use in marker-associated selection (MAS). In this study, a bioinformatic-based approach is proposed to evaluate the effect of missense single-nucleotide polymorphisms (SNPs) in the pig TERT gene on enzyme function and structure, with the prospect of developing genetic markers.

RESULTS

A comparative analysis of the coding and amino acid sequences of the pig TERT was performed with corresponding sequences of other species. The distribution of polymorphisms in the pig TERT gene, with respect to the enzyme's structural-functional domains, was established. A three-dimensional model of the pig TERT structure was obtained through homological modeling. The potential impact of each of the 23 missense SNPs in the pig TERT gene on telomerase function and stability was assessed using predictive bioinformatic tools utilizing data on the amino acid sequence and structure of pig TERT.

CONCLUSIONS

According to bioinformatic analysis of 23 missense SNPs of the pig TERT gene, a predictive effect of rs789641834 (TEN domain), rs706045634 (TEN domain), rs325294961 (TRBD domain) and rs705602819 (RTD domain) on the structural and functional parameters of the enzyme was established. These SNPs hold the potential to serve as genetic markers of productivity traits. Therefore, the possibility of their application in MAS should be further evaluated in associative analysis studies.