In silico analysis predicting effects of deleterious SNPs of human RASSF5 gene on its structure and functions

In-silico analysis of nsSNPs in BCL-2 family proteins: Implications for colorectal cancer pathogenesis and therapeutics

Colorectal cancer (CRC) is a multifaceted disease characterized by abnormal cell proliferation in the colon and rectum. The BCL-2 family proteins are implicated in CRC pathogenesis, yet the impacts of genetic variations within these proteins remains elusive. This in-silico study employs diverse sequence- and structure-based bioinformatics tools to identify potentially pathogenic nonsynonymous single nucleotide polymorphisms (nsSNPs) in BCL-2 family proteins. Leveraging computational tools including SIFT, PolyPhen-2, SNPs&GO, PhD-SNP, PANTHER, and Condel, 94 nsSNPs were predicted as deleterious, damaging, and disease-associated by at least five tools. Stability analysis with I-Mutant2.0, MutPred, and PredictSNP further identified 31 nsSNPs that reduce protein stability. Conservation analysis highlighted highly functional, exposed variants (rs960653284, rs758817904, rs1466732626, rs569276903, rs746711568, rs764437421, rs779690846, and rs2038330314) and structural, buried variants (rs376149674, rs1375767408, rs1582066443, rs367558446, rs367558446, rs1319541919, and rs1370070128). To explore the functional effects of these mutations, molecular docking and molecular dynamics simulations were conducted. G233D (rs376149674) and R12G (rs960653284) mutations in the BCL2 protein exhibited the greatest differences in docking scores with d-α-Tocopherol and Tocotrienol, suggesting enhanced protein-ligand interactions. The simulations revealed that d-α-Tocopherol and Tocotrienol (strong binders) contributed to greater stability of BCL-2 family proteins, while Fluorouracil, though weaker, still demonstrated selective binding stability. This work represents the first comprehensive computational analysis of functional nsSNPs in BCL-2 family proteins, providing insights into their roles in CRC pathogenesis. While these findings demand experimental validation, they hold great promise for guiding future large-scale population studies, facilitating drug repurposing efforts, and advancing the development of targeted diagnostic and therapeutic modalities for CRC.

In silico evaluation of missense SNPs in cancer-associated Cystatin A protein and their potential to disrupt Cathepsin B interaction

Cystatin A (CSTA) functions as a cysteine protease inhibitor by forming tight complexes with the cathepsins. Pathogenic mutations in the CSTA gene can disrupt this interaction, potentially leading to physiological ailments. In this study, eight bioinformatics tools (SIFT, PolyPhen-2, PROVEAN, P-Mut, MutPred2, SNAP2, SNPs & GO, and PHD-SNP) were implemented to analyze non-synonymous SNPs from the dbSNP database. Five mutations (Y43C, Y43N, V48F, Y53H, and E94K) located in the conserved region were found to be highly deleterious and less stabilizing. The protein-protein interaction network found that Cathepsin B (CTSB) interacts highly with CSTA. Mutated CSTAs were created by homology modeling, and their altered binding with CTSB was examined through molecular docking and dynamics simulations. Among these, the Y53H (rs1448459675) and E94K (rs200394711) mutants were recognized as weaker inhibitors because they had 2.5 % and an 8 % lower binding affinity, respectively. Moreover, the E94K-CTSB complex, with a root mean square deviation (RMSD) above 5 Å, was found to be highly unstable during molecular dynamics. The root mean square fluctuation (RMSF) of the E94K mutant showed insufficient flexibility, indicating a reduced capacity to suppress CTSB. These findings suggest that the E94K mutation could affect the protein structure and cathepsin B interaction, potentially leading to pathological consequences as evidenced by colorectal adenocarcinoma patients in the COSMIC (Catalogue of Somatic Mutations in Cancer) database.

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

AIMS

Lung adenocarcinoma (LUAD) is the most prominent histological subtype among the lung cancer which is a leading cause in the cancer mortality rate. High mutational and glycolytic rates are the major reported alterations in the lung cancer. Here in our study we are elucidating the structural and functional role of key glycolytic enzyme Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and associated SNPs in LUAD progression.

MATERIALS AND METHODS

Our gene expression analysis reveals high expression of GAPDH in the LUAD. In silico tools and analysis were used for the identification and characterization of the deleterious SNPs. Molecular Docking and dynamics simulations (MDS) studies characterized the structural consequences of prioritized deleterious mutations.

KEY FINDINGS

The sequence based analysis to identify SNPs in GAPDH resulted in 28 deleterious SNPs and 6 SNPs among them showed deleterious and damaging effect. The structural based analysis resulted in 2 stabilizing SNPs of rs ids rs11549328 (D39Y) and rs200102749 (S51Y) in the conserved domain. The IDR and PTM analysis of the GAPDH sequence resulted an IDR region from 191 to 194 positions with an IDR score of 0.511, 0.520, 0.517 and 0.503 with the PTM modifications.

SIGNIFICANCE

The identified deleterious SNPs (D39Y and S51Y) fall in the functional and conserved domain of GAPDH. In addition, the existence of PTMs within the IDR region of the GAPDH may contribute to its enhanced glycolytic activity in LUAD. The results of our study provide potential background deleterious mutants the pathological aspect of GAPDH in LUAD progression.

Explicit water-ligand docking, drug-likeness and molecular dynamics simulation analysis to predict the potency of Boerhavia diffusa plant extract against mutant wilms tumor-1 protein responsible for type 4 nephrotic syndrome

Thestructure and function of a protein are closely connected. Changes in a protein structure can impact on its function. Nephrotic syndrome type 4 (NPHS4) is an uncommon genetic condition caused by mutations in the WT1 gene, which codes for the wilms tumor-1 protein. Several studies have discovered that patients with nephrotic syndromes are resistant to steroid therapy and are likely to develop end-stage renal failure. The use of phytochemicals-based therapeutics is in demand due to their high potential and low toxicity. Based on this context, we employed the Autodock raccoon to screen 67 distinct potent phytochemicals from the Boerhavia diffusa (B.diffusa) plant against the wild type and mutant model at position C388R (cysteine is replaced with arginine at position 388) of the C-terminal DNA binding domain of the wilms tumor-1 protein. Out of 67 active compounds, only 10 compounds (lunamarine, kaempferol, boeravinone B, boeravinone E, boeravinone A, boeravinone F, boeravinone J, boeravinone P, boerhaavic acid and 4',7-dihydroxy-3'-methylflavone) were screened based on drug-likeness properties and binding energy for explicit water ligand docking against wild and mutant model of C-terminal DNA binding domain of wilms tumor-1 protein. Consequently, the hydrated form of boeravinone F and boeravinone A demonstrated the highest binding energy against the protein mutant model described above, the binding energies were -9.56 and -8.96 Kcal/mol, respectively. Followed by explicit water ligand docking the microscopic properties of wild type, mutant, mutant-boeravinone F complex, and mutant-boeravinone A complex systems were evaluated using molecular dynamics simulation steps with 100 ns of trajectory. The findings indicate that, due to mutation the mutant model system had decreasing stability and decreasing compactness nature. However, boeravinone A effectively monitored the mutant system's stability and improved compactness nature after binding with the mutant model. Boeravinone A with the mutant model complex system was determined to have the lowest energy point as compared to other studied systems. The study revealed minimal structural alterations and reduced conformational mobility.

Computational analysis of the deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in TYR gene impacting human tyrosinase protein and the protein stability

Tyrosinase, a copper-containing oxidase, plays a vital role in the melanin biosynthesis pathway. Mutations in the tyrosinase gene can disrupt the hydroxylation of tyrosine, leading to decreased production of 3,4-dihydroxyphenylalanine (DOPA). Consequently, this impairs the subsequent formation of dopaquinone, a key precursor in melanin pigment synthesis. This study aimed to identify the deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) within the TYR gene that exert an influence on the human TYR protein. Additionally, we evaluated the impact of 10 FDA-approved drugs on the protein stability of mutated structures, exploring the potential for inhibitory pharmaceutical interventions. Through various bioinformatics tools, we detected 47900 nsSNPs, particularly K142M, I151N, M179R, S184L, L189P, and C321R, which were found to be the most deleterious variants, decreasing the protein stability. These drugs (Sapropterin, Azelaic Acid, Menobenzone, Levodopda, Mequinol, Arbutin, Hexylresorcinol, Artenimol, Alloin and Curcumin) interacted with the binding sites in four mutant models K142M, I151N, M179R, and S184L proving that these ligands directly bind with the active site of mutant tyrosinase protein to inhibit it's working. On the other hand, two mutant models L189P and C321R did not show any binding site residue interaction with any ligands. In conclusion, this in-silico analysis of deleterious nsSNPs in the TYR gene, coupled with the evaluation of ligands/drugs on mutated tyrosinase structures not only advances our understanding of molecular variations but also highlights promising pathways for targeted inhibitory interventions in the intricate network of melanin biosynthesis.

Unraveling the potential effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on the Protein structure and function of the human SLC30A8 gene on type 2 diabetes and colorectal cancer: An In silico approach

Background and aims

The single nucleotide polymorphisms (SNPs) in SLC30A8 gene have been recognized as contributing to type 2 diabetes (T2D) susceptibility and colorectal cancer. This study aims to predict the structural stability, and functional impacts on variations in non-synonymous SNPs (nsSNPs) in the human SLC30A8 gene using various computational techniques.

Materials and methods

Several in silico tools, including SIFT, Predict-SNP, SNPs&GO, MAPP, SNAP2, PhD-SNP, PANTHER, PolyPhen-1,PolyPhen-2, I-Mutant 2.0, and MUpro, have been used in our study.

Results

After data analysis, out of 336 missenses, the eight nsSNPs, namely R138Q, I141N, W136G, I349N, L303R, E140A, W306C, and L308Q, were discovered by ConSurf to be in highly conserved regions, which could affect the stability of their proteins. Project HOPE determines any significant molecular effects on the structure and function of eight mutated proteins and the three-dimensional (3D) structures of these proteins. The two pharmacologically significant compounds, Luzonoid B and Roseoside demonstrate strong binding affinity to the mutant proteins, and they are more efficient in inhibiting them than the typical SLC30A8 protein using Autodock Vina and Chimera. Increased binding affinity to mutant SLC30A8 proteins has been determined not to influence drug resistance. Ultimately, the Kaplan-Meier plotter study revealed that alterations in SLC30A8 gene expression notably affect the survival rates of patients with various cancer types.

Conclusion

Finally, the study found eight highly deleterious missense nsSNPs in the SLC30A8 gene that can be helpful for further proteomic and genomic studies for T2D and colorectal cancer diagnosis. These findings also pave the way for personalized treatments using biomarkers and more effective healthcare strategies.

Computational exploration of SLC14A1 genetic variants through structure modeling, protein-ligand docking, and molecular dynamics simulation

The urea transporter UT-B1, encoded by the SLC14A1 gene, has been hypothesized to be a significant protein whose deficiency and dysfunction contribute to the pathogenesis of bladder cancer and many other diseases. Several studies reported the association of genetic alterations in the SLC14A1 (UT-B1) gene with bladder carcinogenesis, suggesting a need for thorough characterization of the UT-B1 protein's coding and non-coding variants. This study used various computational techniques to investigate the commonly occurring germ-line missense and non-coding SNPs (ncSNPs) of the SLC14A1 gene (UT-B1) for their structural, functional, and molecular implications for disease susceptibility and dysfunctionality. SLC14A1 missense variants, primarily identified from the ENSEMBL genome browser, were screened through twelve functionality prediction tools leading to two variants D280Y (predicted detrimental by maximum tools) and D280N (high global MAF) for rs1058396. Subsequently, the ConSurf and NetSurf tools revealed the D280 residue to be in a variable site and exposed on the protein surface. According to I-Mutant2.0 and MUpro, both variants are predicted to cause a significant effect on protein stability. Analysis of molecular docking anticipated these two variants to decrease the binding affinity of UT-B1 protein for the examined ligands to a significant extent. Molecular dynamics also disclosed the possible destabilization of the UT-B1 protein due to single nucleotide polymorphism compared to wild-type protein which may result in impaired protein function. Furthermore, several non-coding SNPs were estimated to affect transcription factor binding and regulation of SLC14A1 gene expression. Additionally, two ncSNPs were found to affect miRNA-based post-transcriptional regulation by creating new seed regions for miRNA binding. This comprehensive in-silico study of SLC14A1 gene variants may serve as a springboard for future large-scale investigations examining SLC14A1 polymorphisms.

In silico energetic and molecular dynamic simulations studies demonstrate potential effect of the point mutations with implications for protein engineering in BDNF

Protein engineering by directed evolution is time-consuming. Hence, in silico techniques like FoldX-Yasara for ∆∆G calculation, and SNPeffect for predicting propensity for aggregation, amyloid formation, and chaperone binding are employed to design proteins. Here, we used in silico techniques to engineer BDNF-NTF3 interaction and validated it using mutations with known functional implications for NGF dimer. The structures of three mutants representing a positive, negative, or neutral ∆∆G involving two interface residues in BDNF and two mutations representing a neutral and positive ∆∆G in NGF, which is aligned with BDNF, were selected for molecular dynamics (MD) simulation. Our MD results conclude that the secondary structure of individual protomers of the positive and negative mutants displayed a similar or different conformation from the NTF3 monomer, respectively. The positive mutants showed fewer hydrophobic interactions and higher hydrogen bonds compared to the wild-type, negative, and neutral mutants with similar SASA, suggesting solvent-mediated disruption of hydrogen-bonded interactions. Similar results were obtained for mutations with known functional implications for NGF and BDNF. The results suggest that mutations with known effects in homologous proteins could help in validation, and in silico directed evolution experiments could be a viable alternative to the experimental technique used for protein engineering.

Phylogeny and evolution of SARS-CoV-2 during Delta and Omicron variant waves in India

SARS-CoV-2 evolution has continued to generate variants, responsible for new pandemic waves locally and globally. Varying disease presentation and severity has been ascribed to inherent variant characteristics and vaccine immunity. This study analyzed genomic data from 305 whole genome sequences from SARS-CoV-2 patients before and through the third wave in India. Delta variant was reported in patients without comorbidity (97%), while Omicron BA.2 was reported in patients with comorbidity (77%). Tissue adaptation studies brought forth higher propensity of Omicron variants to bronchial tissue than lung, contrary to observation in Delta variants from Delhi. Study of codon usage pattern distinguished the prevalent variants, clustering them separately, Omicron BA.2 isolated in February grouped away from December strains, and all BA.2 after December acquired a new mutation S959P in ORF1b (44.3% of BA.2 in the study) indicating ongoing evolution. Loss of critical spike mutations in Omicron BA.2 and gain of immune evasion mutations including G142D, reported in Delta but absent in BA.1, and S371F instead of S371L in BA.1 could explain very brief period of BA.1 in December 2021, followed by complete replacement by BA.2. Higher propensity of Omicron variants to bronchial tissue, probably ensured increased transmission while Omicron BA.2 became the prevalent variant possibly due to evolutionary trade-off. Virus evolution continues to shape the epidemic and its culmination.Communicated by Ramaswamy H. Sarma.

A computational approach for structural and functional analyses of disease-associated mutations in the human CYLD gene

Tumor suppressor cylindromatosis protein (CYLD) regulates NF-κB and JNK signaling pathways by cleaving K63-linked poly-ubiquitin chain from its substrate molecules and thus preventing the progression of tumorigenesis and metastasis of the cancer cells. Mutations in CYLD can cause aberrant structure and abnormal functionality leading to tumor formation. In this study, we utilized several computational tools such as PANTHER, PROVEAN, PredictSNP, PolyPhen-2, PhD-SNP, PON-P2, and SIFT to find out deleterious nsSNPs. We also highlighted the damaging impact of those deleterious nsSNPs on the structure and function of the CYLD utilizing ConSurf, I-Mutant, SDM, Phyre2, HOPE, Swiss-PdbViewer, and Mutation 3D. We shortlisted 18 high-risk nsSNPs from a total of 446 nsSNPs recorded in the NCBI database. Based on the conservation profile, stability status, and structural impact analysis, we finalized 13 nsSNPs. Molecular docking analysis and molecular dynamic simulation concluded the study with the findings of two significant nsSNPs (R830K, H827R) which have a remarkable impact on binding affinity, RMSD, RMSF, radius of gyration, and hydrogen bond formation during CYLD-ubiquitin interaction. The principal component analysis compared native and two mutants R830K and H827R of CYLD that signify structural and energy profile fluctuations during molecular dynamic (MD) simulation. Finally, the protein-protein interaction network showed CYLD interacts with 20 proteins involved in several biological pathways that mutations can impair. Considering all these in silico analyses, our study recommended conducting large-scale association studies of nsSNPs of CYLD with cancer as well as designing precise medications against diseases associated with these polymorphisms.

Exploring the influence of pressure-induced semiconductor-to-metal transition on the physical properties of cubic perovskites FrXCl3 (X = Ge and Sn)

Even though lead halide perovskites have outstanding physiochemical properties and improved power conversion efficiency, most of these compounds threaten their future commercialization because of their instability and highly toxic nature. Thus, it is preferable to use stable alternative elements rather than lead to make environmentally friendly perovskite material that will have comparable optical and electronic properties to those constructed from Pb-based perovskites. However, devices constructed from lead-free perovskites typically display a lower power conversion efficiency. Applying hydrostatic pressure could be deemed an effective method to alter the physical properties of these compounds. This not only improves their performance in application but also reveals significant correlations between structure and properties. This work uses DFT to investigate the structural, electronic, optical, and elastic properties of non-toxic, francium-based halide perovskites FrXCl3 (X = Ge, Sn) at different levels of hydrostatic pressures that vary from 0 to 10 GPa. The estimated structural parameter's strong correlation with the data from earlier studies ensures the accuracy of the current findings. Pressure causes the Fr-Cl and Ge (Sn)-Cl bonds to shorten and become stronger. The electronic property calculations demonstrated that both compounds are direct band-gap semiconductors. The application of pressure leads to a linear reduction in the band gap (semiconducting to metallic state) and raises the electronic density of states around the Fermi level by forcing the valence band electrons upward, indicating that the optoelectronic device's performance can be tuned and improved. The values of the dielectric constant, absorptivity and reflectivity showed an increasing tendency with pressure. As the pressure applied to the compounds increases, the absorption spectra show a redshift. These findings suggested that the FrXCl3 (X = Ge and Sn) compound becomes more appropriate for usage in optoelectronic applications under pressure. Furthermore, our examination of the mechanical properties indicates that both FrGeCl3 and FrSnCl3 exhibit mechanically stability, and ductility. Interestingly, we observe an increase in ductility as pressure levels rise.

Computational Analysis of Single Nucleotide Polymorphisms in Human HIC1 Gene

Background A putative tumor suppressor gene called HIC1 (hypermethylated in cancer) is situated at 17p13.3, a locus where the allelic loss occurs often in human malignancies, including breast cancer. Hypermethylated in cancer 1 protein is a protein that in humans is encoded by the HIC1 gene and it's a Homo sapiens (Human). This gene functions as a growth regulatory and tumor repressor gene. The molecular function of HIC1 gene includes DNA-binding transcription factor activity, sequence-specific DNA binding, DNA binding, histone deacetylase binding, protein binding, metal ion binding, nucleic acid binding, DNA-binding transcription repressor activity, RNA polymerase II-specific, DNA-binding transcription factor activity, RNA polymerase II-specific. The biological process of HIC1 gene includes multicellular organism development, negative regulation of Wnt signaling pathway, positive regulation of DNA damage response, signal transduction by p53 class mediator regulation of transcription, DNA-templated, negative regulation of transcription by RNA polymerase II, Wnt signaling pathway, transcription, DNA-templated, intrinsic apoptotic signaling pathway in response to DNA damage, cellular response to DNA damage stimulus. The study aimed to predict the stability and structure of the protein that will arise from single nucleotide polymorphisms (SNPs) in the human HIC1 gene. Methodology To investigate the possible negative effects associated with these SNPs, bioinformatic analysis is typically essential. The following tools were employed for forecasting harmful SNPs: scale-invariant feature transform (SIFT), Protein Analysis Through Evolutionary Relationships (PANTHER), nonsynonymous SNP by Protein Variation Effect Analyzer (PROVEAN), and nonsynonymous SNP by Single Nucleotide Polymorphism Annotation Platform (SNAP). Results The present study identified a total of 36 SNPs using the SIFT approach, which were shown to have functional significance. Twenty-six were determined to be tolerable, whereas 10 were shown to be detrimental. Out of 20 SNPs, seven (P370A, P646S, R654P, A476T, S400S, D666N, D7V) SNPs were predicted as "Possibly damaging" and seven (L9F, G468R, G490R, L482R, S12W, G489D, S12P) were identified as "probably benign", and six (R725G, G620S, A56V, E463D, D394N, L338V) were identified as "probably damaging" according to the predictions made by PANTHER tools. The majority of the pixels on the strip were red, indicating that the gene changes may have dangerous consequences. These results highlight the need for more research to fully comprehend how these mutations affect the hic1 protein's function, which is essential for the emergence of different types of cancer. Conclusion The current research has provided us with essential information about how SNPs might be used as a diagnostic marker for cancer, given that SNPs may be candidates for cellular changes caused by mutations linked to cancer.

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.

In Silico Prediction and Molecular Docking of SNPs in NRP1 Gene Associated with SARS-COV-2

Neuropilin-1 (NRP1) which is a main transmembrane cell surface receptor acts as a host cell mediator resulting in increasing the SARS-Cov-2 infectivity and also plays a role in neuronal development, angiogenesis and axonal outgrowth. The goal of this study is to estimate the impact of single nucleotide polymorphisms (SNPs) in the NRP1 gene on the function, structure and stabilization of protein as well as on the miRNA-mRNA binding regions using bioinformatical tools. It is also aimed to investigate the changes caused by SNPs in NRP1 on interactions with drug molecule and spike protein. The missense type of SNPs was analyzed using SIFT, PolyPhen-2, SNAP2, PROVEAN, Mutation Assessor, SNPs&GO, PhD-SNP, I-Mutant 3.0, MUpro, STRING, Project HOPE, ConSurf, and PolymiRTS. Docking analyses were conducted by AutoDock Vina program. As a result, a total of 733 missense SNPs were determined within the NRP1 gene and nine SNPs were specified as damaging to the protein. The modelling results showed that wild and mutant type amino acids had some different properties such as size, charge, and hydrophobicity. Additionally, their three-dimensional structures of protein were utilized for confirmation of these differences. After evaluating the results, nine polymorphisms rs141633354, rs142121081, rs145954532, rs200028992, rs200660300, rs369312020, rs370117610, rs370551432, rs370641686 were determined to be damaging on the structure and function of NRP1 protein and located in conserved regions. The results of molecular docking showed that the binding affinity values are nearly the same for wild-type and mutant structures support that the mutations carried out are not in the focus of the binding site, therefore the ligand does not affect the binding energy. It is expected that the results will be useful for future studies.

GLP1R (glucagon-like-peptide-1 incretin receptor), diabetes and obesity phenotypes: An in silico approach revealed new pathogenic variants

OBJECTIVE

Glucagon-like peptide-1 receptor belongs to the B family of G protein-coupled receptors, serving as a binding protein in membranes and is widely expressed in human tissues. Upon stimulation by its agonist, the glucagon-like peptide-1, the receptor plays a role in glucose metabolism, enhancing insulin secretion, and regulating appetite in the hypothalamus. Mutations in the glucagon-like peptide-1 receptor gene can lead to physiological changes that may explain phenotypic variations in individuals with obesity and diabetes. Therefore, this study aimed to evaluate missense variants of the glucagon-like peptide-1 receptor gene.

METHODS

Data mining was performed on the single nucleotide polymorphism database, retrieving a total of 16,399 variants. Among them, 356 were missense. These 356 variants were analyzed using the PolyPhen-2 and filtered based on allele frequency, resulting in 6 pathogenic variants.

RESULTS

D344E, A239T, R310Q, R227H, R421P, and R176G were analyzed using four different prediction tools. The D344E and A239T resulted in larger amino acid residues compared to their wild-type counterparts. The D344E showed a slightly destabilized structure, while A239T affected the transmembrane helices. Conversely, the R310Q, R227H, R421P, and R176G resulted in smaller amino acid residues than the wild-type, leading to a loss of positive charge and increased hydrophobicity. Particularly, the R421P, due to the presence of proline, significantly destabilized the α-helix structure and caused severe damage to the receptor.

CONCLUSION

Elucidating the glucagon-like peptide-1 receptor variants and their potentially detrimental effects on receptor functionality can contribute to an understanding of metabolic diseases and the response to available pharmacological treatments.

Elucidating the mutational impact in causing Niemann-Pick disease type C: an in silico approach

Niemann-Pick disease type C is a rare autosomal recessive of lysosomal storage disorder characterized by impaired intracellular lipid transport and has a tendency to accumulate the fatty acids and glycosphingolipids in a variety of neurovisceral tissues. This work includes computational tools to deciphere the mutational effect in NPC protein. The study initiated with the collection of 471 missense mutations from various databases, which were then analyzed using computational tools. The mutations (G549V, F703S, Q775P and L1244P) were said to be disease associated, altering the biophysical properties, in highly conserved regions and reduces the stability using several in silico methods and were subjected to molecular docking analysis. To analyze the ligand (Itraconazole: a small molecule of antifungal drug class, which is known to inhibit cholesterol export from lysosomes) activity Molecular docking study was performed for all the complex proteins. The average binding affinity was taken and found to be -10.76 kcal/mol (native) and -11.06 kcal/mol (Q775P was located in transmembrane region IV which impacts the sterol-sensing domain of the NPC1 protein and associated with a severe infantile neurological form). Finally, molecular dynamic simulation was performed in duplicate and trajectories were built for the backbone of the RMSD, RMSF, the number of intramolecular hydrogen bonds, the radius of gyration and the SSE percent for both the complex proteins. This work contributes to understand the effectiveness and may provide an insight on the stability of the drug with the complex variant structures.Communicated by Ramaswamy H. Sarma.

Identification of the effects of pathogenic genetic variations of human CYP2C9 and CYP2D6: an in silico approach

Genetic variations in the human cytochrome P450 family 2 subfamily C member 9 (CYP2C9) and cytochrome P450 family 2 subfamily D member 6 (CYP2D6) genes may affect drug metabolism and lead to alterations in phenotypes. Genetic variations are associated with toxicity, adverse drug reactions, inefficient treatment. Various in silico tools were combined to investigate the deleterious effects of missense non-synonymous single nucleotide polymorphisms (nsSNPs) of the human CYP2C9 and CYP2D6. The structural and functional effects of the high-risk non-synonymous SNPs in the human CYP2C9 and CYP2D6 were predicted by numerous computational mutation analysis methods. Out of 24 pathogenic missense SNPs in the CYP2C9, 22 nsSNPs had a decreasing effect on protein stability and 13 SNPs were showed to be located at conserved positions. Out of 27 high-risk deleterious non-synonymous SNPs in the human CYP2D6, 21 SNPs decreased protein stability and 16 nsSNPs were predicted to be positioned at conserved regions. Our present study suggests that the identified functional SNPs may affect drug metabolism associated with CYP2C9 and CYP2D6 enzymes.

Comprehensive analysis predicting effects of deleterious SNPs of human progesterone receptor gene on its structure and functions: a computational approach

Progesterone receptor plays a crucial role in the development of the mammary gland and breast cancer. Single nucleotide polymorphisms (SNPs) within its gene, PGR, are associated with the risk of miscarriages and preterm birth as well as many cancers across different populations. The main aim of this work is to investigate the most deleterious SNPs in the PGR gene to identify potential biomarkers for various disease susceptibility and treatments. Both sequence and structure-based computational approaches were adopted and in total 11 nsSNPs have been filtered out of 674 nsSNPs along with seven non-coding SNPs. R740Q, I744T and D746E belonged to a mutation cluster. R740Q, D746E along with S865L altered H-bond interactions within the receptor. The same mutations have been found to be associated with several cancers including uterine and breast cancer among others. It is, therefore, possible that the high-risk SNPs associated with cancers may exert their effect by causing changes in the protein structure, particularly in its bonding patterns, and thus affecting its function. In addition, seven non-coding SNPs that were located in the UTR region created a new miRNA site while three SNPs disrupted a conserved miRNA site. These high-risk SNPs can play an instrumental role in generating a dataset of the PGR gene's SNPs. Thus, the present study may pave the way to design and develop novel therapeutics for overcoming the challenges associated with certain cancers and pregnancy that result from a change in the protein structure and function due to the SNP mutations in the PGR gene.Communicated by Ramaswamy H. Sarma.

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

BACKGROUND

AKT1 is a serine/threonine kinase necessary for the mediation of apoptosis, angiogenesis, metabolism, and cell proliferation in both normal and cancerous cells. The mutations in the AKT1 gene have been associated with different types of cancer. Further, the AKT1 gene mutations are also reported to be associated with other diseases such as Proteus syndrome and Cowden syndromes. Hence, this study aims to identify the deleterious AKT1 missense SNPs and predict their effect on the function and structure of the AKT1 protein using various computational tools.

METHODS

Extensive in silico approaches were applied to identify deleterious SNPs of the human AKT1 gene and assessment of their impact on the function and structure of the AKT1 protein. The association of these highly deleterious missense SNPs with different forms of cancers was also analyzed. The in silico approach can help in reducing the cost and time required to identify SNPs associated with diseases.

RESULTS

In this study, 12 highly deleterious SNPs were identified which could affect the structure and function of the AKT1 protein. Out of the 12, four SNPs-namely, G157R, G159V, G336D, and H265Y-were predicted to be located at highly conserved residues. G157R could affect the ligand binding to the AKT1 protein. Another highly deleterious SNP, R273Q, was predicted to be associated with liver cancer.

CONCLUSIONS

This study can be useful for pharmacogenomics, molecular diagnosis of diseases, and developing inhibitors of the AKT1 oncogene.

Detection of Nonsynonymous Single Variants in Human HLA-DRB1 Exon 2 Associated with Renal Transplant Rejection

Background: HLA-DRB1 is the most polymorphic gene in the human leukocyte antigen (HLA) class II, and exon 2 is critical because it encodes antigen-binding sites. This study aimed to detect functional or marker genetic variants of HLA-DRB1 exon 2 in renal transplant recipients (acceptance and rejection) using Sanger sequencing. Methods: This hospital-based case-control study collected samples from two hospitals over seven months. The 60 participants were equally divided into three groups: rejection, acceptance, and control. The target regions were amplified and sequenced by PCR and Sanger sequencing. Several bioinformatics tools have been used to assess the impact of non-synonymous single-nucleotide variants (nsSNVs) on protein function and structure. The sequences data that support the findings of this study with accession numbers (OQ747803-OQ747862) are available in National Center for Biotechnology Information (GenBank database). Results: Seven SNVs were identified, two of which were novel (chr6(GRCh38.p12): 32584356C>A (K41N) and 32584113C>A (R122R)). Three of the seven SNVs were non-synonymous and found in the rejection group (chr6(GRCh38.p12): 32584356C>A (K41N), 32584304A>G (Y59H), and 32584152T>A (R109S)). The nsSNVs had varying effects on protein function, structure, and physicochemical parameters and could play a role in renal transplant rejection. The chr6(GRCh38.p12):32584152T>A variant showed the greatest impact. This is because of its conserved nature, main domain location, and pathogenic effects on protein structure, function, and stability. Finally, no significant markers were identified in the acceptance samples. Conclusion: Pathogenic variants can affect intramolecular/intermolecular interactions of amino acid residues, protein function/structure, and disease risk. HLA typing based on functional SNVs could be a comprehensive, accurate, and low-cost method for covering all HLA genes while shedding light on previously unknown causes in many graft rejection cases.

Evaluation of Pathogenicity and Structural Alterations for the Mutations Identified in the Conserved Region of the C-Terminal Kinase Domain of Human-Ribosomal S6 Kinase 1

Human-ribosomal s6 kinase 1 (h-RSK1) is an effector kinase of the Ras/MAPK signaling pathway, which is involved in the regulation of the cell cycle, proliferation, and survival. RSKs comprise two functionally distinct kinase domains at the N-terminal (NTKD) and C-terminal (CTKD) separated by a linker region. The mutations in RSK1 may have the potential to provide an extra benefit to the cancer cell to proliferate, migrate, and survive. The present study focuses on evaluating the structural basis for the missense mutations identified at the C-terminal kinase domain of human-RSK1. A total of 139 mutations reported on RSK1 were retrieved from cBioPortal, where 62 were located at the CTKD region. Furthermore, 10 missense mutations Arg434Pro, Thr701Met, Ala704Thr, Arg725Trp, Arg726Gln, His533Asn, Pro613Leu, Ser720Cys, Arg725Gln, and Ser732Phe were predicted to be deleterious using in silico tools. To our observation, these mutations are located in the evolutionarily conserved region of RSK1 and shown to alter the inter- and intramolecular interactions and also the conformational stability of RSK1-CTKD. The molecular dynamics (MD) simulation study further revealed that the five mutations Arg434Pro, Thr701Met, Ala704Thr, Arg725Trp, and Arg726Gln showed maximum structural alterations in RSK1-CTKD. Thus, based on the in silico and MD simulation analysis, it can be concluded that the reported mutations may serve as potential candidates for further functional studies.

Evaluation of SNPs from human IGFBP6 associated with gene expression: an in-silico study

In the bloodstream and in local tissues, most IGF molecules are associated with the one of the members of the IGF-binding protein (IGFBP) family, which are divided into six distinct types. IGF-binding proteins have been demonstrated to either decrease or increase the growth-promoting effects of IGFs on cell culture, by extending their half-life. They alter how IGFs interact with the receptors on their cell surfaces. IGFBP6 gene is associated with disease in-situ carcinoma. Upregulation or downregulation of IGFBP6 gene has been implicated in different types of cancer in humans. Nonsynonymous SNPs changes have the potential to affect the protein's structure and function. Potential functional SNPs can be assessed before undertaking studies in larger populations because validation of these functional SNPs can be a crucial problem. So, in this in-silico investigation, we used a variety of sequence- and structure-based bioinformatics methods to separate the potential nsSNPs of the IGFBP6 gene from the neutral ones. In total of 216 nsSNPs, 5 were found to have potential effects using 5 prediction tools. From which, 2 nsSNPs (R128G and R164H) were selected as potentially damaging due to their presence in highly conserved region and ability to decrease protein stability. Among these 2 nsSNPs, only R164H was found to be associated with Uterine corpus endometrial carcinoma. It was also found that both, upregulation or downregulation of IGFBP6 gene can lead to the different types of cancers. The findings of the present study will certainly be valuable in the future large population-based investigations as well as drug discovery, especially developing personalized medicine.Communicated by Ramaswamy H. Sarma.

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.

Deleterious single nucleotide polymorphisms (SNPs) of human IFNAR2 gene facilitate COVID-19 severity in patients: a comprehensive in silico approach

In humans, the dimeric receptor complex IFNAR2-IFNAR1 accelerates cellular response triggered by type I interferon (IFN) family proteins in response to viral infection including Coronavirus infection. Studies have revealed the association of the IFNAR2 gene with severe illness in Coronavirus infection and indicated the association of genomic variants, i.e. single nucleotide polymorphisms (SNPs). However, comprehensive analysis of SNPs of the IFNAR2 gene has not been performed in both coding and non-coding region to find the causes of loss of function of IFNAR2 in COVID-19 patients. In this study, we have characterized coding SNPs (nsSNPs) of IFNAR2 gene using different bioinformatics tools and identified deleterious SNPs. We found 9 nsSNPs as pathogenic and disease-causing along with a decrease in protein stability. We employed molecular docking analysis that showed 5 nsSNPs to decrease binding affinity to IFN. Later, MD simulations showed that P136R mutant may destabilize crucial binding with the IFN molecule in response to COVID-19. Thus, P136R is likely to have a high impact on disrupting the structure of the IFNAR2 protein. GTEx portal analysis predicted 14 sQTLs and 5 eQTLs SNPs in lung tissues hampering the post-transcriptional modification (splicing) and altering the expression of the IFNAR2 gene. sQTLs and eQTLs SNPs potentially explain the reduced IFNAR2 production leading to severe diseases. These mutants in the coding and non-coding region of the IFNAR2 gene can help to recognize severe illness due to COVID 19 and consequently assist to develop an effective drug against the infection.Communicated by Ramaswamy H. Sarma.

HBD-2 variants and SARS-CoV-2: New insights into inter-individual susceptibility

Background

A deep understanding of the causes of liability to SARS-CoV-2 is essential to develop new diagnostic tests and therapeutics against this serious virus in order to overcome this pandemic completely. In the light of the discovered role of antimicrobial peptides [such as human b-defensin-2 (hBD-2) and cathelicidin LL-37] in the defense against SARS-CoV-2, it became important to identify the damaging missense mutations in the genes of these molecules and study their role in the pathogenesis of COVID-19.

Methods

We conducted a comprehensive analysis with multiple in silico approaches to identify the damaging missense SNPs for hBD-2 and LL-37; moreover, we applied docking methods and molecular dynamics analysis to study the impact of the filtered mutations.

Results

The comprehensive analysis reveals the presence of three damaging SNPs in hBD-2; these SNPs were predicted to decrease the stability of hBD-2 with a damaging impact on hBD-2 structure as well. G51D and C53G mutations were located in highly conserved positions and were associated with differences in the secondary structures of hBD-2. Docking-coupled molecular dynamics simulation analysis revealed compromised binding affinity for hBD-2 SNPs towards the SARS-CoV-2 spike domain. Different protein-protein binding profiles for hBD-2 SNPs, in relation to their native form, were guided through residue-wise levels and differential adopted conformation/orientation.

Conclusions

The presented model paves the way for identifying patients prone to COVID-19 in a way that would guide the personalization of both the diagnostic and management protocols for this serious disease.

In Silico Evaluation of Nonsynonymous SNPs in Human ADAM33: The Most Common Form of Genetic Association to Asthma Susceptibility

ADAM33 is a zinc-dependent metalloprotease of the ADAM family, which plays a vital biological role as an activator of Th2 cytokines and growth factors. Moreover, this protein is crucial for the normal development of the lung in the fetus two months after gestation leading to determining lung functions all over life. In this regard, mutations in ADAM33 have been linked with asthma risk factors. Consequently, identifying ADAM33 pathogenic nonsynonymous single-nucleotide polymorphisms (nsSNPs) can be very important in asthma treatment. In the present study, 1055 nsSNPs of human ADAM33 were analyzed using biocomputational software, 31 of which were found to be detrimental mutations. Precise structural and stability analysis revealed D219V, C669G, and C606S as the most destabilizing SNPs. Furthermore, MD simulations disclosed higher overall fluctuation and alteration in intramolecular interactions compared with the wild-type structure. Overall, the results suggest D219V, C669G, and C606S detrimental mutations as a starting point for further case-control studies on the ADAM33 protein as well as an essential source for future targeted mechanisms.

A bioinformatics approach to the identification of novel deleterious mutations of human TPMT through validated screening and molecular dynamics

Polymorphisms of Thiopurine S-methyltransferase (TPMT) are known to be associated with leukemia, inflammatory bowel diseases, and more. The objective of the present study was to identify novel deleterious missense SNPs of TPMT through a comprehensive in silico protocol. The initial SNP screening protocol used to identify deleterious SNPs from the pool of all TPMT SNPs in the dbSNP database yielded an accuracy of 83.33% in identifying extremely dangerous variants. Five novel deleterious missense SNPs (W33G, W78R, V89E, W150G, and L182P) of TPMT were identified through the aforementioned screening protocol. These 5 SNPs were then subjected to conservation analysis, interaction analysis, oncogenic and phenotypic analysis, structural analysis, PTM analysis, and molecular dynamics simulations (MDS) analysis to further assess and analyze their deleterious nature. Oncogenic analysis revealed that all five SNPs are oncogenic. MDS analysis revealed that all SNPs are deleterious due to the alterations they cause in the binding energy of the wild-type protein. Plasticity-induced instability caused by most of the mutations as indicated by the MDS results has been hypothesized to be the reason for this alteration. While in vivo or in vitro protocols are more conclusive, they are often more challenging and expensive. Hence, future research endeavors targeted at TPMT polymorphisms and/or their consequences in relevant disease progressions or treatments, through in vitro or in vivo means can give a higher priority to these SNPs rather than considering the massive pool of all SNPs of TPMT.

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

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

ADGRL3 genomic variation implicated in neurogenesis and ADHD links functional effects to the incretin polypeptide GIP

Attention deficit/hyperactivity disorder (ADHD) is the most common childhood neurodevelopmental disorder. Single nucleotide polymorphisms (SNPs) in the Adhesion G Protein-Coupled Receptor L3 (ADGRL3) gene are associated with increased susceptibility to developing ADHD worldwide. However, the effect of ADGRL3 non-synonymous SNPs (nsSNPs) on the ADGRL3 protein function is vastly unknown. Using several bioinformatics tools to evaluate the impact of mutations, we found that nsSNPs rs35106420, rs61747658, and rs734644, previously reported to be associated and in linkage with ADHD in disparate populations from the world over, are predicted as pathogenic variants. Docking analysis of rs35106420, harbored in the ADGLR3-hormone receptor domain (HRM, a common extracellular domain of the secretin-like GPCRs family), showed that HRM interacts with the Glucose-dependent insulinotropic polypeptide (GIP), part of the incretin hormones family. GIP has been linked to the pathogenesis of diabetes mellitus, and our analyses suggest a potential link to ADHD. Overall, the comprehensive application of bioinformatics tools showed that functional mutations in the ADGLR3 gene disrupt the standard and wild ADGRL3 structure, most likely affecting its metabolic regulation. Further in vitro experiments are granted to evaluate these in silico predictions of the ADGRL3-GIP interaction and dissect the complexity underlying the development of ADHD.

Investigation of TLR2 and TLR4 Polymorphisms and Sepsis Susceptibility: Computational and Experimental Approaches

Toll-like receptors (TLR) play an eminent role in the regulation of immune responses to invading pathogens during sepsis. TLR genetic variants might influence individual susceptibility to developing sepsis. The current study aimed to investigate the association of genetic polymorphisms of the TLR2 and TLR4 with the risk of developing sepsis with both a pilot study and in silico tools. Different in silico tools were used to predict the impact of our SNPs on protein structure, stability, and function. Furthermore, in our prospective study, all patients matching the inclusion criteria in the intensive care units (ICU) were included and followed up, and DNA samples were genotyped using real-time polymerase chain reaction (RT-PCR) technology. There was a significant association between TLR2 Arg753Gln polymorphisms and sepsis under the over-dominant model (p = 0.043). In contrast, we did not find a significant difference with the TLR4 Asp299Gly polymorphism with sepsis. However, there was a significant association between TLR4 Asp299Gly polymorphisms and Acinetobacter baumannii infection which is quite a virulent organism in ICU (p = 0.001) and post-surgical cohorts (p = 0.033). Our results conclude that the TLR2 genotype may be a risk factor for sepsis in adult patients.

A comparative study of the mechanical stability, electronic, optical and photocatalytic properties of CsPbX3 (X = Cl, Br, I) by DFT calculations for optoelectronic applications

Organic free Cs-based perovskite materials are potential candidates for electronic and optoelectronic applications. A systematic comparative study of the mechanical, electronic, optical, and photocatalytic properties of CsPbX3 (X = Cl, Br, I) was conducted using density functional theory to compare the applicability of these materials in optoelectronic, photocatalytic, and photovoltaic (PV) devices. We calculated structural and elastic properties to determine the better agreement of damage-tolerance and electronic and optical responses for suitable device applications. Optimized lattice parameters and elastic constants showed excellent agreement with the experimental data whereas some properties were found to be much better than other theoretical reports. CsPbBr3 is thermodynamically more stable and more ductile compared to the other two perovskites. The hydrostatic pressure dependent mechanical stability showed that CsPbCl3 and CsPbBr3 sustained stability under low applied pressure, whereas the stability of CsPbI3 was very high. The electronic band gap calculations showed that CsPbCl3, CsPbBr3, and CsPbI3 are suitable for green, orange, and red emissions of optical spectra owing to the proper electronic band gaps. CsPbI3 can be shown as the best photocatalyst for the hydrogen evolution reaction and CsPbBr3 is the most stable photocatalyst due to its nearly balanced oxidation and reduction potentials, but CaPbCl3 is better for O2 production. The density of states and other optical properties have been reported in this study. Thus, our findings would be beneficial for experimental studies and can open a new window for efficient electronic, optoelectronic, and hydrogen production along with the biodegradation of polluted and waste materials.

Computational Analysis of Deleterious SNPs in NRAS to Assess Their Potential Correlation With Carcinogenesis

The NRAS gene is a well-known oncogene that acts as a major player in carcinogenesis. Mutations in the NRAS gene have been linked to multiple types of human tumors. Therefore, the identification of the most deleterious single nucleotide polymorphisms (SNPs) in the NRAS gene is necessary to understand the key factors of tumor pathogenesis and therapy. We aimed to retrieve NRAS missense SNPs and analyze them comprehensively using sequence and structure approaches to determine the most deleterious SNPs that could increase the risk of carcinogenesis. We also adopted structural biology methods and docking tools to investigate the behavior of the filtered SNPs. After retrieving missense SNPs and analyzing them using six in silico tools, 17 mutations were found to be the most deleterious mutations in NRAS. All SNPs except S145L were found to decrease NRAS stability, and all SNPs were found on highly conserved residues and important functional domains, except R164C. In addition, all mutations except G60E and S145L showed a higher binding affinity to GTP, implicating an increase in malignancy tendency. As a consequence, all other 14 mutations were expected to increase the risk of carcinogenesis, with 5 mutations (G13R, G13C, G13V, P34R, and V152F) expected to have the highest risk. Thermodynamic stability was ensured for these SNP models through molecular dynamics simulation based on trajectory analysis. Free binding affinity toward the natural substrate, GTP, was higher for these models as compared to the native NRAS protein. The Gly13 SNP proteins depict a differential conformational state that could favor nucleotide exchange and catalytic potentiality. A further application of experimental methods with all these 14 mutations could reveal new insights into the pathogenesis and management of different types of tumors.

Screening of Pathogenic Missense Single Nucleotide Variants From LHPP Gene Associated With the Hepatocellular Carcinoma: An In silico Approach

LHPP gene encodes a phospholysine phosphohistidine inorganic pyrophosphate phosphatase, which functions as a tumor-suppressor protein. The tumor suppression by this protein has been confirmed in various cancers, including hepatocellular carcinoma (HCC). LHPP downregulation promotes cell growth and proliferation by modulating the PI3K/AKT signaling pathway. This study identifies potentially deleterious missense single nucleotide variants (SNVs) associated with the LHPP gene using multiple computational tools based on different algorithms. A total of 4 destabilizing mutants are identified as L22P, I212T, G227R, and G236R, from the conserved region of the phosphatase. The 3-dimensional (3D) modeling and structural comparison of variants with the native protein reveals significant structural and conformational variations after mutations, suggesting disruption in the function of phospholysine phosphohistidine inorganic pyrophosphate phosphatase. The identified mutations might, therefore, participate in the cause of HCC.

Molecular Insights into the Role of Pathogenic nsSNPs in GRIN2B Gene Provoking Neurodevelopmental Disorders

The GluN2B subunit of N-methyl-D-aspartate receptors plays an important role in the physiology of different neurodevelopmental diseases. Genetic variations in the GluN2B coding gene (GRIN2B) have consistently been linked to West syndrome, intellectual impairment with focal epilepsy, developmental delay, macrocephaly, corticogenesis, brain plasticity, as well as infantile spasms and Lennox–Gastaut syndrome. It is unknown, however, how GRIN2B genetic variation impacts protein function. We determined the cumulative pathogenic impact of GRIN2B variations on healthy participants using a computational approach. We looked at all of the known mutations and calculated the impact of single nucleotide polymorphisms on GRIN2B, which encodes the GluN2B protein. The pathogenic effect, functional impact, conservation analysis, post-translation alterations, their driving residues, and dynamic behaviors of deleterious nsSNPs on protein models were then examined. Four polymorphisms were identified as phylogenetically conserved PTM drivers and were related to structural and functional impact: rs869312669 (p.Thr685Pro), rs387906636 (p.Arg682Cys), rs672601377 (p.Asn615Ile), and rs1131691702 (p.Ser526Pro). The combined impact of protein function is accounted for by the calculated stability, compactness, and total globularity score. GluN2B hydrogen occupancy was positively associated with protein stability, and solvent-accessible surface area was positively related to globularity. Furthermore, there was a link between GluN2B protein folding, movement, and function, indicating that both putative high and low local movements were linked to protein function. Multiple GRIN2B genetic variations are linked to gene expression, phylogenetic conservation, PTMs, and protein instability behavior in neurodevelopmental diseases. These findings suggest the relevance of GRIN2B genetic variations in neurodevelopmental problems.

Genetic determinants of lung cancer: Understanding the oncogenic potential of somatic missense mutations

BACKGROUND

Treatment of lung cancer is getting more personalized nowadays and medical practitioners are moving away from conventional histology-driven empirical treatments, platinum-based chemotherapy, and other invasive surgical resections and have started adopting alternate therapies in which therapeutic targets are patient's molecular oncogenic drivers.

AIM

The aim of the current study is to extract meaningful information from the online somatic mutation data (retrieved from cBioPortal) of 16 most significantly mutated oncogenes in non-small-cell lung cancer (NSCLC), namely EGFR, NRAS, KRAS, HER2 (ERBB2), RET, MET, ROS1, FGFR1, BRAF, AKT1, MEK1 (MAP2K1), PIK3CA, PTEN, DDR2, LKB1 (STK11) and ALK, for improving our understanding of the pathobiology of the lung cancer that can aid decision-making on critical clinical and therapeutic considerations.

METHODS

Using an integrated approach comprising 4 steps, the oncogenic potential of 661 missense non-synonymous single nucleotide polymorphisms (nsSNPs) in 16 genes was ascertained using 2059 NSCLC (1575 lung adenocarcinomas, 484 lung squamous cell carcinomas) patients' online mutation data. The steps used comprise sequence/structure homology-based prediction, scoring of conservation of mutated residues and positions, prediction of resulting molecular and functional consequences using machine-learning and structure-guided approach.

RESULTS

Out of a total of 661 nsSNPs analyzed, a set of 29 nsSNPs has been identified as conserved high confidence mutations in 10 of 16 genes relevant to the under study. Out of 29 conserved high confidence nsSNPs, 4 nsSNPs (EGFR N1094Y, BRAF M620I, DDR2 R307L, ALK P1350T) have been found to be putative novel rare genetic markers for NSCLC.

CONCLUSIONS

The current study, the first of its kind, has provided a list of deleterious non-synonymous somatic mutations in a selected pool of oncogenes that can be considered as a promising target for future drug design and therapy for patients with lung adenocarcinomas and squamous cell carcinomas.

Analysis of missense SNPs in the SLC47A1 and SLC47A2 genes affecting the pharmacokinetics of metformin: Computational approach

Background

Metformin as an anti-hyperglycaemic drug is commonly used for the treatment of type 2 diabetes mellitus (T2DM). The metformin response is variable due to the interindividual variation of pharmacokinetics which is based on strong genetic background. MATE1 and MATE2 proteins are significantly implicated in the pharmacokinetics of metformin. Missense SNPs with high risk of pathogenicity are expected to affect response to metformin via pharmacokinetics. Therefore, the aim of the current study is to determine the effects of missense SNPs in the SLC47A1 and SLC47A2 genes. The structural and functional consequences of all known SLC47A1 and SLC47A2 missense SNPs of the human MATE1 and MATE2 proteins were identified by various bioinformatics methods (SIFT, PhD-SNP, PolyPhen-2, PROVEAN, PMut, MUpro, I-Mutant 3.0, COACH, RaptorX Binding, ConSurf, STRING).

Results

The SLC47A1 variants P186T, L116P and the SLC47A2 variants I158N, L112P, V118G exhibited ΔΔG values less than − 1 kcal/mol, and these variants are considered to disrupt the structure and function of MATE1 and MATE2 proteins. SLC47A1 R118Q and SLC47A2 Y273C, V118G may significantly disturb protein function and transporting activities according to the analysis of ligand-binding regions.

Conclusion

It is suggested that high-risk deleterious missense SNPs may mediate the pharmacokinetics of metformin and may be associated with altered tissue distribution, renal clearance and metformin toxicity. We suppose that our results might serve as potential targets for the studies composed of the development of potential diagnostic and therapeutic strategies based on the relationship between mutations and metformin response.

Association of Cytotoxic T-Lymphocyte Antigen-4 Gene Polymorphism with Type 1 Diabetes Mellitus: In silico Analysis of Biological Features of CTLA-4 Protein on Ethiopian Population

BACKGROUND

T1DM is a chronic organ-specific T-cell-mediated autoimmune disease characterized by the selective destruction of β-cells in the islets of Langerhans, resulting in insulin deficiency and hyperglycemia. Genes for cytotoxic T lymphocyte-associated antigen 4 have been hypothesized as possible contender genes for T1DM vulnerability. However, it has not been studied in the Ethiopian population yet.

OBJECTIVE

The aim of the study was to investigate CTLA-4 exon 1 was linked to A49G polymorphism with T1DM and its biological features of CTLA-4 among T1DM patients, in Ethiopia.

METHODS

A case-control study was done from December 2019 to March 2020 on 210 study participants (105 T1DM patients and 105 healthy controls). Polymerase Chain Reaction amplification with forward and reverse primers was followed by restriction fragment length polymorphism and gel electrophoresis to determine gene polymorphism. Bioinformatics data of SNP was retrieved from National Centers for Biotechnology Information databases. The chi-square test and logistic regression were used. Statistical significance was defined as a P-value of less than 0.05.

RESULTS

The CTLA-4 (+A49G) gene polymorphism was observed on 56 (26.7%) study participants, 39 (18.57%) of T1DM patients, and 17 (0.08%) were controls. In T1DM and controls, the frequency of the A allele was 73.3% and 89.5%, while the G allele was 26.7% and 10.5%, respectively. The G allele was found to be associated with T1DM (OR=3.1; 95% CI, 1.82 -5.32; P=0.001). Statistical analysis revealed an association between the likelihood of T1DM and GG genotype of the CTLA-4 (+A49G) gene polymorphism (OR=3.11; 95% CI, 1.37-10.90; P=0.01). Further in silico analyzed the SNP to assess its biological features.

CONCLUSION

The study showed as CTLA-4 (+A49G) gene polymorphism is linked with T1DM in the Ethiopian population.

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

MYB proteins are highly conserved DNA-binding domains (DBD) and mutations in MYB oncoproteins have been reported to cause aberrant and augmented cancer progression. Identification of MYB molecular biomarkers predictive of cancer progression can be used for improving cancer management. To address this, a biomarker discovery pipeline was employed in investigating deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in predicting damaging and potential alterations on the properties of proteins. The nsSNP of the MYB family; MYB, MYBL1, and MYBL2 was extracted from the NCBI database. Five in silico tools (PROVEAN, SIFT, PolyPhen-2, SNPs&GO and PhD-SNP) were utilized to investigate the outcomes of nsSNPs. A total of 45 nsSNPs were predicted as high-risk and damaging, and were subjected to PMut and I-Mutant 2.0 for protein stability analysis. This resulted in 32 nsSNPs with decreased stability with a DDG score lower than - 0.5, indicating damaging effect. G111S, N183S, G122S, and S178C located within the helix-turn-helix (HTH) domain were predicted to be conserved, further posttranslational modifications and 3-D protein analysis indicated these nsSNPs to shift DNA-binding specificity of the protein thus altering the protein function. Findings from this study would help in the field of pharmacogenomic and cancer therapy towards better intervention and management of cancer.

Comprehensive Characterization of the Coding and Non-Coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools

Single nucleotide polymorphisms (SNPs) help to understand the phenotypic variations in humans. Genome-wide association studies (GWAS) have identified SNPs located in the tumor protein 63 (TP63) locus to be associated with the genetic susceptibility of cancers. However, there is a lack of in-depth characterization of the structural and functional impacts of the SNPs located at the TP63 gene. The current study was designed for the comprehensive characterization of the coding and non-coding SNPs in the human TP63 gene for their functional and structural significance. The functional and structural effects of the SNPs were investigated using a wide variety of computational tools and approaches, including molecular dynamics (MD) simulation. The deleterious impact of eight nonsynonymous SNPs (nsSNPs) affecting protein stability, structure, and functions was measured by using 13 bioinformatics tools. These eight nsSNPs are in highly conserved positions in protein and were predicted to decrease protein stability and have a deleterious impact on the TP63 protein function. Molecular docking analysis showed five nsSNPs to reduce the binding affinity of TP63 protein to DNA with significant results for three SNPs (R319H, G349E, and C347F). Further, MD simulations revealed the possible disruption of TP63 and DNA binding, hampering the essential protein function. PolymiRTS study found five non-coding SNPs in miRNA binding sites, and the GTEx portal recognized five eQTLs SNPs in single tissue of the lung, heart (LV), and cerebral hemisphere (brain). Characterized nsSNPs and non-coding SNPs will help researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability

Genetic polymorphisms in DNA damage repair and tumor suppressor genes have been associated with increasing the risk of several types of cancer. Analyses of putative functional single nucleotide polymorphisms (SNP) in such genes can greatly improve human health by guiding choice of therapeutics. In this study, we selected nine genes responsible for various cancer types for gene enrichment analysis and found that BRCA1, ATM, and TP53 were more enriched in connectivity. Therefore, we used different computational algorithms to classify the nonsynonymous SNPs which are deleterious to the structure and/or function of these three proteins. The present study showed that the major pathogenic variants (V1687G and V1736G of BRCA1, I2865T and V2906A of ATM, V216G and L194H of TP53) might have a greater impact on the destabilization of the proteins. To stabilize the high-risk SNPs, we performed mutation site-specific molecular docking analysis and validated using molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) studies. Additionally, SNPs of untranslated regions of these genes affecting miRNA binding were characterized. Hence, this study will assist in developing precision medicines for cancer types related to these polymorphisms.

Characterisation of SARS-CoV-2 clades based on signature SNPs unveils continuous evolution

Since the emergence of SARS-CoV-2 in Wuhan, China more than a year ago, it has spread across the world in a very short span of time. Although, different forms of vaccines are being rolled out for vaccination programs around the globe, the mutation of the virus is still a cause of concern among the research communities. Hence, it is important to study the constantly evolving virus and its strains in order to provide a much more stable form of cure. This fact motivated us to conduct this research where we have initially carried out multiple sequence alignment of 15359 and 3033 global dataset without Indian and the dataset of exclusive Indian SARS-CoV-2 genomes respectively, using MAFFT. Subsequently, phylogenetic analyses are performed using Nextstrain to identify virus clades. Consequently, the virus strains are found to be distributed among 5 major clades or clusters viz. 19A, 19B, 20A, 20B and 20C. Thereafter, mutation points as SNPs are identified in each clade. Henceforth, from each clade top 10 signature SNPs are identified based on their frequency i.e. number of occurrences in the virus genome. As a result, 50 such signature SNPs are individually identified for global dataset without Indian and dataset of exclusive Indian SARS-CoV-2 genomes respectively. Out of each 50 signature SNPs, 39 and 41 unique SNPs are identified among which 25 non-synonymous signature SNPs (out of 39) resulted in 30 amino acid changes in protein while 27 changes in amino acid are identified from 22 non-synonymous signature SNPs (out of 41). These 30 and 27 amino acid changes for the non-synonymous signature SNPs are visualised in their respective protein structure as well. Finally, in order to judge the characteristics of the identified clades, the non-synonymous signature SNPs are considered to evaluate the changes in proteins as biological functions with the sequences using PROVEAN and PolyPhen-2 while I-Mutant 2.0 is used to evaluate their structural stability. As a consequence, for global dataset without Indian sequences, G251V in ORF3a in clade 19A, F308Y and G196V in NSP4 and ORF3a in 19B are the unique amino acid changes which are responsible for defining each clade as they are all deleterious and unstable. Such changes which are common for both global dataset without Indian and dataset of exclusive Indian sequences are R203M in Nucleocapsid for 20B, T85I and Q57H in NSP2 and ORF3a respectively for 20C while for exclusive Indian sequences such unique changes are A97V in RdRp, G339S and G339C in NSP2 in 19A and Q57H in ORF3a in 20A.

Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays

Single-nucleotide polymorphisms (SNPs) are the most common genetic variations for various complex human diseases, including cancers. Genome-wide association studies (GWAS) have identified numerous SNPs that increase cancer risks, such as breast cancer, colorectal cancer, and leukemia. These SNPs were cataloged for scientific use. However, GWAS are often conducted on certain populations in which the Orang Asli and Malays were not included. Therefore, we have developed a bioinformatic pipeline to mine the whole-genome sequence databases of the Orang Asli and Malays to determine the presence of pathogenic SNPs that might increase the risks of cancers among them. Five different in silico tools, SIFT, PROVEAN, Poly-Phen-2, Condel, and PANTHER, were used to predict and assess the functional impacts of the SNPs. Out of the 80 cancer-related nsSNPs from the GWAS dataset, 52 nsSNPs were found among the Orang Asli and Malays. They were further analyzed using the bioinformatic pipeline to identify the pathogenic variants. Three nsSNPs; rs1126809 (TYR), rs10936600 (LRRC34), and rs757978 (FARP2), were found as the most damaging cancer pathogenic variants. These mutations alter the protein interface and change the allosteric sites of the respective proteins. As TYR, LRRC34, and FARP2 genes play important roles in numerous cellular processes such as cell proliferation, differentiation, growth, and cell survival; therefore, any impairment on the protein function could be involved in the development of cancer. rs1126809, rs10936600, and rs757978 are the important pathogenic variants that increase the risks of cancers among the Orang Asli and Malays. The roles and impacts of these variants in cancers will require further investigations using in vitro cancer models.

Structural and functional analysis of disease-associated mutations in GOT1 gene: An in silico study

Disease-associated single nucleotide polymorphisms (SNPs) alter the natural functioning and the structure of proteins. Glutamic-oxaloacetic transaminase 1 (GOT1) is a gene associated with multiple cancers and neurodegenerative diseases which codes for aspartate aminotransferase. The present study involved a comprehensive in-silico analysis of the disease-associated SNPs of human GOT1. Four highly deleterious nsSNPs (L36R, Y159C, W162C and L345P) were identified through SNP screening using several sequence-based and structure-based tools. Conservation analysis and oncogenic analysis showed that most of the nsSNPs are at highly conserved residues, oncogenic in nature and cancer drivers. Molecular dynamics simulations (MDS) analysis was performed to understand the dynamic behaviour of native and mutant proteins. PTM analysis revealed that the nsSNP Y159C is at a PTM site and will mostly affect phosphorylation at that site. Based on the overall analyses carried out in this study, L36R is the most deleterious mutation amongst the aforementioned deleterious mutations of GOT1.

Computational Insights into the Deleterious Impacts of Missense Variants on N-Acetyl-d-glucosamine Kinase Structure and Function

An enzyme of the mammalian amino-sugar metabolism pathway, N-acetylglucosamine kinase (NAGK), that synthesizes N-acetylglucosamine (GlcNAc)-6-phosphate, is reported to promote dynein functions during mitosis, axonal and dendritic growth, cell migration, and selective autophagy, which all are unrelated to its enzyme activity. As non-enzymatic structural functions can be altered by genetic variation, we made an effort in this study aimed at deciphering the pathological effect of nonsynonymous single-nucleotide polymorphisms (nsSNPs) in NAGK gene. An integrated computational approach, including molecular dynamics (MD) simulation and protein-protein docking simulation, was used to identify the damaging nsSNPs and their detailed structural and functional consequences. The analysis revealed the four most damaging variants (G11R, G32R, G120E, and A156D), which are highly conserved and functional, positioned in both small (G11R and G32R) and large (G120E and A156D) domains of NAGK. G11R is located in the ATP binding region, while variants present in the large domain (G120E and A156D) were found to induce substantial alterations in the structural organizations of both domains, including the ATP and substrate binding sites. Furthermore, all variants were found to reduce binding energy between NAGK and dynein subunit DYNLRB1, as revealed by protein-protein docking and MM-GBSA binding energy calculation supporting their deleteriousness on non-canonical function. We hope these findings will direct future studies to gain more insight into the role of these variants in the loss of NAGK function and their role in neurodevelopmental disorders.

In silico analysis of deleterious SNPs of human MTUS1 gene and their impacts on subsequent protein structure and function

The mitochondrial tumor suppressor 1 (MTUS1) gene acts as a crucial tumor suppressor by inhibiting growth and proliferation of eukaryotic cells including tumor cell lines. Down regulation of MTUS1 gene has been implicated in a wide range of cancers as well as various human diseases. Alteration through nsSNPs can potentially damage the structure and/or function of the protein. As characterization of functional SNPs in such disease linked genes is a major challenge, it is feasible to analyze putative functional SNPs prior to performing larger population studies. Hence, in this in silico study we differentiated the potentially harmful nsSNPs of the MTUS1 gene from the neutral ones by using various sequence and structure based bioinformatic tools. In a total of 215 nsSNPs, 9 were found to be most likely to exert deleterious effect using 7 prediction tools. From which, 5nsSNPs (S1259L, E960K, P503T, L1084V and L1143Q) were selected as potentially damaging due to their presence in the highly conserved region and ability to decrease protein stability. In fact, 2 nsSNPs (S1259L and E960K) among these 5 were found to be individually associated with two distinctive cancers named Stomach adenocarcinoma and Uterine corpus endometrial carcinoma. As this is the first comprehensive study analyzing the functional nsSNPs of MTUS1, the results of the current study would certainly be helpful in future prospects concerning large population-based studies as well as drug discovery, especially developing individualized medicine.

Genome-wide analysis of 10664 SARS-CoV-2 genomes to identify virus strains in 73 countries based on single nucleotide polymorphism

Since the onslaught of SARS-CoV-2, the research community has been searching for a vaccine to fight against this virus. However, during this period, the virus has mutated to adapt to the different environmental conditions in the world and made the task of vaccine design more challenging. In this situation, the identification of virus strains is very much timely and important task. We have performed genome-wide analysis of 10664 SARS-CoV-2 genomes of 73 countries to identify and prepare a Single Nucleotide Polymorphism (SNP) dataset of SARS-CoV-2. Thereafter, with the use of this SNP data, the advantage of hierarchical clustering is taken care of in such a way so that Average Linkage and Complete Linkage with Jaccard and Hamming distance functions are applied separately in order to identify the virus strains as clusters present in the SNP data. In this regard, the consensus of both the clustering results are also considered while Silhouette index is used as a cluster validity index to measure the goodness of the clusters as well to determine the number of clusters or virus strains. As a result, we have identified five major clusters or virus strains present worldwide. Apart from quantitative measures, these clusters are also visualized using Visual Assessment of Tendency (VAT) plot. The evolution of these clusters are also shown. Furthermore, top 10 signature SNPs are identified in each cluster and the non-synonymous signature SNPs are visualised in the respective protein structures. Also, the sequence and structural homology-based prediction along with the protein structural stability of these non-synonymous signature SNPs are reported in order to judge the characteristics of the identified clusters. As a consequence, T85I, Q57H and R203M in NSP2, ORF3a and Nucleocapsid respectively are found to be responsible for Cluster 1 as they are damaging and unstable non-synonymous signature SNPs. Similarly, F506L and S507C in Exon are responsible for both Clusters 3 and 4 while Clusters 2 and 5 do not exhibit such behaviour due to the absence of any non-synonymous signature SNPs. In addition to all these, the code, SNP dataset, 10664 labelled SARS-CoV-2 strains and additional results as supplementary are provided through our website for further use.