In silico screening and identification of deleterious missense SNPs along with their effects on CD-209 gene: An insight to CD-209 related-diseases

Prediction of the most deleterious non-synonymous SNPs in the human IL1B gene: evidence from bioinformatics analyses

BACKGROUND

Polymorphisms in IL1B play a significant role in depression, multiple inflammatory-associated disorders, and susceptibility to infection. Functional non-synonymous SNPs (nsSNPs) result in changes in the encoded amino acids, potentially leading to structural and functional alterations in the mutant proteins. So far, most genetic studies have concentrated on SNPs located in the IL1B promoter region, without addressing nsSNPs and their association with multifactorial diseases. Therefore, this study aimed to explore the impact of deleterious nsSNPs retrieved from the dbSNP database on the structure and functions of the IL1B protein.

RESULTS

Six web servers (SIFT, PolyPhen-2, PROVEAN, SNPs&GO, PHD-SNP, PANTHER) were used to analyze the impact of 222 missense SNPs on the function and structure of IL1B protein. Five novel nsSNPs (E100K, T240I, S53Y, D128Y, and F228S) were found to be deleterious and had a mutational impact on the structure and function of the IL1B protein. The I-mutant v2.0 and MUPro servers predicted that these mutations decreased the stability of the IL1B protein. Additionally, these five mutations were found to be conserved, underscoring their significance in protein structure and function. Three of them (T240I, D128Y, and F228S) were predicted to be cancer-causing nsSNPs. To analyze the behavior of the mutant structures under physiological conditions, we conducted a 50 ns molecular dynamics simulation using the WebGro online tool. Our findings indicate that the mutant values differ from those of the IL1B wild type in terms of RMSD, RMSF, Rg, SASA, and the number of hydrogen bonds.

CONCLUSIONS

This study provides valuable insights into nsSNPs located in the coding regions of IL1B, which lead to direct deleterious effects on the functional and structural aspects of the IL1B protein. Thus, these nsSNPs could be considered significant candidates in the pathogenesis of disorders caused by IL1B dysfunction, contributing to effective drug discovery and the development of precision medications. Thorough research and wet lab experiments are required to verify our findings. Moreover, bioinformatic tools were found valuable in the prediction of deleterious nsSNPs.

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.

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/.

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.

Non-synonymous SNPs variants of PRKCG and its association with oncogenes predispose to hepatocellular carcinoma

BACKGROUND

PRKCG encodes PKC γ, which is categorized under the classical protein kinase C family. No studies have specifically established the relationship between PRKCG nsSNPs with structural and functional variations in PKC γ in the context of hepatocellular carcinoma (HCC). The present study aims to uncover this link through in-silico and experimental studies.

METHODS

The 3D structure of PKC γ was predicted. Molecular Dynamic (MD) Simulations were run and estimates were made for interactions, stability, conservation and post-translational alterations between wild and mutant structures. The association of PRKCG levels with HCC survival rate was determined. Genotyping analyses were conducted to investigate the deleterious PRKCG nsSNP association with HCC. mRNA expression of PKC γ, HIF-1 alpha, AKT, SOCS3 and VEGF in the blood of controls and HCC patients was analyzed and a genetic cascade was constructed depicting these interactions.

RESULTS

The expression level of studied oncogenes was compared to tumour suppressor genes. Through Alphafold, the 3D structure of PKC γ was explored. Fifteen SNPs were narrowed down for in-silico analyses that were identified in exons 5, 10 and 18 and the regulatory and kinase domain of PKC γ. Root mean square deviation and fluctuation along with the radius of gyration unveiled potential changes between the wild and mutated variant structures. Mutant genotype AA (homozygous) corresponding to nsSNP, rs386134171 had more frequency in patients with OR (2.446), RR (1.564) and P-values (< 0.0029) that highlights its significant association with HCC compared to controls in which the wild genotype GG was found more prevalent.

CONCLUSION

nsSNP rs386134171 can be a genetic marker for HCC diagnosis and therapeutic studies. This study has laid down a road map for future studies to be conducted on HCC.

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

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

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.