Prediction of the deleterious nsSNPs in ABCB transporters

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/.

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.

Non-neutral nonsynonymous single nucleotide polymorphisms in human ABC transporters: the first comparison of six prediction methods

Nonsynonymous single nucleotide polymorphisms (nsSNPs) in coding regions that can lead to amino acid changes may cause alteration of protein function and account for susceptibility to disease and altered drug/xenobiotic response. Abundant nsSNPs have been found in genes coding for human ATP-binding cassette (ABC) transporters, but there is little known about the relationship between the genotype and phenotype of nsSNPs in these membrane proteins. In addition, it is unknown which prediction method is better suited for the prediction of non-neutral nsSNPs of ABC transporters. We have identified 2,172 validated nsSNPs in 49 human ABC transporter genes from the Ensembl genome database and the NCBI SNP database. Using six different algorithms, 41 to 52% of nsSNPs in ABC transporter genes were predicted to have functional impacts on protein function. Predictions largely agreed with the available experimental annotations. Overall, 78.5% of non-neutral nsSNPs were predicted correctly as damaging by SNAP, which together with SIFT and PolyPhen, was superior to the prediction methods Pmut, PhD-SNP, and Panther. This study also identified any amino acids that were likely to be functionally critical but have not yet been studied experimentally. There was significant concordance between the predicted results of SIFT and PolyPhen. Evolutionarily non-neutral (destabilizing) amino acid substitutions are predicted to be the basis for the pathogenic alteration of ABC transporter activity that is associated with disease susceptibility and altered drug/xenobiotic response.

An ANN model for the identification of deleterious nsSNPs in tumor suppressor genes

Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occurs approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs), lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases and cancer. One of the main problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. An attempt was made to develop a new approach to predict such nsSNPs. This would enhance our understanding of genetic diseases and helps to predict the disease. We detect nsSNPs and all possible and reliable alleles by ANN, a soft computing model using potential SNP information. Reliable nsSNPs are identified, based on the reconstructed alleles and on sequence redundancy. The model gives good results with mean specificity (95.85&), sensitivity (97.40&) and accuracy (96.25&). Our results indicate that ANNs can serve as a useful method to analyze quantitative effect of nsSNPs on protein function and would be useful for large-scale analysis of genomic nsSNP data.

AVAILABILITY

The database is available for free at http://www.snp.mirworks.in.

Prediction of deleterious non-synonymous single-nucleotide polymorphisms of human uridine diphosphate glucuronosyltransferase genes

UDP glucuronosyltransferases (UGTs) are an important class of Phase II enzymes involved in the metabolism and detoxification of numerous xenobiotics including therapeutic drugs and endogenous compounds (e.g. bilirubin). To date, there are 21 human UGT genes identified, and most of them contain single-nucleotide polymorphisms (SNPs). Non-synonymous SNPs (nsSNPs) of the human UGT genes may cause absent or reduced enzyme activity and polymorphisms of UGT have been found to be closely related to altered drug clearance and/or drug response, hyperbilirubinemia, Gilbert's syndrome, and Crigler-Najjar syndrome. However, it is unlikely to study the functional impact of all identified nsSNPs in humans using laboratory approach due to its giant number. We have investigated the potential for bioinformatics approach for the prediction of phenotype based on known nsSNPs. We have identified a total of 248 nsSNPs from human UGT genes. The two algorithms tools, sorting intolerant from tolerant (SIFT) and polymorphism phenotyping (PolyPhen), were used to predict the impact of these nsSNPs on protein function. SIFT classified 35.5% of the UGT nsSNPs as "deleterious"; while PolyPhen identified 46.0% of the UGT nsSNPs as "potentially damaging" and "damaging". The results from the two algorithms were highly associated. Among 63 functionally characterized nsSNPs in the UGTs, 24 showed altered enzyme expression/activities and 45 were associated with disease susceptibility. SIFT and Polyphen had a correct prediction rate of 57.1% and 66.7%, respectively. These findings demonstrate the potential use of bioinformatics techniques to predict genotype-phenotype relationships which may constitute the basis for future functional studies.