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Identification of deleterious nsSNPs in ?, ?, ? and ? class of GST family and their influence on protein structure.

ABSTRACT: GST family genes have a critical role in xenobiotic metabolism and drug resistance. Among the GST family the GST-?, GST-?, GST-? and GST-? are the most abundant classes and have a major role in the carcinogen detoxification process. Nevertheless the activity of these enzymes may differ due to polymorphisms which ultimately results in interindividual susceptibility to cancer development. In this work, we have analyzed the potentially deleterious nsSNPs that can alter the function of these genes. As a result among the nsSNPs, 101 (42.61%) were found to be deleterious by a sequence homology-based tool, 67 (28.27%) by a structure homology based tool and a total of 59 (24.89%) by both. We propose a modeled structure of the five highly deleterious mutant proteins. Our results will provide useful information in selecting target SNPs that are likely to have an impact on GST activity and contribute to an individual's susceptibility to the disease.

SUBMITTER: Yadav P

PROVIDER: S-EPMC4535831 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Identification of deleterious nsSNPs in α, μ, π and θ class of GST family and their influence on protein structure.

Yadav P P Chatterjee A A Bhattacharjee A A

Genomics data 20140509

GST family genes have a critical role in xenobiotic metabolism and drug resistance. Among the GST family the GST-μ, GST-π, GST-α and GST-θ are the most abundant classes and have a major role in the carcinogen detoxification process. Nevertheless the activity of these enzymes may differ due to polymorphisms which ultimately results in interindividual susceptibility to cancer development. In this work, we have analyzed the potentially deleterious nsSNPs that can alter the function of these genes. ...[more]

PMID: 26484073

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Project description:BACKGROUND: Recent reports suggest the role of nonsynonymous single nucleotide polymorphisms (nsSNPs) in cyclin-dependent kinase 7 (CDK7) gene associated with defect in the DNA repair mechanism that may contribute to cancer risk. Among the various inhibitors developed so far, flavopiridol proved to be a potential antitumor drug in the phase-III clinical trial for chronic lymphocytic leukemia. Here, we described a theoretical assessment for the discovery of new drugs or drug targets in CDK7 protein owing to the changes caused by deleterious nsSNPs. METHODS: Three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on protein function by SIFT, PolyPhen2, I-Mutant3, PANTHER, SNPs&GO, PhD-SNP, and screening for non-acceptable polymorphisms (SNAP). Furthermore, we analyzed the native and proposed mutant models in atomic level 10 ns simulation using the molecular dynamics (MD) approach. Finally, with the aid of Autodock 4.0 and PatchDock, we analyzed the binding efficacy of flavopiridol with CDK7 protein with respect to the deleterious mutations. RESULTS: By comparing the results of all seven prediction tools, three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on the protein function. The results of protein stability analysis inferred that I63R and H135R exhibited less deviation in root mean square deviation in comparison with the native and T285M protein. The flexibility of all the three mutant models of CDK7 protein is diverse in comparison with the native protein. Following to that, docking study revealed the change in the active site residues and decrease in the binding affinity of flavopiridol with mutant proteins. CONCLUSION: This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. The identification of disease related SNPs by computational methods has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases. LAY ABSTRACT: Cell cycle regulatory protein, CDK7, is linked with DNA repair mechanism which can contribute to cancer risk. The main aim of this study is to extrapolate the relationship between the nsSNPs and their effects in drug-binding capability. In this work, we propose a new methodology which (1) efficiently identified the deleterious nsSNPs that tend to have functional effect on protein function upon mutation by computational tools, (2) analyze d the native protein and proposed mutant models in atomic level using MD approach, and (3) investigated the protein-ligand interactions to analyze the binding ability by docking analysis. This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. Overall, this approach has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases.

Dataset Information

Identification of deleterious nsSNPs in ?, ?, ? and ? class of GST family and their influence on protein structure.

Publications

Identification of deleterious nsSNPs in α, μ, π and θ class of GST family and their influence on protein structure.

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