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• W3172610570 abstract "The Aldo-keto reductase family consists of a number of enzymes which are essential to the catalysis of redox transformation and which are also involved in intermediate metabolism and detoxification. Several studies have been reported that the catalytic-dependent function of AKR1C family members isoforms and their essential roles in various cancer types including prostate cancer and play a key role in drug resistance and drug detoxification. The aim of the current study was to predict and analyze the deleterious single nucleotide polymorphism (SNP) that is highly associated with prostate cancer. In addition, to find the potent bioactive compounds as effective inhibitors against prostate cancer. Various computational methods are employed to analyze the various non-synonymous single nucleotide polymorphisms (nsSNPs) in the AKR1C3 gene. A total of 18,594 SNPs data set of deleterious and non-coding synonymous were retrieved from the dbSNP database followed by various computational SNP prediction tools were performed to find the most deleterious nsSNP. A total of eight high-risk nsSNPs were predicted and most of the residue is present in the structural and functional conserved domain, hence, both wild type and mutant forms of AKR1C3 were selected for structural analysis. Besides, molecular docking, ADME, and Prime MM/GBSA calculations were also performed with plant derived bioactive compounds with AKR1C3 receptors. The results of the study depicted that the rs62621365 and its possible mutation A258C was considered as the most deleterious nsSNP and plant compounds such as Ginkgetin and Withaferin A shows best binding affinity with both wild type and mutant from of AKR1C3. The overall results depicted that nsSNPs may be considered for risk assessment against prostate cancer and for cure, the suggested plant derived bioactive compounds may act as potent inhibitors." @default.
• W3172610570 created "2021-06-22" @default.
• W3172610570 creator A5085478268 @default.
• W3172610570 date "2021-09-01" @default.
• W3172610570 modified "2023-10-14" @default.
• W3172610570 title "In silico prediction of deleterious single nucleotide polymorphism in human AKR1C3 gene and identification of potent inhibitors using molecular docking approach" @default.
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• W3172610570 doi "https://doi.org/10.1016/j.jksus.2021.101514" @default.
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