In silico screening and analysis of nonsynonymous SNPs in human CYP1A2 to assess possible associations with pathogenicity and cancer susceptibility

Abstract Cytochrome P450 1A2 (CYP1A2) is one of the main hepatic CYPs involved in metabolism of carcinogens and clinically used drugs. Nonsynonymous single nucleotide polymorphisms (nsSNPs) of this enzyme could affect cancer susceptibility and drug efficiency. Hence, identification of human CYP1A2 p...

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Autores principales: Leila Navapour, Navid Mogharrab
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/6127382a36544a71adb5f24a6f1d7322
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Sumario:Abstract Cytochrome P450 1A2 (CYP1A2) is one of the main hepatic CYPs involved in metabolism of carcinogens and clinically used drugs. Nonsynonymous single nucleotide polymorphisms (nsSNPs) of this enzyme could affect cancer susceptibility and drug efficiency. Hence, identification of human CYP1A2 pathogenic nsSNPs could be of great importance in personalized medicine and pharmacogenetics. Here, 176 nsSNPs of human CYP1A2 were evaluated using a variety of computational tools, of which 18 nsSNPs were found to be associated with pathogenicity. Further analysis suggested possible association of 9 nsSNPs (G73R, G73W, R108Q, R108W, E168K, E346K, R431W, F432S and R456H) with the risk of hepatocellular carcinoma. Molecular dynamics simulations revealed higher overall flexibility, decreased intramolecular hydrogen bonds and lower content of regular secondary structures for both cancer driver variants G73W and F432S when compared to the wild-type structure. In case of F432S, loss of the conserved hydrogen bond between Arg137 and heme propionate oxygen may affect heme stability and the observed significant rise in fluctuation of the CD loop could modify CYP1A2 interactions with its redox partners. Together, these findings propose CYP1A2 as a possible candidate for hepatocellular carcinoma and provide structural insights into how cancer driver nsSNPs could affect protein structure, heme stability and interaction network.