Selection of Appropriate Inhibitor for Ovarian Type Cytochrome P450 Aromatase Gene of Heteropneustesfossilis with the Application of Homology Modeling, Molecular Docking and ADMET Analysis

Abstract

Background: Cytochrome P450 aromatase (Cyp P450 arom) is an important steroidogenic enzyme responsible for conversion of androgens to estrogens and therefore it plays a critical role in vertebrate reproduction. In contrast to vertebrates, teleost fish have two distinct forms of aromatase. One form predominates in ovaries (ovarian type;cyp19a1a), while the other form prevails in brain (brain aromatase;cyp19a1b). Aromatase is highly present during the differentiation of ovaries. It is also susceptible to environmental influences, particularly temperature and xenoestrogens, environmental natural and synthetic pollutants. Methods: In the present study, by applying bioinformatics approach we investigated relationship between catfish ovarian aromatase to its inhibitor related compounds or drugs. The three dimensional (3D) structure of cyp19a1ais not predicted experimentally, so its structure is modelled by using comparative modelling approach and further validated by various tools like RAMPAGE, ProSa, Errat. Various bioinformatics approaches such asUniprot, Homology modeling, Molecular docking, ADMET analysis were followed for the selection of appropriate aromatase inhibitor. We mainly emphasised on Docking that was carried out by Autodock 4.2. The ligand molecules docked into the structures of macromolecular targets (aromatase) and as a result of docking we came to know about different Binding Energies of different inhibitor molecules and lastly Docked structures were analysed by Protein-Ligand Interaction Profiler. Results: Predicted protein model described in this work may be further used for finding interactions with other proteins involved in different types of diseases. Among the drugs Exemestane is highly structurally similar to known active compounds. The selected compounds were further analysed and refined using drug-likeness and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) analysis and with the help of tools we predicted about pharmacokinetic, metabolic, toxicity endpoints. Conclusion: The study showed that how in silico approaches will further increase our ability in the discovery of appropriate drug in the form of Exemestane (which is most stable and perfect aromatase inhibitor among various selected inhibitors). Overall, findings of this study may be helpful in designing the novel therapeutic targets to cure aromatase related cancer disorders.