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Novel aromatase inhibitors selection using induced fit docking and extra precision methods: Potential clinical use in ER-alpha-positive breast cancer

Aromatase (CYP19A1) the key enzyme of estrogen biosynthesis, is often deregulated in breast cancer patients. It catalyzes the conversion of androgen to estrogen, thus responsible for production of estrogen in human body. However, it causes over-production of estrogen which eventually leads to prolif...

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Autores principales: Kumavath, Ranjith, Azad, Manan, Devarapalli, Pratap, Tiwari, Sandeep, Kar, Shreya, Barh, Debmalya, Azevedo, Vasco, Kumar, Alan Prem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320928/
https://www.ncbi.nlm.nih.gov/pubmed/28293075
http://dx.doi.org/10.6026/97320630012324
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author Kumavath, Ranjith
Azad, Manan
Devarapalli, Pratap
Tiwari, Sandeep
Kar, Shreya
Barh, Debmalya
Azevedo, Vasco
Kumar, Alan Prem
author_facet Kumavath, Ranjith
Azad, Manan
Devarapalli, Pratap
Tiwari, Sandeep
Kar, Shreya
Barh, Debmalya
Azevedo, Vasco
Kumar, Alan Prem
author_sort Kumavath, Ranjith
collection PubMed
description Aromatase (CYP19A1) the key enzyme of estrogen biosynthesis, is often deregulated in breast cancer patients. It catalyzes the conversion of androgen to estrogen, thus responsible for production of estrogen in human body. However, it causes over-production of estrogen which eventually leads to proliferation of breast cancer cells. Identification of new small molecule inhibitors targeted against CYP19A1 therefore, facilitates to increase drug sensitivity of cancer cells. In this scenario, the present study aims to identify new molecules which could block or suppress the activity of aromatase enzyme by molecular docking studies using Schrödinger-Maestro v9.3. In this study we used in silico approach by modeling CYP19A1 protein the strcture was subjected to protein preparation wizard; to add hydrogen and optimize the protonation states of Thr310 and Ser478 and Asp309 residues. Active site of the CYP19A1 protein was identified using SiteMap tool of Scchrodinger package. We further carried out docking studies by means of Glid, with various ligands. Based on glid score, potential ligands were screeened and their interaction with CYP19A1 was identified. The best hits were further screened for Lipinski’s rule for drug-likeliness and bioactivity scoring properties. Thus, we report two rubivivaxin and rhodethrin compounds that have successfully satisfied all in silico parameters, necessitating further in vitro and in vivo studies.
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spelling pubmed-53209282017-03-14 Novel aromatase inhibitors selection using induced fit docking and extra precision methods: Potential clinical use in ER-alpha-positive breast cancer Kumavath, Ranjith Azad, Manan Devarapalli, Pratap Tiwari, Sandeep Kar, Shreya Barh, Debmalya Azevedo, Vasco Kumar, Alan Prem Bioinformation Hypothesis Aromatase (CYP19A1) the key enzyme of estrogen biosynthesis, is often deregulated in breast cancer patients. It catalyzes the conversion of androgen to estrogen, thus responsible for production of estrogen in human body. However, it causes over-production of estrogen which eventually leads to proliferation of breast cancer cells. Identification of new small molecule inhibitors targeted against CYP19A1 therefore, facilitates to increase drug sensitivity of cancer cells. In this scenario, the present study aims to identify new molecules which could block or suppress the activity of aromatase enzyme by molecular docking studies using Schrödinger-Maestro v9.3. In this study we used in silico approach by modeling CYP19A1 protein the strcture was subjected to protein preparation wizard; to add hydrogen and optimize the protonation states of Thr310 and Ser478 and Asp309 residues. Active site of the CYP19A1 protein was identified using SiteMap tool of Scchrodinger package. We further carried out docking studies by means of Glid, with various ligands. Based on glid score, potential ligands were screeened and their interaction with CYP19A1 was identified. The best hits were further screened for Lipinski’s rule for drug-likeliness and bioactivity scoring properties. Thus, we report two rubivivaxin and rhodethrin compounds that have successfully satisfied all in silico parameters, necessitating further in vitro and in vivo studies. Biomedical Informatics 2016-10-10 /pmc/articles/PMC5320928/ /pubmed/28293075 http://dx.doi.org/10.6026/97320630012324 Text en © 2016 Biomedical Informatics This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
spellingShingle Hypothesis
Kumavath, Ranjith
Azad, Manan
Devarapalli, Pratap
Tiwari, Sandeep
Kar, Shreya
Barh, Debmalya
Azevedo, Vasco
Kumar, Alan Prem
Novel aromatase inhibitors selection using induced fit docking and extra precision methods: Potential clinical use in ER-alpha-positive breast cancer
title Novel aromatase inhibitors selection using induced fit docking and extra precision methods: Potential clinical use in ER-alpha-positive breast cancer
title_full Novel aromatase inhibitors selection using induced fit docking and extra precision methods: Potential clinical use in ER-alpha-positive breast cancer
title_fullStr Novel aromatase inhibitors selection using induced fit docking and extra precision methods: Potential clinical use in ER-alpha-positive breast cancer
title_full_unstemmed Novel aromatase inhibitors selection using induced fit docking and extra precision methods: Potential clinical use in ER-alpha-positive breast cancer
title_short Novel aromatase inhibitors selection using induced fit docking and extra precision methods: Potential clinical use in ER-alpha-positive breast cancer
title_sort novel aromatase inhibitors selection using induced fit docking and extra precision methods: potential clinical use in er-alpha-positive breast cancer
topic Hypothesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320928/
https://www.ncbi.nlm.nih.gov/pubmed/28293075
http://dx.doi.org/10.6026/97320630012324
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