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Origin of aromatase inhibitory activity via proteochemometric modeling

Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high selectivity, lower...

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Autores principales: Simeon, Saw, Spjuth, Ola, Lapins, Maris, Nabu, Sunanta, Anuwongcharoen, Nuttapat, Prachayasittikul, Virapong, Wikberg, Jarl E.S., Nantasenamat, Chanin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868594/
https://www.ncbi.nlm.nih.gov/pubmed/27190705
http://dx.doi.org/10.7717/peerj.1979
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author Simeon, Saw
Spjuth, Ola
Lapins, Maris
Nabu, Sunanta
Anuwongcharoen, Nuttapat
Prachayasittikul, Virapong
Wikberg, Jarl E.S.
Nantasenamat, Chanin
author_facet Simeon, Saw
Spjuth, Ola
Lapins, Maris
Nabu, Sunanta
Anuwongcharoen, Nuttapat
Prachayasittikul, Virapong
Wikberg, Jarl E.S.
Nantasenamat, Chanin
author_sort Simeon, Saw
collection PubMed
description Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high selectivity, lower toxicity and increased potency. Designing a novel therapeutic agent against aromatase could be achieved computationally by means of ligand-based and structure-based methods. For over a decade, we have utilized both approaches to design potential AIs for which quantitative structure–activity relationships and molecular docking were used to explore inhibitory mechanisms of AIs towards aromatase. However, such approaches do not consider the effects that aromatase variants have on different AIs. In this study, proteochemometrics modeling was applied to analyze the interaction space between AIs and aromatase variants as a function of their substructural and amino acid features. Good predictive performance was achieved, as rigorously verified by 10-fold cross-validation, external validation, leave-one-compound-out cross-validation, leave-one-protein-out cross-validation and Y-scrambling tests. The investigations presented herein provide important insights into the mechanisms of aromatase inhibitory activity that could aid in the design of novel potent AIs as breast cancer therapeutic agents.
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spelling pubmed-48685942016-05-17 Origin of aromatase inhibitory activity via proteochemometric modeling Simeon, Saw Spjuth, Ola Lapins, Maris Nabu, Sunanta Anuwongcharoen, Nuttapat Prachayasittikul, Virapong Wikberg, Jarl E.S. Nantasenamat, Chanin PeerJ Bioinformatics Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high selectivity, lower toxicity and increased potency. Designing a novel therapeutic agent against aromatase could be achieved computationally by means of ligand-based and structure-based methods. For over a decade, we have utilized both approaches to design potential AIs for which quantitative structure–activity relationships and molecular docking were used to explore inhibitory mechanisms of AIs towards aromatase. However, such approaches do not consider the effects that aromatase variants have on different AIs. In this study, proteochemometrics modeling was applied to analyze the interaction space between AIs and aromatase variants as a function of their substructural and amino acid features. Good predictive performance was achieved, as rigorously verified by 10-fold cross-validation, external validation, leave-one-compound-out cross-validation, leave-one-protein-out cross-validation and Y-scrambling tests. The investigations presented herein provide important insights into the mechanisms of aromatase inhibitory activity that could aid in the design of novel potent AIs as breast cancer therapeutic agents. PeerJ Inc. 2016-05-12 /pmc/articles/PMC4868594/ /pubmed/27190705 http://dx.doi.org/10.7717/peerj.1979 Text en ©2016 Simeon et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Simeon, Saw
Spjuth, Ola
Lapins, Maris
Nabu, Sunanta
Anuwongcharoen, Nuttapat
Prachayasittikul, Virapong
Wikberg, Jarl E.S.
Nantasenamat, Chanin
Origin of aromatase inhibitory activity via proteochemometric modeling
title Origin of aromatase inhibitory activity via proteochemometric modeling
title_full Origin of aromatase inhibitory activity via proteochemometric modeling
title_fullStr Origin of aromatase inhibitory activity via proteochemometric modeling
title_full_unstemmed Origin of aromatase inhibitory activity via proteochemometric modeling
title_short Origin of aromatase inhibitory activity via proteochemometric modeling
title_sort origin of aromatase inhibitory activity via proteochemometric modeling
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868594/
https://www.ncbi.nlm.nih.gov/pubmed/27190705
http://dx.doi.org/10.7717/peerj.1979
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