Origin of aromatase inhibitory activity via proteochemometric modeling
- Subject Areas
- Bioinformatics, Computational Biology, Drugs and Devices, Pharmacology
- aromatase, quantitative structure-activity relationship, breast cancer, data mining, QSAR, aromatase inhibitor, proteochemometrics
- © 2015 Simeon et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, 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 PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Origin of aromatase inhibitory activity via proteochemometric modeling. PeerJ PrePrints 3:e1556v1 https://doi.org/10.7287/peerj.preprints.1556v1
Aromatase, which is a 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 relationship 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.
This is a submission to PeerJ for review. This is part of the "1st International Conference on Pharmaceutical Bioinformatics" PeerJ collection.