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Origin of aromatase inhibitory activity via proteochemometric modeling
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-8083-2864
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
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2016 (English)In: PeerJ, ISSN 2167-8359, E-ISSN 2167-8359, Vol. 4, e1979Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. Vol. 4, e1979
Keyword [en]
Aromatase; Quantitative structure-activity relationship; Breast cancer; Data mining; QSAR; Aromatase inhibitor; Proteochemometrics
National Category
Bioinformatics and Systems Biology
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:uu:diva-295609DOI: 10.7717/peerj.1979ISI: 000376575000001PubMedID: 27190705OAI: oai:DiVA.org:uu-295609DiVA: diva2:934124
Funder
Swedish Research Council, C0610701
Available from: 2016-06-08 Created: 2016-06-08 Last updated: 2017-11-30Bibliographically approved

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Spjuth, OlaLapins, MarisWikberg, Jarl E. S.

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