Origin of aromatase inhibitory activity via proteochemometric modeling

Abstract

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.

Towards understanding aromatase inhibitory activity via QSAR modeling

“…For example, it is anticipated that insights gained from QSAR models alone provides one aspect where it may be beneficial to also call upon complementary methodologies such as structure-based and systems-based approaches to facilitate and augment the ligand-based QSAR approach. In fact, there have been a few studies employing a multitude of ligand, structure and systems-based approaches in studying aromatase inhibition (Simeon et al 2016[ 88 ]); Suvannang et al 2011[ 93 ]) and future works along this line is expected to be of great benefit to the scientific community.…”

Pathophysiological Effects of Contemporary Lifestyle on Evolutionary-Conserved Survival Mechanisms that Disturb the Hallmarks of Health in Polycystic Ovary Syndrome

“…Structural models used to investigate the molecular evolution of aromatase have characterized the amino-acid sequences and configurations, substrate recognition sites, catalytic mechanisms and inhibitor specificities (226)(227)(228). It is clear that there is a complex molecular network that regulates the normal physiology of aromatase activity, and subsequent androgen and estrogen production, to maintain tissue-specific homeostasis and function (228).…”

Proteochemometric modeling of the origin of thymidylate synthase inhibition

“…As an extension of the traditional quantitative structure–activity relationship (QSAR) models, proteochemometrics (PCM) is a ligand‐target modeling approach, where the bioactivity of multiple ligands against multiple proteins is modeled simultaneously to analyze the interaction space of ligands across protein targets . So far, PCM has been successfully applied with regard to many protein families including G protein‐coupled receptors, proteases, kinases, cytochrome P450, carbonic anhydrase, and phosphodiesterase . Inhibition of folate‐dependent enzymes, including thymidylate synthase (TS) and dihydrofolate reductase (DHFR), has long been considered as a promising strategy for the treatment of hyperproliferative disorders such as cancer and infectious diseases .…”