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3D-QSAR Study of Steroidal and Azaheterocyclic Human Aromatase Inhibitors using Quantitative Profile of Protein-Ligand Interactions
Citation:
Lee, S. AND M. Barron. 3D-QSAR Study of Steroidal and Azaheterocyclic Human Aromatase Inhibitors using Quantitative Profile of Protein-Ligand Interactions. Journal of Cheminformatics. Springer, New York, NY, 10(2):1-13, (2018).
Impact/Purpose:
This manuscript presents a three dimensional quantitative prediction model for the enzyme aromatase. It is important because 1) aromatase is an area of active research in ORD, 2) it contributes to the development of an Adverse Outcome Pathway (AOP) for aromatase by helping defining the applicability domain and chemical space of the AOP, and 3) the model can be used to screen environmental chemicals for aromatase activity. An AOP is an organizing framework for linking a chemical’s interaction with a biological receptor, through a cascade of biochemical and tissue responses, to an ultimate population outcome. Aromatase is an active area of research in ORD because of its important role in determining organism development. This paper is important because it better defines the molecular interactions of environmental chemicals with aromatase, which is a first critical step in the AOP.
Description:
Aromatase is a member of the cytochrome P450 superfamily responsible for a key step in the biosynthesis of estrogens. As estrogens are involved in the control of important reproduction-related processes, including sexual differentiation and maturation, aromatase is a potential target for endocrine disrupting chemicals as well as breast cancer therapy. In this work, 3D-QSAR combined with quantitative profile of protein–ligand interactions was employed in the identification and characterization of critical steric and electronic features of aromatase-inhibitor complexes and the estimation of their quantitative contribution to inhibition potency. Bioactivity data on pIC50 values of 175 steroidal and 124 azaheterocyclic human aromatase inhibitors (AIs) were used for the 3D-QSAR analysis. For the quantitative description of the effects of the hydrophobic contact and nitrogen–heme–iron coordination on aromatase inhibition, the hydrophobicity density field model and the smallest dual descriptor Δf(r)S were introduced, respectively. The model revealed that hydrophobic contact and nitrogen–heme–iron coordination primarily determines inhibition potency of steroidal and azaheterocyclic AIs, respectively. Moreover, hydrogen bonds with key amino acid residues, in particular Asp309 and Met375, and interaction with the heme–iron are required for potent inhibition. Phe221 and Thr310 appear to be quite flexible and adopt different conformations according to a substituent at 4- or 6-position of steroids. Flexible docking results indicate that proper representation of the residues’ flexibility is critical for reasonable description of binding of the structurally diverse inhibitors. Our results provide a quantitative and mechanistic understanding of inhibitory activity of steroidal and azaheterocyclic AIs of relevance to adverse outcome pathway development and rational drug design.
