3D-QSAR study of steroidal and azaheterocyclic human aromatase inhibitors using quantitative profile of protein–ligand interactions

Abstract

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.Electronic supplementary materialThe online version of this article (10.1186/s13321-017-0253-8) contains supplementary material, which is available to authorized users.

Towards an Understanding of the Mode of Action of Human Aromatase Activity for Azoles through Quantum Chemical Descriptors-Based Regression and Structure Activity Relationship Modeling Analysis

“…Two techniques are commonly used for the global application of the predictive modeling in toxicology, for example, employing either a large dataset using techniques such as machine learning methods or deriving mechanistically interpretable simple models [14,15]. Both techniques have been exploited to create predictive models for the antagonist activity of azoles with CYP19A1 [16][17][18][19][20]. Shoombuatong et al, 2018, reviewed this area and concluded that the modeling of nonsteroidal aromatase inhibition requires nitrogen-containing descriptors, polarizability, the energy of highest occupied molecular orbital (HOMO), the energy gap of highest occupied molecular orbital and lowest unoccupied molecular orbital (HOMO-LUMO gap), and descriptors for hydrogen bond acceptors [21].…”

Towards understanding aromatase inhibitory activity via QSAR modeling

“…The current trend (2016-2018; Figure 4 (Fig. 4) ) shows that eight articles (Song et al, 2016[ 91 ]; Ghodsi and Hemmateenejad, 2016[ 32 ]; Adhikari et al, 2017[ 1 ]; Prachayasittikul et al, 2017[ 72 ]; Pingaew et al, 2018[ 70 ]; Lee and Barron, 2018[ 49 ]; Barigye et al, 2018[ 9 ]) have already been published in comparison to a total of 13 publications for the previous 5 years. Thus, it is promising that the number of publication regarding AIs utilizing QSAR models for prediction will continue to grow.…”

“…The authors noted that these results were further validated with the 3D-QSAR analysis while the HQSAR model inferred the importance of the p -cyanophenyl moiety in regulating AI. Additionally, Lee and Barron (2018[ 49 ]) conducted 3D-QSAR studies on the bioactivity (IC 50 ) of 124 compounds exhibiting AI activity (steroidal and heterocyclic). Multiple linear regress-ion combined with genetic algorithm (GA-MLR) was used to build the models which was then validated via the LOO and external validation methods.…”

“…As summarized in Table 4 (Tab. 4) (References in Table 4: Nagy et al, 1994[ 58 ]; Recanatini, 1996[ 75 ]; Oprea and García, 1996[ 67 ]; Sulea et al, 1997[ 92 ]; Recanatini and Cavalli, 1998[ 76 ]; Cavalli et al, 2000[ 18 ]; Beger et al, 2001[ 10 ]; Gironés and Carbó-Dorca, 2002[ 34 ]; Beger and Wilkes, 2002[ 12 ]; Polanski and Gieleciak, 2003[ 71 ]; Leonetti et al, 2004[ 50 ]; Beger et al, 2004[ 11 ]; Cavalli et al, 2005[ 17 ]; Bak and Polanski, 2007[ 8 ]; Castellano et al, 2008[ 16 ]; Nagar et al, 2008[ 55 ]; Mittal et al, 2009[ 53 ]; Gueto et al, 2009[ 36 ]; Dai et al, 2010[ 24 ]; Roy and Roy, 2010[ 78 ]; Roy and Roy, 2010[ 77 ]; Nagar and Saha, 2010[ 57 ]; Nagar and Saha, 2010[ 56 ]; Narayana et al, 2012[ 65 ]; Nantasenamat et al, 2013[ 64 ]; Nantasenamat et al, 2013[ 61 ]; Kishore et al, 2013[ 44 ]; Worachartcheewan et al, 2014[ 103 ]; Worachartcheewan et al, 2014[ 101 ]; Nantasenamat et al, 2014[ 63 ]; Dai et al, 2014[ 25 ]; Awasthi et al, 2015[ 7 ]; Xie et al, 2015[ 105 ]; Shoombuatong et al, 2015[ 85 ]; Xie et al, 2014[ 102 ]; Kumar et al, 2016[ 48 ]; Ghodsi and Hemmateenejad, 2016[ 32 ]; Song et al, 2016[ 91 ]; Prachayasittikul et al, 2017[ 72 ]; Adhikari et al, 2017[ 1 ]; Lee and Barron, 2018[ 49 ]; Pingaew et al, 2018[ 70 ]; Barigye et al, 2018[ 9 ]), it can be observed that prior to 2010, MLR and PLS models, also known as white-box approaches, were the most popular and yet simple learning algorithms used for QSAR modeling of AIs.…”

Molecular Modeling Of Novel Non-Steroidal Aromatase Inhibitors Containing 1,2,4-Triazole

“…До недавнего времени в связи с отсутствием информации о трехмерной структуре ароматазы новые ингибиторы CYP19A1 разрабатывались с привлечением непрямых методов компьютерного конструирования лекарств, базирующихся на анализе известных лигандов к ферменту и последующем выявлении их общих структурных свойств, которые обусловливают биологическую активность [6][7][8]. Определение методом рентгеноструктурного анализа пространственной структуры ароматазы высокого разрешения [2,9] создало предпосылки не только для понимания функции и механизма действия фермента, но и для разработки новых эффективных ингибиторов CYP19А1 на основе прямых методов компьютерного конструирования лекарств, использующих данные о структуре молекулярной мишени (см., например, работы [10][11][12][13][14][15][16][17][18][19][20][21]).…”