Computer-Aided Ligand Discovery for Estrogen Receptor Alpha

Abstract

Breast cancer (BCa) is one of the most predominantly diagnosed cancers in women. Notably, 70% of BCa diagnoses are Estrogen Receptor α positive (ERα+) making it a critical therapeutic target. With that, the two subtypes of ER, ERα and ERβ, have contrasting effects on BCa cells. While ERα promotes cancerous activities, ERβ isoform exhibits inhibitory effects on the same. ER-directed small molecule drug discovery for BCa has provided the FDA approved drugs tamoxifen, toremifene, raloxifene and fulvestrant that all bind to the estrogen binding site of the receptor. These ER-directed inhibitors are non-selective in nature and may eventually induce resistance in BCa cells as well as increase the risk of endometrial cancer development. Thus, there is an urgent need to develop novel drugs with alternative ERα targeting mechanisms that can overcome the limitations of conventional anti-ERα therapies. Several functional sites on ERα, such as Activation Function-2 (AF2), DNA binding domain (DBD), and F-domain, have been recently considered as potential targets in the context of drug research and discovery. In this review, we summarize methods of computer-aided drug design (CADD) that have been employed to analyze and explore potential targetable sites on ERα, discuss recent advancement of ERα inhibitor development, and highlight the potential opportunities and challenges of future ERα-directed drug discovery.

ERpred: a web server for the prediction of subtype-specific estrogen receptor antagonists

“…estradiol or E2) has only 1 aromatic ring while the most common antagonists of ER (i.e. tamoxifen and fulvestrant) have 3 and 1 aromatic rings, respectively ( Bafna et al, 2020 ). In addition, it is the phenyl group in E2 that forms hydrogen bonds with Glu353 and Arg394 in the active site.…”

“…Owing to the very subtle differences in the LBDs of both ER subtypes, the design of a subtype-specific antagonist is a challenge. Nevertheless, major advances over the past two decades in the fields of structural biology pertaining to ERs have shed light on the plasticity and binding modes of both ER subtypes ( Bafna et al, 2020 ; Brzozowski et al, 1997 ; Pavlin et al, 2018 ; Pang et al, 2018 ; Shiau et al, 2002 ).…”

“…Such models are also useful for rationalizing the importance and contributions of molecular features on investigated activities/properties. Subsequently, crucial information pertaining to ER binding affinity coupled with structure-binding and structure-activity relationship data, have led to the formulation of reliable ERα models ( Anstead, Carlson & Katzenellenbogen, 1997 ; Serafimova et al, 2007 ; Xiang et al, 2009 ; Toropov et al, 2012 ; Chang et al, 2013 ; Ribay et al, 2016 ; Suvannang et al, 2018 ; Lee & Barron, 2017 ; Pavlin et al, 2018 ; Pang et al, 2018 ; Balabin & Judson, 2018 ; Cotterill et al, 2019 ; Bafna et al, 2020 ). However, not much research has been conducted regarding the binding specificity towards ERβ ( Manas et al, 2004 ; Coriano et al, 2018 ).…”

PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors

“…Subsequently, the descriptor ensPLIF was introduced to cover all relevant docking poses produced during the SBVS campaign in order to mimic the lock-and-key and the induced-fit theories [ 23 ] in the RPART analysis [ 13 , 24 ]. The protocols were then employed to prospectively screen and design novel ligands for ERα [ 13 , 25 , 26 ] and inhibitors for acetylcholine esterase (AChE) [ 27 , 28 , 29 ]. Interestingly, besides increasing the prediction quality of the SBVS protocols to identify ligand for ERα [ 13 ] and inhibitor for acetylcholine esterase (AChE) [ 24 ], the combined methods could also predict the important amino acid residues that play an essential role in the ligand binding to the proteins [ 13 , 24 ].…”

Integration of Molecular Docking and In Vitro Studies: A Powerful Approach for Drug Discovery in Breast Cancer

“…In addition, molecular docking can be also used for predicting the effects of a drug; for example, the identification of an undesired interaction between a compound and off-targets. To date, 57,000 abstracts/papers have been published on molecular docking, indicating the importance of this computational method in drug development [13][14][15].…”