Exploring Ligand Binding Domain Dynamics in the NRs Superfamily

Abstract

Nuclear receptors (NRs) are transcription factors that play an important role in multiple diseases, such as cancer, inflammation, and metabolic disorders. They share a common structural organization composed of five domains, of which the ligand-binding domain (LBD) can adopt different conformations in response to substrate, agonist, and antagonist binding, leading to distinct transcription effects. A key feature of NRs is, indeed, their intrinsic dynamics that make them a challenging target in drug discovery. This work aims to provide a meaningful investigation of NR structural variability to outline a dynamic profile for each of them. To do that, we propose a methodology based on the computation and comparison of protein cavities among the crystallographic structures of NR LBDs. First, pockets were detected with the FLAPsite algorithm and then an “all against all” approach was applied by comparing each pair of pockets within the same sub-family on the basis of their similarity score. The analysis concerned all the detectable cavities in NRs, with particular attention paid to the active site pockets. This approach can guide the investigation of NR intrinsic dynamics, the selection of reference structures to be used in drug design and the easy identification of alternative binding sites.

Liver X Receptors (LXRs) in cancer-an Eagle’s view on molecular insights and therapeutic opportunities

“…Nuclear receptors (NRs) are a class of proteins that act as eukaryotic transcription factors, regulate gene expression, mediate the transduction of signaling pathways, and are associated with various diseases such as cardiovascular diseases and cancer. To date, 48 NRs have been identified and their roles are being studied concerning their role in cellular development, homeostasis, and lipid and cholesterol metabolism ( Sever and Glass, 2013 ; Mazaira et al, 2018 ; D’Arrigo et al, 2022 ). The 48 NRs are classified into 6 types, namely, Thyroid Hormone Receptor-like (NR1), Retinoid X Receptor-like (NR2), Estrogen Receptor-like (NR3), Nerve Growth Factor IB-like (NR4), Steroidogenic Factor-like (NR5), and Germ Cell Nuclear Factor-like (NR6) respectively ( Tanaka et al, 2017 ; Tran et al, 2018 ).…”

Progress of the “Molecular Informatics” Section in 2022

“…To collect high-quality research articles in all fields of molecular informatics applied to the understanding of the intricate world of biology and molecular research, the Topical Collection, "Feature Papers in Molecular Informatics", was created, and, looking only at 2022, 25 papers were published . The most recent developments in computer science applied to molecular modeling have been discussed as hot topics in the two collections entitled "State-of-the-Art Molecular Informatics in Italy", which was closed at the end of 2022 [26][27][28][29][30][31][32][33], and "Latest Review Papers in Molecular Informatics 2023", which was closed at the end of February 2023 [34,35]. In 2022, the number of closed SIs was 22, and the top SI based on the number of published articles and visualizations was the one dedicated to "Deep Learning and Machine Learning in Bioinformatics" .…”