Understanding nuclear receptors using computational methods

Abstract

Nuclear receptors (NRs) are important targets for therapeutic drugs. NRs regulate transcriptional activities through binding to ligands and interacting with several regulating proteins. Computational methods can provide insights into essential ligand-receptor and protein-protein interactions. These in turn have facilitated the discovery of novel agonists and antagonists with high affinity and specificity as well as have aided in the prediction of toxic side effects of drugs by identifying possible off-target interactions. Here, we review the application of computational methods toward several clinically important NRs (with special emphasis on PXR) and discuss their use for screening and predicting the toxic side effects of xenobiotics.

Figure 1

Domain structures of NRs and mechanism of action upon ligand binding. A) The NR superfamily shares a common domain structure consisting of a NH₂-terminal domain (NTD), a central DNA-binding domain (DBD), a carboxy-terminal ligand-binding domain (LBD), and a hinge domain between DBD and LBD. Functions for each domain are also listed. B) General model for transcriptional activation and repression in presence of agonist and antagonist. Upon agonist binding, heat shock protein (HSP) and corepressor are dissociated from receptor. Conformational changes occur in the LBD during coactivator recruitment, then activation complexes are formed with other cofactor proteins to turn on target gene expression. Antagonism of NRs is complex and not completely understood. Here we present one known silencing mechanisms associated with antagonist binding. Antagonist binding may induce a difference conformation of LBD, therefore prohibiting coactivator binding or promoting the recruitment of corepressors. It is important to note many NRs work as homo- or heterodimers (and possibly higher order multimers). The monomer is displayed here for simplicity.

Figure 1

Domain structures of NRs and mechanism of action upon ligand binding. A) The NR superfamily shares a common domain structure consisting of a NH₂-terminal domain (NTD), a central DNA-binding domain (DBD), a carboxy-terminal ligand-binding domain (LBD), and a hinge domain between DBD and LBD. Functions for each domain are also listed. B) General model for transcriptional activation and repression in presence of agonist and antagonist. Upon agonist binding, heat shock protein (HSP) and corepressor are dissociated from receptor. Conformational changes occur in the LBD during coactivator recruitment, then activation complexes are formed with other cofactor proteins to turn on target gene expression. Antagonism of NRs is complex and not completely understood. Here we present one known silencing mechanisms associated with antagonist binding. Antagonist binding may induce a difference conformation of LBD, therefore prohibiting coactivator binding or promoting the recruitment of corepressors. It is important to note many NRs work as homo- or heterodimers (and possibly higher order multimers). The monomer is displayed here for simplicity.

Figure 2

Functional sites on the LBD of NRs. The ER is used as an example. The ligand binding pocket is shown as a green surface. The cofactor protein binding site is colored in orange. The computationally identified possible steroid receptor-specific functional site is shown with a yellow surface. The helices of ER are rendered by a ribbon representation and colored in magenta, while the coactivator motif is in white.

Figure 3

A–D). 3-D agonist pharmacophores derived from different training sets [51,70] E). Antagonist pharmacophore [70,86]. All pharmacophores were generated with Catalyst (Accelrys, San Diego). Pharmacophore features represent: green = hydrogen bond acceptor; cyan = hydrophobic, Orange = ring hydrophobic and Purple = hydrogen bond donor.

Figure 4

A Virtual Screening scheme for NRs. First, the corporate or commercial databases need to undergo prescreening processing that incorporates input file formatting, pre-filtering for the drug-like compounds, exploring the conformational space of compounds, as well as their protonation, tautomeric, and stereochemical stages. Studies have identified the “hidden” impact of database preprocessing on VS results [91]. It is common that the number of screening compounds in the database can reach thousands to millions. Since docking/scoring is the most CPU-intensive and rate-limiting step, it is useful to apply certain prescreening filters to speed up the process. Generally these filters can be derived from structural and activities information from known modulators based on pharmacophore modeling and structural similarity. Structural information about the target receptor can be extracted from resolved X-ray crystallography structures or homology modeling. Abundant knowledge about ligand-protein interactions can be utilized to customize or fine tune important parameters for the docking/scoring step of VS. The list of hits will be then post-processed by target-specific filters. This step tries to increase the retrieval rate of the active compounds from the whole database and minimize the occurrence of false positives. Finally selected hits that pass through all requirements are suggested to be evaluated biologically.

Figure 5

Structural drawings of some computationally discovered NR modulators using identifiers in the original references. These compounds bind to the LBP of the corresponding NRs.

Figure 6

Structural drawings of some computationally discovered NR antagonists using identifiers in the original references. These compounds bind to the alternative site on the surface of NRs.

Figure 7

Ligand efficiency versus heavy atom count for PXR antagonists which highlights the relative positions of the three compounds of interest. When we consider the ligand efficiency (logK_i/heavy atom count (no hydrogens) versus heavy atom count, there is an exponential decrease in efficiency between 10–20 heavy atoms, which is comparable with observations for much larger datasets across different targets [87].