Statistical Analysis of Protein-Ligand Interaction Patterns in Nuclear Receptor RORγ.

The receptor RORγ belongs to the nuclear receptor superfamily that senses small signaling molecules and regulates at the gene transcription level. Since RORγ has a high basal activity and plays an important role in immune responses, inhibitors targeting this receptor have been a focus for many studies. The receptor-ligand interaction is complex, and often subtle differences in ligand structure can determine its role as an inverse agonist or an agonist. We examined more than 130 existing RORγ crystal structures that have the same receptor complexed with different ligands. We reported the features of receptor-ligand interaction patterns and the differences between agonist and inverse agonist binding. Specific changes in the contact interaction map are identified to distinguish active and inactive conformations. Further statistical analysis of the contact interaction patterns using principal component analysis reveals a dominant mode which separates allosteric binding vs. canonical binding and a second mode which may indicate active vs. inactive structures. We also studied the nature of constitutive activity by performing a 100-ns computer simulation of apo RORγ. Using constitutively active nuclear receptor CAR as a comparison, we identified a group of conserved contacts that have similar contact strength between the two receptors. These conserved contact interactions, especially a couple key contacts in H11-H12 interaction, can be considered essential to the constitutive activity of RORγ. These protein-ligand and internal protein contact interactions can be useful in the development of new drugs that direct receptor activity.

Protein conformational switch discerned via network centrality properties.

Network analysis has emerged as a powerful tool for examining structural biology systems. The spatial organization of the components of a biomolecular structure has been rendered as a graph representation and analyses have been performed to deduce the biophysical and mechanistic properties of these components. For proteins, the analysis of protein structure networks (PSNs), especially via network centrality measurements and cluster coefficients, has led to identifying amino acid residues that play key functional roles and classifying amino acid residues in general. Whether these network properties examined in various studies are sensitive to subtle (yet biologically significant) conformational changes remained to be addressed. Here, we focused on four types of network centrality properties (betweenness, closeness, degree, and eigenvector centralities) for conformational changes upon ligand binding of a sensor protein (constitutive androstane receptor) and an allosteric enzyme (ribonucleotide reductase). We found that eigenvector centrality is sensitive and can distinguish salient structural features between protein conformational states while other centrality measures, especially closeness centrality, are less sensitive and rather generic with respect to the structural specificity. We also demonstrated that an ensemble-informed, modified PSN with static edges removed (which we term PSN*) has enhanced sensitivity at discerning structural changes.

Regulatory Mechanics of Constitutive Androstane Receptors: Basal and Ligand-Directed Actions.

Constitutive androstane receptor (CAR) is a nuclear hormone receptor that primarily functions in sensing and metabolizing xenobiotics. The basal activity of this receptor is relatively high, and CAR is deemed active in the absence of ligand. The (over)activation can promote drug toxicity and tumor growth. Thus, therapeutic treatments seek inverse agonists to inhibit or modulate CAR activities. To advance our understanding of the regulatory mechanisms of CAR, we used computational and experimental approaches to elucidate three aspects of CAR activation and inactivation: (1) ligand-dependent actions, (2) ligand-orthologue specificity, and (3) constitutive activity. For ligand-dependent actions, we examined the ligand-bound simulations and identified two sets of ligand-induced contacts promoting CAR activation via coactivator binding (H11-H12 contact) or inactivation via corepressor binding (H4-H11 contact). For orthologue specificity, we addressed a puzzling fact that murine CAR (mCAR) and human CAR (hCAR) respond differently to the same ligand (CITCO), despite their high sequence homology. We found that the helix H7 of hCAR is responsible for a stronger binding of the ligand CITCO compared to mCAR, hence a stronger CITCO-induced activation. For basal activity, we reported computer-generated unliganded CAR structures and critical mutagenesis (mCAR's V209A and N333D) results of a cell-based transcription assay. Our results reveal that the basal conformation of CAR shares prominent features with the agonist-bound form, and helix HX has an important contribution to the constitutive activity. These findings altogether can be useful for the understanding of constitutively active receptors and the design of drug molecules targeting them.

Effector-Binding-Directed Dimerization and Dynamic Communication between Allosteric Sites of Ribonucleotide Reductase.

Proteins forming dimers or larger complexes can be strongly influenced by their effector-binding status. We investigated how the effector-binding event is coupled with interface formation via computer simulations, and we quantified the correlation of two types of contact interactions: between the effector and its binding pocket and between protein monomers. This was achieved by connecting the protein dynamics at the monomeric level with the oligomer interface information. We applied this method to ribonucleotide reductase (RNR), an essential enzyme for de novo DNA synthesis. RNR contains two important allosteric sites, the s-site (specificity site) and the a-site (activity site), which bind different effectors. We studied these different binding states with atomistic simulation and used their coarse-grained contact information to analyze the protein dynamics. The results reveal that the effector-protein dynamics at the s-site and dimer interface formation are positively coupled. We further quantify the resonance level between these two events, which can be applied to other similar systems. At the a-site, different effector-binding states (ATP vs dATP) drastically alter the protein dynamics and affect the activity of the enzyme. On the basis of these results, we propose a new mechanism of how the a-site regulates enzyme activation.

Characterizing the 3D structure and dynamics of chromosomes and proteins in a common contact matrix framework.

Conformational ensembles of biopolymers, whether proteins or chromosomes, can be described using contact matrices. Principal component analysis (PCA) on the contact data has been used to interrogate both protein and chromosome structures and/or dynamics. However, as these fields have developed separately, variants of PCA have emerged. Previously, a variant we hereby term Implicit-PCA (I-PCA) has been applied to chromosome contact matrices and revealed the spatial segregation of active and inactive chromatin. Separately, Explicit-PCA (E-PCA) has previously been applied to proteins and characterized their correlated structure fluctuations. Here, we swapped analysis methods (I-PCA and E-PCA), applying each to a different biopolymer type (chromosome or protein) than the one for which they were initially developed. We find that applying E-PCA to chromosome distance matrices derived from microscopy data can reveal the dominant motion (concerted fluctuation) of these chromosomes. Further, by applying E-PCA to Hi-C data across the human blood cell lineage, we isolated the aspects of chromosome structure that most strongly differentiate cell types. Conversely, when we applied I-PCA to simulation snapshots of proteins, the major component reported the consensus features of the structure, making this a promising approach for future analysis of semi-structured proteins.