Dynamic allosteric communication pathway directing differential activation of the glucocorticoid receptor

Insights into mechanisms of MALT1 allostery from NMR and AlphaFold dynamic analyses

Mucosa-associated lymphoid tissue lymphoma-translocation protein 1 (MALT1) is an attractive target for the development of modulatory compounds in the treatment of lymphoma and other cancers. While the three-dimensional structure of MALT1 has been previously determined through X-ray analysis, its dynamic behaviour in solution has remained unexplored. We present here dynamic analyses of the apo MALT1 form along with the E549A mutation. This investigation used NMR 15N relaxation and NOE measurements between side-chain methyl groups. Our findings confirm that MALT1 exists as a monomer in solution, and demonstrate that the domains display semi-independent movements in relation to each other. Our dynamic study, covering multiple time scales, along with the assessment of conformational populations by Molecular Dynamic simulations, Alpha Fold modelling and PCA analysis, put the side chain of residue W580 in an inward position, shedding light at potential mechanisms underlying the allosteric regulation of this enzyme.

NMR tools to detect protein allostery

Allostery is a fundamental mechanism of cellular homeostasis by intra-protein communication between distinct functional sites. It is an internal process of proteins to steer interactions not only with each other but also with other biomolecules such as ligands, lipids, and nucleic acids. In addition, allosteric regulation is particularly important in enzymatic activities. A major challenge in structural and molecular biology today is unraveling allosteric sites in proteins, to elucidate the detailed mechanism of allostery and the development of allosteric drugs. Here we summarize the recently developed tools and approaches which enable the elucidation of regulatory hotspots and correlated motion in biomolecules, focusing primarily on solution-state nuclear magnetic resonance spectroscopy (NMR). These tools open an avenue towards a rational understanding of the mechanism of allostery and provide essential information for the design of allosteric drugs.

Glucocorticoid receptor signaling: intricacies and therapeutic opportunities

The glucocorticoid receptor (GR) is a major nuclear receptor (NR) drug target for the treatment of inflammatory disorders and several cancers. Despite the effectiveness of GR ligands, their systemic action triggers a plethora of side effects, limiting long-term use. Here, we discuss new concepts of and insights into GR mechanisms of action to assist in the identification of routes toward enhanced therapeutic benefits. We zoom in on the communication between different GR domains and how this is influenced by different ligands. We detail findings on the interaction between GR and chromatin, and highlight how condensate formation and coregulator confinement can perturb GR transcriptional responses. Last, we discuss the potential of novel ligands and the therapeutic exploitation of crosstalk with other NRs.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of cooperativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semiquantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different rescuabilities observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same noninducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscores the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions and therefore provides quantitative guidance to allostery modulation for therapeutic and engineering applications.

Glycan Shape, Motions, and Interactions Explored by NMR Spectroscopy

Glycans in the form of oligosaccharides, polysaccharides, and glycoconjugates are ubiquitous in nature, and their structures range from linear assemblies to highly branched and decorated constructs. Solution state NMR spectroscopy facilitates elucidation of preferred conformations and shapes of the saccharides, motions, and dynamic aspects related to processes over time as well as the study of transient interactions with proteins. Identification of intermolecular networks at the atomic level of detail in recognition events by carbohydrate-binding proteins known as lectins, unraveling interactions with antibodies, and revealing substrate scope and action of glycosyl transferases employed for synthesis of oligo- and polysaccharides may efficiently be analyzed by NMR spectroscopy. By utilizing NMR active nuclei present in glycans and derivatives thereof, including isotopically enriched compounds, highly detailed information can be obtained by the experiments. Subsequent analysis may be aided by quantum chemical calculations of NMR parameters, machine learning-based methodologies and artificial intelligence. Interpretation of the results from NMR experiments can be complemented by extensive molecular dynamics simulations to obtain three-dimensional dynamic models, thereby clarifying molecular recognition processes involving the glycans.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of co-operativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semi-quantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different degrees of rescuability observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same non-inducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscore the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions, and therefore provide quantitative guidance to allostery modulation for therapeutic and engineering applications.

Biomolecular NMR spectroscopy in the era of artificial intelligence

Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts.

Protein conformational ensembles in function: roles and mechanisms

The sequence-structure-function paradigm has dominated twentieth century molecular biology. The paradigm tacitly stipulated that for each sequence there exists a single, well-organized protein structure. Yet, to sustain cell life, function requires (i) that there be more than a single structure, (ii) that there be switching between the structures, and (iii) that the structures be incompletely organized. These fundamental tenets called for an updated sequence-conformational ensemble-function paradigm. The powerful energy landscape idea, which is the foundation of modernized molecular biology, imported the conformational ensemble framework from physics and chemistry. This framework embraces the recognition that proteins are dynamic and are always interconverting between conformational states with varying energies. The more stable the conformation the more populated it is. The changes in the populations of the states are required for cell life. As an example, in vivo, under physiological conditions, wild type kinases commonly populate their more stable "closed", inactive, conformations. However, there are minor populations of the "open", ligand-free states. Upon their stabilization, e.g., by high affinity interactions or mutations, their ensembles shift to occupy the active states. Here we discuss the role of conformational propensities in function. We provide multiple examples of diverse systems, including protein kinases, lipid kinases, and Ras GTPases, discuss diverse conformational mechanisms, and provide a broad outlook on protein ensembles in the cell. We propose that the number of molecules in the active state (inactive for repressors), determine protein function, and that the dynamic, relative conformational propensities, rather than the rigid structures, are the hallmark of cell life.

Biased regulation of glucocorticoid receptors signaling

Glucocorticoids (GCs), steroid hormones that depend on glucocorticoid receptor (GR) binding for their action, are essential for regulating numerous homeostatic functions in the body.GR signals are biased, that is, GR signals are various in different tissue cells, disease states and ligands. This biased regulation of GR signaling appears to depend on ligand-induced metameric regulation, protein post-translational modifications, assembly at response elements, context-specific assembly (recruitment of co-regulators) and intercellular differences. Based on the bias regulation of GR, selective GR agonists and modulators (SEGRAMs) were developed to bias therapeutic outcomes toward expected outcomes (e.g., anti-inflammation and immunoregulation) by influencing GR-mediated gene expression. This paper provides a review of the bias regulation and mechanism of GR and the research progress of drugs.

Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

The Biologist's Guide to the Glucocorticoid Receptor's Structure

The glucocorticoid receptor α (GRα) is a member of the nuclear receptor superfamily and functions as a glucocorticoid (GC)-responsive transcription factor. GR can halt inflammation and kill off cancer cells, thus explaining the widespread use of glucocorticoids in the clinic. However, side effects and therapy resistance limit GR's therapeutic potential, emphasizing the importance of resolving all of GR's context-specific action mechanisms. Fortunately, the understanding of GR structure, conformation, and stoichiometry in the different GR-controlled biological pathways is now gradually increasing. This information will be crucial to close knowledge gaps on GR function. In this review, we focus on the various domains and mechanisms of action of GR, all from a structural perspective.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

Quaternary glucocorticoid receptor structure highlights allosteric interdomain communication

The glucocorticoid receptor (GR) is a ligand-activated transcription factor that binds DNA and assembles co-regulator complexes to regulate gene transcription. GR agonists are widely prescribed to people with inflammatory and autoimmune diseases. Here we present high-resolution, multidomain structures of GR in complex with ligand, DNA and co-regulator peptide. The structures reveal how the receptor forms an asymmetric dimer on the DNA and provide a detailed view of the domain interactions within and across the two monomers. Hydrogen-deuterium exchange and DNA-binding experiments demonstrate that ligand-dependent structural changes are communicated across the different domains in the full-length receptor. This study demonstrates how GR forms a distinct architecture on DNA and how signal transmission can be modulated by the ligand pharmacophore, provides a platform to build a new level of understanding of how receptor modifications can drive disease progression and offers key insight for future drug design.

Structural basis of glucocorticoid receptor signaling bias

Dissociation between the healthy and toxic effects of cortisol, a major stress-responding hormone has been a widely used strategy to develop anti-inflammatory glucocorticoids with fewer side effects. Such strategy falls short when treating brain disorders as timing and activity state within large-scale neuronal networks determine the physiological and behavioral specificity of cortisol response. Advances in structural molecular dynamics posit the bases for engineering glucocorticoids with precision bias for select downstream signaling pathways. Design of allosteric and/or cooperative control for the glucocorticoid receptor could help promote the beneficial and reduce the deleterious effects of cortisol on brain and behavior in disease conditions.

Probing conformational landscapes and mechanisms of allosteric communication in the functional states of the ABL kinase domain using multiscale simulations and network-based mutational profiling of allosteric residue potentials

In the current study, multiscale simulation approaches and dynamic network methods are employed to examine the dynamic and energetic details of conformational landscapes and allosteric interactions in the ABL kinase domain that determine the kinase functions. Using a plethora of synergistic computational approaches, we elucidate how conformational transitions between the active and inactive ABL states can employ allosteric regulatory switches to modulate intramolecular communication networks between the ATP site, the substrate binding region, and the allosteric binding pocket. A perturbation-based network approach that implements mutational profiling of allosteric residue propensities and communications in the ABL states is proposed. Consistent with biophysical experiments, the results reveal functionally significant shifts of the allosteric interaction networks in which preferential communication paths between the ATP binding site and substrate regions in the active ABL state become suppressed in the closed inactive ABL form, which in turn features favorable allosteric coupling between the ATP site and the allosteric binding pocket. By integrating the results of atomistic simulations with dimensionality reduction methods and Markov state models, we analyze the mechanistic role of macrostates and characterize kinetic transitions between the ABL conformational states. Using network-based mutational scanning of allosteric residue propensities, this study provides a comprehensive computational analysis of long-range communications in the ABL kinase domain and identifies conserved regulatory hotspots that modulate kinase activity and allosteric crosstalk between the allosteric pocket, ATP binding site, and substrate binding regions.

Interactions governing transcriptional activity of nuclear receptors

The key players in transcriptional regulation are transcription factors (TFs), proteins that bind specific DNA sequences. Several mechanisms exist to turn TFs ‘on’ and ‘off’, including ligand binding which induces conformational changes within TFs, subsequently influencing multiple inter- and intramolecular interactions to drive transcriptional responses. Nuclear receptors are a specific family of ligand-regulated TFs whose activity relies on interactions with DNA, coregulator proteins and other receptors. These multidomain proteins also undergo interdomain interactions on multiple levels, further modulating transcriptional outputs. Cooperation between these distinct interactions is critical for appropriate transcription and remains an intense area of investigation. In this review, we report and summarize recent findings that continue to advance our mechanistic understanding of how interactions between nuclear receptors and diverse partners influence transcription.

α1-Antitrypsin Binds to the Glucocorticoid Receptor with Anti-Inflammatory and Antimycobacterial Significance in Macrophages

α1-Antitrypsin (AAT), a serine protease inhibitor, is the third most abundant protein in plasma. Although the best-known function of AAT is irreversible inhibition of elastase, AAT is an acute-phase reactant and is increasingly recognized to have a panoply of other functions, including as an anti-inflammatory mediator and a host-protective molecule against various pathogens. Although a canonical receptor for AAT has not been identified, AAT can be internalized into the cytoplasm and is known to affect gene regulation. Because AAT has anti-inflammatory properties, we examined whether AAT binds the cytoplasmic glucocorticoid receptor (GR) in human macrophages. We report the finding that AAT binds to GR using several approaches, including coimmunoprecipitation, mass spectrometry, and microscale thermophoresis. We also performed in silico molecular modeling and found that binding between AAT and GR has a plausible stereochemical basis. The significance of this interaction in macrophages is evinced by AAT inhibition of LPS-induced NF-κB activation and IL-8 production as well as AAT induction of angiopoietin-like 4 protein, which are, in part, dependent on GR. Furthermore, this AAT-GR interaction contributes to a host-protective role against mycobacteria in macrophages. In summary, this study identifies a new mechanism for the gene regulation, anti-inflammatory, and host-defense properties of AAT.

The Role of Conformational Dynamics and Allostery in the Control of Distinct Efficacies of Agonists to the Glucocorticoid Receptor

Glucocorticoid receptor (GR) regulates various cellular functions. Given its broad influence on metabolic activities, it has been the target of drug discovery for decades. However, how drugs induce conformational changes in GR has remained elusive. Herein, we used five GR agonists (dex, AZ938, pred, cor, and dibC) with different efficacies to investigate which aspect of the ligand induced the differences in efficacy. We performed molecular dynamics simulations on the five systems (dex-, AZ938-, pred-, cor-, and dibC-bound systems) and observed a distinct discrepancy in the conformation of the cofactor TIF2. Moreover, we discovered ligand-induced differences regarding the level of conformational changes posed by the binding of cofactor TIF2 and identified a pair of essential residues D590 and T39. We further found a positive correlation between the efficacies of ligands and the interaction of the two binding pockets’ domains, where D590 and T739 were involved, implying their significance in the participation of allosteric communication. Using community network analysis, two essential communities containing D590 and T739 were identified with their connectivity correlating to the efficacy of ligands. The potential communication pathways between these two residues were revealed. These results revealed the underlying mechanism of allosteric communication between the ligand-binding and cofactor-binding pockets and identified a pair of important residues in the allosteric communication pathway, which can serve as a guide for future drug discovery.

Discovery of novel non-steroidal selective glucocorticoid receptor modulators by structure- and IGN-based virtual screening, structural optimization, and biological evaluation

Glucocorticoids (GCs) are the most commonly used anti-inflammatory drugs. However, their excellent therapeutic effects are often accompanied by undesirable side effects. To discover selective glucocorticoid receptor modulators (SGRMs) that preferentially induce transrepression with little or no transactivation activity, a structure-based virtual screening by combining molecular docking and InteractionGraphNet (IGN) rescoring was performed, and compound HP210 was identified. HP210 did not induce the transactivation functions of GR while still acted on the NF-κB mediated tethered transrepression function (IC₅₀ = 2.32 μM), and suppressed the secretion of pro-inflammation cytokines IL-1β and IL-6. Compared with dexamethasone, HP210 showed no cross activities with phylogenetically related mineralcorticoid receptor and progesterone receptor and no significant effect on osteoprotegerin, exhibiting a reduced side-effect profile. Then, guided by the molecular dynamics simulations and binding free energy calculations, compound HP210_b4 with over two-fold higher transrepression activity (IC₅₀ = 0.99 μM) was discovered. This study reported a group of non-steroidal new-scaffold SGRMs, providing valuable clues for the development of novel anti-inflammatory drugs.

Design and engineering of allosteric communications in proteins

Allostery in proteins plays an important role in regulating protein activities and influencing many biological processes such as gene expression, enzyme catalysis, and cell signaling. The process of allostery takes place when a signal detected at a site on a protein is transmitted via a mechanical pathway to a functional site and, thus, influences its activity. The pathway of allosteric communication consists of amino acids that form a network with covalent and non-covalent bonds. By mutating residues in this allosteric network, protein engineers have successfully established novel allosteric pathways to achieve desired properties in the target protein. In this review, we highlight the most recent and state-of-the-art techniques for allosteric communication engineering. We also discuss the challenges that need to be overcome and future directions for engineering protein allostery.

Discovery of Novel GR Ligands toward Druggable GR Antagonist Conformations Identified by MD Simulations and Markov State Model Analysis

Binding of different ligands to glucocorticoid receptor (GR) may induce different conformational changes and even trigger completely opposite biological functions. To understand the allosteric communication within the GR ligand binding domain, the folding pathway of helix 12 (H12) induced by the binding of the agonist dexamethasone (DEX), antagonist RU486, and modulator AZD9567 are explored by molecular dynamics simulations and Markov state model analysis. The ligands can regulate the volume of the activation function-2 through the residues Phe737 and Gln738. Without ligand or with agonist binding, H12 swings from inward to outward to visit different folding positions. However, the binding of RU486 or AZD9567 perturbs the structural state, and the passive antagonist state appears more stable. Structure-based virtual screening and in vitro bioassays are used to discover novel GR ligands that bias the conformation equilibria toward the passive antagonist state. HP-19 exhibits the best anti-inflammatory activity (IC50 = 0.041 ± 0.011 µm) in nuclear factor-kappa B signaling pathway, which is comparable to that of DEX. HP-19 also does not induce adverse effect-related transactivation functions of GR. The novel ligands discovered here may serve as promising starting points for the development of GR modulators.

Cooperativity as quantification and optimization paradigm for nuclear receptor modulators

A cooperativity framework describes the formation of nuclear receptor ternary complexes and deconvolutes ligand and cofactor binding into intrinsic affinities and a cooperativity factor, providing a conceptually new understanding of NR modulation.

Client binding shifts the populations of dynamic Hsp90 conformations through an allosteric network

Hsp90 is a molecular chaperone that interacts with a specific set of client proteins and assists their folding. The underlying molecular mechanisms, involving dynamic transitions between open and closed conformations, are still enigmatic. Combining nuclear magnetic resonance, small-angle x-ray scattering, and biochemical experiments, we have identified a key intermediate state of Hsp90 induced by adenosine triphosphate (ATP) binding, in which rotation of the Hsp90 N-terminal domain (NTD) yields a domain arrangement poised for closing. This ATP-stabilized NTD rotation is allosterically communicated across the full Hsp90 dimer, affecting distant client sites. By analyzing the interactions of four distinct clients, i.e., steroid hormone receptors (glucocorticoid receptor and mineralocorticoid receptor), p53, and Tau, we show that client-specific interactions with Hsp90 select and enhance the NTD-rotated state and promote closing of the full-length Hsp90 dimer. The p23 co-chaperone shifts the population of Hsp90 toward the closed state, thereby enhancing client interaction and processing.

Folding Intermediates, Heterogeneous Native Ensembles and Protein Function

Single domain proteins fold via diverse mechanisms emphasizing the intricate relationship between energetics and structure, which is a direct consequence of functional constraints and demands imposed at the level of sequence. On the other hand, elucidating the interplay between folding mechanisms and function is challenging in large proteins, given the inherent shortcomings in identifying metastable states experimentally and the sampling limitations associated with computational methods. Here, we show that free energy profiles and surfaces of large systems (>150 residues), as predicted by a statistical mechanical model, display a wide array of folding mechanisms with ubiquitous folding intermediates and heterogeneous native ensembles. Importantly, residues around the ligand binding or enzyme active site display a larger tendency to partially unfold and this manifests as intermediates or excited states along the folding coordinate in ligand binding domains, transcription repressors, and representative enzymes from all the six classes, including the SARS-CoV-2 receptor binding domain (RBD) of the spike protein and the protease M^pro. It thus appears that it is relatively easier to distill the imprints of function on the folding landscape of larger proteins as opposed to smaller systems. We discuss how an understanding of energetic-entropic features in ordered proteins can pinpoint specific avenues through which folding mechanisms, populations of partially structured states and function can be engineered.

Signaling in the crowded cell

High-resolution technologies have clarified some of the principles underlying cellular actions. However, understanding how cells receive, communicate, and respond to signals is still challenging. Questions include how efficient regulation of assemblies, which execute cell actions at the nanoscales, transmits productively at micrometer scales, especially considering the crowded environment, and how the cell organization makes it happen. Here, we describe how cells can navigate long-range diffusion-controlled signaling via association/dissociation of spatially proximal entities. Dynamic clusters can span the cell, engaging in most signaling steps. Effective local concentration, allostery, scaffolding, affinities, and the chemical and mechanical properties of the macromolecules and the environment play key roles. Signaling strength and duration matter, for example, deciding if a mutation promotes cancer or developmental syndromes.

A hierarchy of coupling free energies underlie the thermodynamic and functional architecture of protein structures

Protein sequences and structures evolve by satisfying varied physical and biochemical constraints. This multi-level selection is enabled not just by the patterning of amino acids on the sequence, but also via coupling between residues in the native structure. Here, we employ an energetically detailed statistical mechanical model with millions of microstates to extract such long-range structural correlations, i.e. thermodynamic coupling free energies, from a diverse family of protein structures. We find that despite the intricate and anisotropic distribution of coupling patterns, the majority of residues (>70%) are only marginally coupled contributing to functional motions and catalysis. Physical origins of ‘sectors’, determinants of native ensemble heterogeneity in extant, ancient and designed proteins, and the basis for allostery emerge naturally from coupling free energies. The statistical framework highlights how evolutionary selection and optimization occur at the level of global interaction network for a given protein fold impacting folding, function, and allosteric outputs.

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Evolution of protein structures occurs at the level of global interaction network.

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More than 70% of the protein residues are weakly or marginally coupled.

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Functional ‘sector’ regions are a manifestation of marginal coupling.

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Coupling indices vary across the entire proteins in extant-ancient and natural-designed pairs.

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The proposed methodology can be used to understand allostery and epistasis.

Unraveling the Allosteric Cross-Talk between the Coactivator Peptide and the Ligand-Binding Site in the Glucocorticoid Receptor

The glucocorticoid receptor (GR) is a nuclear receptor that controls critical biological processes by regulating the transcription of specific genes. There is a known allosteric cross-talk between the ligand and coregulator binding sites within the GR ligand-binding domain that is crucial for the control of the functional response. However, the molecular mechanisms underlying such an allosteric control remain elusive. Here, molecular dynamics (MD) simulations, bioinformatic analysis, and biophysical measurements are integrated to capture the structural and dynamic features of the allosteric cross-talk within the GR. We identified a network of evolutionarily conserved residues that enables the allosteric signal transduction, in agreement with experimental data. MD simulations clarify how such a network is dynamically interconnected and offer a mechanistic explanation of how different peptides affect the intensity of the allosteric signal. This study provides useful insights to elucidate the GR allosteric regulation, ultimately providing a foundation for designing novel drugs.

Diverse Native Ensembles Dictate the Differential Functional Responses of Nuclear Receptor Ligand-Binding Domains

Native states of folded proteins are characterized by a large ensemble of conformations whose relative populations and interconversion dynamics determine the functional output. This is more apparent in transcription factors that have evolved to be inherently sensitive to small perturbations, thus fine-tuning gene expression. To explore the extent to which such functional features are imprinted on the folding landscape of transcription factor ligand-binding domains (LBDs), we characterize paralogous LBDs of the nuclear receptor (NR) family employing an energetically detailed and ensemble-based Ising-like statistical mechanical model. We find that the native ensembles of the LBDs from glucocorticoid receptor, PPAγ, and thyroid hormone receptor display a remarkable diversity in the width of the native wells, the number and nature of partially structured states, and hence the degree of conformational order. Monte Carlo simulations employing the full state representation of the ensemble highlight that many of the functional conformations coexist in equilibrium, whose relative populations are sensitive to both temperature and the strength of ligand binding. Allosteric modulation of the degree of structure at a coregulator binding site on ligand binding is shown to arise via a redistribution of populations in the native ensembles of glucocorticoid and PPAγ LBDs. Our results illustrate how functional requirements can drive the evolution of conformationally diverse native ensembles in paralogs.

Structural mechanism underlying ligand binding and activation of PPARγ

Ligands bind to an occluded orthosteric ligand-binding pocket within the nuclear receptor ligand-binding domain. Molecular simulations have revealed theoretical ligand entry/exit pathways to the orthosteric pocket; however, it remains unclear whether ligand binding proceeds through induced fit or conformational selection mechanisms. Here, using nuclear magnetic resonance spectroscopy, isothermal titration calorimetry, and surface plasmon resonance analysis, we provide evidence that structurally distinct agonists bind peroxisome proliferator-activated receptor γ (PPARγ) via a two-step induced fit mechanism involving an initial fast kinetic step followed by a slow conformational change. The agonist encounter complex binding pose is suggested in crystal structures where ligands bind to a surface pore suggested as a ligand entry site in molecular simulations. Our findings suggest an activation mechanism for PPARγ whereby agonist binding occurs through an initial encounter complex followed by a transition of the ligand into the final binding pose within the orthosteric pocket, inducing a transcriptionally active conformation.

A Methionine Chemical Shift Based Order Parameter Characterizing Global Protein Dynamics

Coupling of side chain dynamics over long distances is an important component of allostery. Methionine side chains show the largest intrinsic flexibility among methyl-containing residues but the actual degree of conformational averaging depends on the proximity and mobility of neighboring residues. The ¹³ C NMR chemical shifts of the methyl groups of methionine residues located at long distances in the same protein show a similar scaling with respect to the values predicted from the static X-ray structure by quantum methods. This results in a good linear correlation between calculated and observed chemical shifts. The slope is protein dependent and ranges from zero for the highly flexible calmodulin to 0.7 for the much more rigid calcineurin catalytic domain. The linear correlation is indicative of a similar level of side-chain conformational averaging over long distances, and the slope of the correlation line can be interpreted as an order parameter of the global side-chain flexibility.