Quantifying Correlations Between Allosteric Sites in Thermodynamic Ensembles

Dynamic allostery-based molecular workings of kinase:peptide complexes

A dense interplay between structure and dynamics underlies the working of proteins, especially enzymes. Protein kinases are molecular switches that are optimized for their regulation rather than catalytic turnover rates. Using long-simulations dynamic allostery analysis, this study describes an exploration of the dynamic kinase:peptide complex. We have used protein kinase A (PKA) as a model system as a generic prototype of the protein kinase superfamily of signaling enzymes. Our results explain the role of dynamic coupling of active-site residues that must work in coherence to provide for a successful activation or inhibition response from the kinase. Amino acid networks-based community analysis allows us to ponder the conformational entropy of the kinase:nucleotide:peptide ternary complex. We use a combination of 7 peptides that include 3 types of PKA-binding partners: Substrates, products, and inhibitors. The substrate peptides provide for dynamic insights into the enzyme:substrate complex, while the product phospho-peptide allows for accessing modes of enzyme:product release. Mapping of allosteric communities onto the PKA structure allows us to locate the more unvarying and flexible dynamic regions of the kinase. These distributions, when correlated with the structural elements of the kinase core, allow for a detailed exploration of key dynamics-based signatures that could affect peptide recognition and binding at the kinase active site. These studies provide a unique dynamic allostery-based perspective to kinase:peptide complexes that have previously been explored only in a structural or thermodynamic context.

Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

BACKGROUND

Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks.

METHODS

In this work, we performed a large scale comprehensive and multi-faceted analysis of >300 diverse allosteric proteins and complexes with allosteric modulators. By modeling and exploring coarse-grained dynamics, residue coevolution, and residue interaction networks for allosteric proteins, we have determined unifying molecular signatures shared by allosteric systems.

RESULTS

The results of this study have suggested that allosteric inhibitors and allosteric activators may differentially affect global dynamics and network organization of protein systems, leading to diverse allosteric mechanisms. By using structural and functional data on protein kinases, we present a detailed case study that that included atomic-level analysis of coevolutionary networks in kinases bound with allosteric inhibitors and activators.

CONCLUSIONS

We have found that coevolutionary networks can form direct communication pathways connecting functional regions and can recapitulate key regulatory sites and interactions responsible for allosteric signaling in the studied protein systems. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of known regulatory hotspots in protein kinases.

GENERAL SIGNIFICANCE

This study has shown that allosteric inhibitors and allosteric activators can have a different effect on residue interaction networks and can exploit distinct regulatory mechanisms, which could open up opportunities for probing allostery and new drug combinations with broad range of activities.

ANCA: Anharmonic Conformational Analysis of Biomolecular Simulations

Anharmonicity in time-dependent conformational fluctuations is noted to be a key feature of functional dynamics of biomolecules. Although anharmonic events are rare, long-timescale (μs-ms and beyond) simulations facilitate probing of such events. We have previously developed quasi-anharmonic analysis to resolve higher-order spatial correlations and characterize anharmonicity in biomolecular simulations. In this article, we have extended this toolbox to resolve higher-order temporal correlations and built a scalable Python package called anharmonic conformational analysis (ANCA). ANCA has modules to: 1) measure anharmonicity in the form of higher-order statistics and its variation as a function of time, 2) output a storyboard representation of the simulations to identify key anharmonic conformational events, and 3) identify putative anharmonic conformational substates and visualization of transitions between these substates.

Conformational coupling by trans-phosphorylation in calcium calmodulin dependent kinase II

The calcium calmodulin-dependent protein kinase II (CaMKII) is a dodecameric holoenzyme important for encoding memory. Its activation, triggered by binding of calcium-calmodulin, persists autonomously after calmodulin dissociation. One (receiver) kinase captures and subsequently phosphorylates the regulatory domain peptide of a donor kinase forming a chained dimer as the first stage of autonomous activation. Protein dynamics simulations examined the conformational changes triggered by dimer formation and phosphorylation, aimed to provide a molecular rationale for human mutations that result in learning disabilities. Ensembles generated from X-ray crystal structures were characterized by network centrality and community analysis. Mutual information related collective motions to local fragment dynamics encoded with a structural alphabet. Implicit solvent tCONCOORD conformational ensembles revealed the dynamic architecture of inactive kinase domains was co-opted in the activated dimer but the network hub shifted from the nucleotide binding cleft to the captured peptide. Explicit solvent molecular dynamics (MD) showed nucleotide and substrate binding determinants formed coupled nodes in long-range signal relays between regulatory peptides in the dimer. Strain in the extended captured peptide was balanced by reduced flexibility of the receiver kinase C-lobe core. The relays were organized around a hydrophobic patch between the captured peptide and a key binding helix. The human mutations aligned along the relays. Thus, these mutations could disrupt the allosteric network alternatively, or in addition, to altered binding affinities. Non-binding protein sectors distant from the binding sites mediated the allosteric signalling; providing possible targets for inhibitor design. Phosphorylation of the peptide modulated the dielectric of its binding pocket to strengthen the patch, non-binding sectors, domain interface and temporal correlations between parallel relays. These results provide the molecular details underlying the reported positive kinase cooperativity to enrich the discussion on how autonomous activation by phosphorylation leads to long-term behavioural effects.

Protein kinases play central roles in intracellular signalling. Auto-phosphorylation by bound nucleotide typically precedes phosphate transfer to multiple substrates. Protein conformational changes are central to kinase function, altering binding affinities to change cellular location and shunt from one signal pathway to another. In the brain, the multi-subunit kinase, CaMKII is activated by calcium-calmodulin upon calcium jumps produced by synaptic stimulation. Auto-transphosphorylation of a regulatory peptide enables the kinase to remain activated and mediate long-term behavioural effects after return to basal calcium levels. A database of mutated residues responsible for these effects is difficult to reconcile solely with impaired nucleotide or substrate binding. Therefore, we have computationally generated interaction networks to map the conformational plasticity of the kinase domains where most mutations localize. The network generated from the atomic structure of a phosphorylated dimer resolves protein sectors based on their collective motions. The sectors link nucleotide and substrate binding sites in self-reinforcing relays between regulatory peptides. The self-reinforcement is strengthened by phosphorylation consistent with the reported positive cooperativity of kinase activity with calcium-calmodulin concentration. The network gives a better match with the mutations and, in addition, reveals target sites for drug development.

Cosolvent-Enhanced Sampling and Unbiased Identification of Cryptic Pockets Suitable for Structure-Based Drug Design

Modulating protein activity with small-molecules binding to cryptic pockets offers great opportunities to overcome hurdles in drug design. Cryptic sites are atypical binding sites in proteins that are closed in the absence of a stabilizing ligand and are thus inherently difficult to identify. Many studies have proposed methods to predict cryptic sites. However, a general approach to prospectively sample open conformations of these sites and to identify cryptic pockets in an unbiased manner suitable for structure-based drug design remains elusive. Here, we describe an all-atom, explicit cosolvent, molecular dynamics (MD) simulations-based workflow to sample the open states of cryptic sites and identify opened pockets, in a manner that does not require a priori knowledge about these sites. Furthermore, the workflow relies on a target-independent parametrization that only distinguishes between binding pockets for peptides or small molecules. We validated our approach on a diverse test set of seven proteins with crystallographically determined cryptic sites. The known cryptic sites were found among the three highest-ranked predicted cryptic sites, and an open site conformation was sampled and selected for most of the systems. Crystallographic ligand poses were well reproduced by docking into these identified open conformations for five of the systems. When the fully open state could not be reproduced, we were still able to predict the location of the cryptic site, or identify other cryptic sites that could be retrospectively validated with knowledge of the protein target. These characteristics render our approach valuable for investigating novel protein targets without any prior information.

Peeking at G-protein-coupled receptors through the molecular dynamics keyhole

Molecular dynamics is a state of the art computational tool for the investigation of biophysics phenomenon at a molecular scale, as it enables the modeling of dynamic processes, such as conformational motions, molecular solvation and ligand binding. The recent advances in structural biology have led to a bloom in published G-protein-coupled receptor structures, representing a solid and valuable resource for molecular dynamics studies. During the last decade, indeed, a plethora of physiological and pharmacological facets of this membrane protein superfamily have been addressed by means of molecular dynamics simulations, including the activation mechanism, allosterism and, very recently, biased signaling. Here, we try to recapitulate some of the main contributions that molecular dynamics has recently produced in the field.

Enspara: Modeling molecular ensembles with scalable data structures and parallel computing

Markov state models (MSMs) are quantitative models of protein dynamics that are useful for uncovering the structural fluctuations that proteins undergo, as well as the mechanisms of these conformational changes. Given the enormity of conformational space, there has been ongoing interest in identifying a small number of states that capture the essential features of a protein. Generally, this is achieved by making assumptions about the properties of relevant features-for example, that the most important features are those that change slowly. An alternative strategy is to keep as many degrees of freedom as possible and subsequently learn from the model which of the features are most important. In these larger models, however, traditional approaches quickly become computationally intractable. In this paper, we present enspara, a library for working with MSMs that provides several novel algorithms and specialized data structures that dramatically improve the scalability of traditional MSM methods. This includes ragged arrays for minimizing memory requirements, message passing interface-parallelized implementations of compute-intensive operations, and a flexible framework for model construction and analysis.

Quantifying correlations between mutational sites in the catalytic subunit of γ-secretase

Presenilin 1 (PS1) is the catalytic subunit of the γ-secretase complex which is involved in the generation of amyloid-β peptides (Aβ). Single point mutations in PS1 alter the cleavage pattern of the amyloid precursor protein (APP) and lead to the formation of aberrant Aβ peptides. To date, more than two hundred mutations distributed among almost a third of PS1's amino acids have been associated to the development of Alzheimer's disease (AD). Nevertheless, the mechanism by which mutations far from the catalytic site alter the γ-secretase's cleavage pattern remains unclear. In this work we analyzed correlated motions between amino acids in the wild type (WT) enzyme and 13 γ-secretase mutant models employing a multi-scale molecular dynamics approach. The effect of the protonation state of key catalytic residue Asp385 on the correlation networks was also evaluated. We observed that the strength and number of correlations is highly influenced in all mutant models in both protonation state models. The biggest changes were observed in mutants I83T, W165G, H214Y and L435F; the latest has been proved to drastically reduce γ-secretase activity. Finally, we made a classification of the studied mutations according to their correlation networks with amino acids at: (1) the interfaces with the other γ-secretase components, (2) the catalytic site, (3) the substrate entry site and (4) the substrate recognition site. Overall, this work provides insight into the allosteric communication networks of PS1.

Journey to the center of the protein: allostery from multitemperature multiconformer X-ray crystallography

Proteins inherently fluctuate between conformations to perform functions in the cell. For example, they sample product-binding, transition-state-stabilizing and product-release states during catalysis, and they integrate signals from remote regions of the structure for allosteric regulation. However, there is a lack of understanding of how these dynamic processes occur at the basic atomic level. This gap can be at least partially addressed by combining variable-temperature (instead of traditional cryogenic temperature) X-ray crystallography with algorithms for modeling alternative conformations based on electron-density maps, in an approach called multitemperature multiconformer X-ray crystallography (MMX). Here, the use of MMX to reveal alternative conformations at different sites in a protein structure and to estimate the degree of energetic coupling between them is discussed. These insights can suggest testable hypotheses about allosteric mechanisms. Temperature is an easily manipulated experimental parameter, so the MMX approach is widely applicable to any protein that yields well diffracting crystals. Moreover, the general principles of MMX are extensible to other perturbations such as pH, pressure, ligand concentration etc. Future work will explore strategies for leveraging X-ray data across such perturbation series to more quantitatively measure how different parts of a protein structure are coupled to each other, and the consequences thereof for allostery and other aspects of protein function.

Catalytic Domains of Phosphodiesterase 5, 6, and 5/6 Chimera Display Differential Dynamics and Ligand Dissociation Energy Barriers

The enzyme phosphodiesterase 6 (PDE6) is a critical component of the visual signaling pathway and functions to convert cGMP to GMP. The ability of PDE6 to affect cellular cGMP levels leads to deactivation of cGMP-gated ion channels in both rod and cone cells. PDE6 has been difficult to structurally characterize experimentally, though the structures of the closely related PDE5 and a PDE5/6 chimera have been determined by X-ray crystallography. In this work, we employ a computational approach to study the dynamics of the catalytic domains of PDE6, PDE5, and the PDE5/6 chimera. Through equilibrium molecular dynamics (MD) simulations, we identify a region of PDE6 (α12) to be correlated to distal regions of the enzyme (H- and M-loops), which surround the catalytic center. These correlations are not observed for PDE5, and we speculate that these unique motions in PDE6 may relate to the high catalytic efficiency of PDE6 and the requirement for an endogenous inhibitory subunit (Pγ). We further investigate the ligand binding pathways and energetics by enhanced sampling simulations (metadynamics) using the inhibitor sildenafil and GMP. The energetics and pathways of ligand binding are consistent with the high efficiency of PDE6 and further implicate the α12 region as an important regulatory element for PDE6 functional dynamics.

Cooperative Changes in Solvent Exposure Identify Cryptic Pockets, Switches, and Allosteric Coupling

Proteins are dynamic molecules that undergo conformational changes to a broad spectrum of different excited states. Unfortunately, the small populations of these states make it difficult to determine their structures or functional implications. Computer simulations are an increasingly powerful means to identify and characterize functionally relevant excited states. However, this advance has uncovered a further challenge: it can be extremely difficult to identify the most salient features of large simulation data sets. We reasoned that many functionally relevant conformational changes are likely to involve large, cooperative changes to the surfaces that are available to interact with potential binding partners. To examine this hypothesis, we introduce a method that returns a prioritized list of potentially functional conformational changes by segmenting protein structures into clusters of residues that undergo cooperative changes in their solvent exposure, along with the hierarchy of interactions between these groups. We term these groups exposons to distinguish them from other types of clusters that arise in this analysis and others. We demonstrate, using three different model systems, that this method identifies experimentally validated and functionally relevant conformational changes, including conformational switches, allosteric coupling, and cryptic pockets. Our results suggest that key functional sites are hubs in the network of exposons. As a further test of the predictive power of this approach, we apply it to discover cryptic allosteric sites in two different β-lactamase enzymes that are widespread sources of antibiotic resistance. Experimental tests confirm our predictions for both systems. Importantly, we provide the first evidence, to our knowledge, for a cryptic allosteric site in CTX-M-9 β-lactamase. Experimentally testing this prediction did not require any mutations and revealed that this site exerts the most potent allosteric control over activity of any pockets found in β-lactamases to date. Discovery of a similar pocket that was previously overlooked in the well-studied TEM-1 β-lactamase demonstrates the utility of exposons.

Correlation Between Allosteric and Orthosteric Sites

Correlation between an allosteric site and its orthosteric site refers to the phenomenon that perturbations like ligand binding, mutation, or posttranslational modifications at the allosteric site leverage variation in the orthosteric site. Understanding this kind of correlation not only helps to disclose how information is transmitted in allosteric regulation but also provides clues for allosteric drug discovery. This chapter starts with an overview of correlation studies on allosteric and orthosteric sites and then introduces recent progress in evolutionary and simulation-based dynamic studies. Discussions and perspectives on future directions are also given.

Significance of triple torsional correlations in proteins

The free energy landscape (FEL) of a given complex molecular system is fundamentally the joint probability density of its many comprising degrees of freedom (DOFs). Computation of a complete FEL at atomistic scale is unfortunately intractable for a typical biomolecular system. The challenge of entropy calculation comes from various correlations among different DOFs. The common strategy to treat such complexity is expansion of the full correlation into various orders of local correlations. In reality, expansion is usually cut off at the second order (i.e. pairwise interactions) for protein torsional correlations without reliable estimation of the resulting error. Here, we estimated the mutual information of different torsion sets and found that triple correlations were significant for both local/distant residue pairs and consecutive backbone torsional segments. As expected, the third order approximations were found to be consistently better than the second order approximations. These findings were true for all analyzed proteins with different folds, were independent of the two different force fields utilized to generate trajectory sets, and were therefore likely to be of general importance for proteins. Additionally, binning strategies are of universal importance for numerical computation of correlations, we here provided a detailed comparison between equal-width and equal-sample binning for different bin numbers and demonstrated the impact of binning strategies on variances and biases of calculated mutual information. Our observation suggested that caution should be taken when quantitative comparison of correlations were intended between different studies with different binning strategies.

Torsional mutual information for 10 typical residue pairs calculated with full joint distributions (MI), second order expansion (MI₂), third order expansions (MI₃), and their linear recombinations (MI_LR).

A Biophysical Perspective on Enzyme Catalysis

Even after a century of investigation, our understanding of how enzymes work remains far from complete. In particular, several factors that enable enzymes to achieve high catalytic efficiencies remain only poorly understood. A number of theories have been developed, which propose or reaffirm that enzymes work as structural scaffolds, serving to bring together and properly orient the participants so that the reaction can proceed; therefore, leading to enzymes being viewed as only passive participants in the catalyzed reaction. A growing body of evidence shows that enzymes are not rigid structures but are constantly undergoing a wide range of internal motions and conformational fluctuations. In this Perspective, on the basis of studies from our group, we discuss the emerging biophysical model of enzyme catalysis that provides a detailed understanding of the interconnection among internal protein motions, conformational substates, enzyme mechanisms, and the catalytic efficiency of enzymes. For a number of enzymes, networks of conserved residues that extend from the surface of the enzyme all the way to the active site have been discovered. These networks are hypothesized to serve as pathways of energy transfer that enables thermodynamical coupling of the surrounding solvent with enzyme catalysis and play a role in promoting enzyme function. Additionally, the role of enzyme structure and electrostatic effects has been well acknowledged for quite some time. Collectively, the recent knowledge gained about enzyme mechanisms suggests that the conventional paradigm of enzyme structure encoding function is incomplete and needs to be extended to structure encodes dynamics, and together these enzyme features encode function including catalytic rate acceleration.

Advanced Methods for Accessing Protein Shape-Shifting Present New Therapeutic Opportunities

A protein is a dynamic shape-shifter whose function is determined by the set of structures it adopts. Unfortunately, atomically detailed structures are only available for a few conformations of any given protein, and these structures have limited explanatory and predictive power. Here, we provide a brief historical perspective on protein dynamics and introduce recent advances in computational and experimental methods that are providing unprecedented access to protein shape-shifting. Next, we focus on how these tools are revealing the mechanism of allosteric communication and features like cryptic pockets; both of which present new therapeutic opportunities. A major theme is the importance of considering the relative probabilities of different structures and the control one can exert over protein function by modulating this balance.

Structurally- and dynamically-driven allostery of the chymotrypsin-like proteases of SARS, Dengue and Zika viruses

Coronavirus 3C-like and Flavivirus NS2B-NS3 proteases utilize the chymotrypsin fold to harbor their catalytic machineries but also contain additional domains/co-factors. Over the past decade, we aimed to decipher how the extra domains/co-factors mediate the catalytic machineries of SARS 3C-like, Dengue and Zika NS2B-NS3 proteases by characterizing their folding, structures, dynamics and inhibition with NMR, X-ray crystallography and MD simulations, and the results revealed: 1) the chymotrypsin fold of the SARS 3C-like protease can independently fold, while, by contrast, those of Dengue and Zika proteases lack the intrinsic capacity to fold without co-factors. 2) Mutations on the extra domain of SARS 3C-like protease can transform the active catalytic machinery into the inactive collapsed state by structurally-driven allostery. 3) Amazingly, even without detectable structural changes, mutations on the extra domain are sufficient to either inactivate or enhance the catalytic machinery of SARS 3C-like protease by dynamically-driven allostery. 4) Global networks of correlated motions have been identified: for SARS 3C-like protease, N214A inactivates the catalytic machinery by decoupling the network, while STI/A and STIF/A enhance by altering the patterns of the network. The global networks of Dengue and Zika proteases are coordinated by their NS2B-cofactors. 5) Natural products were identified to allosterically inhibit Zika and Dengue proteases through binding a pocket on the back of the active site. Therefore, by introducing extra domains/cofactors, nature develops diverse strategies to regulate the catalytic machinery embedded on the chymotrypsin fold through folding, structurally- and dynamically-driven allostery, all of which might be exploited to develop antiviral drugs.

Dynamic Allostery in PLCγ1 and Its Modulation by a Cancer Mutation Revealed by MD Simulation and NMR

Phosphatidylinositol phospholipase Cγ (PLCγ) is an intracellular membrane-associated second-messenger signaling protein activated by tyrosine kinases such as fibroblast growth factor receptor 1. PLCγ contains the regulatory γ-specific array (γSA) comprising a tandem Src homology 2 (SH2) pair, an SH3 domain, and a split pleckstrin homology domain. Binding of an activated growth factor receptor to γSA leads to Tyr783 phosphorylation and consequent PLCγ activation. Several disease-relevant mutations in γSA have been identified; all lead to elevated phospholipase activity. In this work, we describe an allosteric mechanism that connects the Tyr783 phosphorylation site to the nSH2-cSH2 junction and involves dynamic interactions between the cSH2-SH3 linker and cSH2. Molecular dynamics simulations of the tandem SH2 protein suggest that Tyr783 phosphorylation is communicated to the nSH2-cSH2 junction by modulating cSH2 binding to sections of the cSH2-SH3 linker. NMR chemical shift perturbation analyses for designed tandem SH2 constructs reveal combined fast and slow dynamic processes that can be attributed to allosteric communication involving these regions of the protein, establishing an example in which complex N-site exchange can be directly inferred from 1H,15N-HSQC spectra. Furthermore, in tandem SH2 and γSA constructs, molecular dynamics and NMR results show that the Arg687Trp mutant in PLCγ1 (equivalent to the cancer mutation Arg665Trp in PLCγ2) perturbs the dynamic allosteric pathway. This combined experimental and computational study reveals a rare example of multistate kinetics involved in a dynamic allosteric process that is modulated in the context of a disease-relevant mutation. The allosteric influences and the weakened binding of the cSH2-SH3 linker to cSH2 should be taken into account in any more holistic investigation of PLCγ regulation.

Energy transport pathway in proteins: Insights from non-equilibrium molecular dynamics with elastic network model

Intra-molecular energy transport between distant functional sites plays important roles in allosterically regulating the biochemical activity of proteins. How to identify the specific intra-molecular signaling pathway from protein tertiary structure remains a challenging problem. In the present work, a non-equilibrium dynamics method based on the elastic network model (ENM) was proposed to simulate the energy propagation process and identify the specific signaling pathways within proteins. In this method, a given residue was perturbed and the propagation of energy was simulated by non-equilibrium dynamics in the normal modes space of ENM. After that, the simulation results were transformed from the normal modes space to the Cartesian coordinate space to identify the intra-protein energy transduction pathways. The proposed method was applied to myosin and the third PDZ domain (PDZ3) of PSD-95 as case studies. For myosin, two signaling pathways were identified, which mediate the energy transductions form the nucleotide binding site to the 50 kDa cleft and the converter subdomain, respectively. For PDZ3, one specific signaling pathway was identified, through which the intra-protein energy was transduced from ligand binding site to the distant opposite side of the protein. It is also found that comparing with the commonly used cross-correlation analysis method, the proposed method can identify the anisotropic energy transduction pathways more effectively.

Consistent Prediction of Mutation Effect on Drug Binding in HIV-1 Protease Using Alchemical Calculations

Despite a large number of antiretroviral drugs targeting HIV-1 protease for inhibition, mutations in this protein during the course of patient treatment can render them inefficient. This emerging resistance inspired numerous computational studies of the HIV-1 protease aimed at predicting the effect of mutations on drug binding in terms of free binding energy Δ G, as well as in mechanistic terms. In this study, we analyze ten different protease-inhibitor complexes carrying major resistance-associated mutations (RAMs) G48V, I50V, and L90M using molecular dynamics simulations. We demonstrate that alchemical free energy calculations can consistently predict the effect of mutations on drug binding. By explicitly probing different protonation states of the catalytic aspartic dyad, we reveal the importance of the correct choice of protonation state for the accuracy of the result. We also provide insight into how different mutations affect drug binding in their specific ways, with the unifying theme of how all of them affect the crucial drug binding regions of the protease.

Combining computational and experimental biology to develop therapeutically valuable IL2 muteins

High-dose IL2, first approved in 1992, has been used in the treatment of advanced renal cell carcinoma and melanoma. In these indications, IL2 induces long lasting objective responses in 5% to 20% of patients. However, toxicity and the unexpected expansion of regulatory T cells (Tregs) have limited its practical use and therapeutic impact, respectively. At the Center of Molecular Immunology in Havana, Cuba, a project was launched in 2005 to rationally design IL2 muteins that could be deployed in the therapy of cancer. The basic goal was to uncouple the pleiotropic effect of IL2 on different immune T cells, to obtain a mutein with a therapeutic index that was better than that achieved with wild type (wt) IL2. Using a combination of computational and experimental biology approaches, we predicted and developed two novel IL2 muteins with therapeutic potential. The first, designated no-alpha mutein, is an agonist of IL2R signaling with a reduced ability to expand Treg in vivo. In mice, the no-alpha mutein IL2 has higher antitumor activity and lower toxicity than wt IL2. It represents a potential best-in-class drug that has begun phase I/II clinical trials in solid tumors. The second, designated no-gamma mutein, is an antagonist of IL2R signaling, with some preferential affinity for Tregs. This mutein has antitumor activity in mice that likely derives from its ability to reduce Treg accumulation in vivo. It represents a first-in-class drug that offers a novel strategy to inhibit Treg activity in vivo.

Energetics and conformational pathways of functional rotation in the multidrug transporter AcrB

The multidrug transporter AcrB transports a broad range of drugs out of the cell by means of the proton-motive force. The asymmetric crystal structure of trimeric AcrB suggests a functionally rotating mechanism for drug transport. Despite various supportive forms of evidence from biochemical and simulation studies for this mechanism, the link between the functional rotation and proton translocation across the membrane remains elusive. Here, calculating the minimum free energy pathway of the functional rotation for the complete AcrB trimer, we describe the structural and energetic basis behind the coupling between the functional rotation and the proton translocation at atomic resolution. Free energy calculations show that protonation of Asp408 in the transmembrane portion of the drug-bound protomer drives the functional rotation. The conformational pathway identifies vertical shear motions among several transmembrane helices, which regulate alternate access of water in the transmembrane as well as peristaltic motions that pump drugs in the periplasm.

CAMERRA: An analysis tool for the computation of conformational dynamics by evaluating residue-residue associations

A computational method which extracts the dominant motions from an ensemble of biomolecular conformations via a correlation analysis of residue-residue contacts is presented. The algorithm first renders the structural information into contact matrices, then constructs the collective modes based on the correlated dynamics of a selected set of dynamic contacts. Associated programs can bridge the results for further visualization using graphics software. The aim of this method is to provide an analysis of conformations of biopolymers from the contact viewpoint. It may assist a systematical uncovering of conformational switching mechanisms existing in proteins and biopolymer systems in general by statistical analysis of simulation snapshots. In contrast to conventional correlation analyses of Cartesian coordinates (such as distance covariance analysis and Cartesian principal component analysis), this program also provides an alternative way to locate essential collective motions in general. Herein, we detail the algorithm in a stepwise manner and comment on the importance of the method as applied to decoding allosteric mechanisms. © 2018 Wiley Periodicals, Inc.

Structural analysis of protein tyrosine phosphatase 1B reveals potentially druggable allosteric binding sites

Catalytic proteins such as human protein tyrosine phosphatase 1B (PTP1B), with conserved and highly polar active sites, warrant the discovery of druggable nonactive sites, such as allosteric sites, and potentially, therapeutic small molecules that can bind to these sites. Catalyzing the dephosphorylation of numerous substrates, PTP1B is physiologically important in intracellular signal transduction pathways in diverse cell types and tissues. Aberrant PTP1B is associated with obesity, diabetes, cancers, and neurodegenerative disorders. Utilizing clustering methods (based on root mean square deviation, principal component analysis, nonnegative matrix factorization, and independent component analysis), we have examined multiple PTP1B structures. Using the resulting representative structures in different conformational states, we determined consensus clustroids and used them to identify both known and novel binding sites, some of which are potentially allosteric. We report several lead compounds that could potentially bind to the novel PTP1B binding sites and can be further optimized. Considering the possibility for drug repurposing, we discovered homologous binding sites in other proteins, with ligands that could potentially bind to the novel PTP1B binding sites.

Computational Exploration of Conformational Transitions in Protein Drug Targets

Protein drug targets vary from highly structured to completely disordered; either way dynamics governs function. Hence, understanding the dynamical aspects of how protein targets function can enable improved interventions with drug molecules. Computational approaches offer highly detailed structural models of protein dynamics which are becoming more predictive as model quality and sampling power improve. However, the most advanced and popular models still have errors owing to imperfect parameter sets and often cannot access longer timescales of many crucial biological processes. Experimental approaches offer more certainty but can struggle to detect and measure lightly populated conformations of target proteins and subtle allostery. An emerging solution is to integrate available experimental data into advanced molecular simulations. In the future, molecular simulation in combination with experimental data may be able to offer detailed models of important drug targets such that improved functional mechanisms or selectivity can be accessed.

Molecular Dynamics Simulation and Prediction of Druggable Binding Sites

Binding site identification and druggability evaluation are two essential steps in structure-based drug design. A druggable binding site tends to have high binding affinity to drug-like molecules. Predicting such sites can have a significant impact on a drug design campaign. This chapter focuses on summarizing the different methods that are used to predict druggable binding sites. The chapter also discusses the importance of including protein flexibility in the search process and the use of molecular dynamics simulations to address this aspect. Case studies from the literature are also summarized and discussed. We hope that this chapter would provide an overview on the different methods employed in binding site identification evaluation.

Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs

From the pharmacological point of view, allosteric modulators may present numerous advantages over orthosteric ligands. Growing availability of novel tools and experimental data provides a tempting opportunity to apply computational methods to improve known modulators and design novel ones. However, recent progress in understanding of complexity of allostery increases awareness of problems involved in design of modulators with desired properties. Deeper insight into phenomena such as probe dependence, altering signaling bias with minor changes in ligand structure, as well as influence of subtle endogenous allosteric factors turns out to be fundamental. These effects make the design of a modulator with precise pharmacological outcome a very challenging task, and need to be taken into consideration throughout the design process. In this chapter, we focus on nuances of targeting GPCR allosteric sites in computational drug design efforts, in particular with application of docking, virtual screening, and molecular dynamics.

Ensemble- and Rigidity Theory-Based Perturbation Approach To Analyze Dynamic Allostery

Allostery describes the functional coupling between sites in biomolecules. Recently, the role of changes in protein dynamics for allosteric communication has been highlighted. A quantitative and predictive description of allostery is fundamental for understanding biological processes. Here, we integrate an ensemble-based perturbation approach with the analysis of biomolecular rigidity and flexibility to construct a model of dynamic allostery. Our model, by definition, excludes the possibility of conformational changes, evaluates static, not dynamic, properties of molecular systems, and describes allosteric effects due to ligand binding in terms of a novel free-energy measure. We validated our model on three distinct biomolecular systems: eglin c, protein tyrosine phosphatase 1B, and the lymphocyte function-associated antigen 1 domain. In all cases, it successfully identified key residues for signal transmission in very good agreement with the experiment. It correctly and quantitatively discriminated between positively or negatively cooperative effects for one of the systems. Our model should be a promising tool for the rational discovery of novel allosteric drugs.

Conformational landscapes of membrane proteins delineated by enhanced sampling molecular dynamics simulations

The expansion of computational power, better parameterization of force fields, and the development of novel algorithms to enhance the sampling of the free energy landscapes of proteins have allowed molecular dynamics (MD) simulations to become an indispensable tool to understand the function of biomolecules. The temporal and spatial resolution of MD simulations allows for the study of a vast number of processes of interest. Here, we review the computational efforts to uncover the conformational free energy landscapes of a subset of membrane proteins: ion channels, transporters and G-protein coupled receptors. We focus on the various enhanced sampling techniques used to study these questions, how the conclusions come together to build a coherent picture, and the relationship between simulation outcomes and experimental observables.

HtrA1 activation is driven by an allosteric mechanism of inter-monomer communication

The human protease family HtrA is responsible for preventing protein misfolding and mislocalization, and a key player in several cellular processes. Among these, HtrA1 is implicated in several cancers, cerebrovascular disease and age-related macular degeneration. Currently, HtrA1 activation is not fully characterized and relevant for drug-targeting this protease. Our work provides a mechanistic step-by-step description of HtrA1 activation and regulation. We report that the HtrA1 trimer is regulated by an allosteric mechanism by which monomers relay the activation signal to each other, in a PDZ-domain independent fashion. Notably, we show that inhibitor binding is precluded if HtrA1 monomers cannot communicate with each other. Our study establishes how HtrA1 trimerization plays a fundamental role in proteolytic activity. Moreover, it offers a structural explanation for HtrA1-defective pathologies as well as mechanistic insights into the degradation of complex extracellular fibrils such as tubulin, amyloid beta and tau that belong to the repertoire of HtrA1.

Allosteric Communication Networks in Proteins Revealed through Pocket Crosstalk Analysis

The detection and characterization of binding pockets and allosteric communication in proteins is crucial for studying biological regulation and performing drug design. Nowadays, ever-longer molecular dynamics (MD) simulations are routinely used to investigate the spatiotemporal evolution of proteins. Yet, there is no computational tool that can automatically detect all the pockets and potential allosteric communication networks along these extended MD simulations. Here, we use a novel and fully automated algorithm that examines pocket formation, dynamics, and allosteric communication embedded in microsecond-long MD simulations of three pharmaceutically relevant proteins, namely, PNP, A2A, and Abl kinase. This dynamic analysis uses pocket crosstalk, defined as the temporal exchange of atoms between adjacent pockets, along the MD trajectories as a fingerprint of hidden allosteric communication networks. Importantly, this study indicates that dynamic pocket crosstalk analysis provides new mechanistic understandings on allosteric communication networks, enriching the available experimental data. Thus, our results suggest the prospective use of this unprecedented dynamic analysis to characterize transient binding pockets for structure-based drug design.

Calcium-Mediated Control of S100 Proteins: Allosteric Communication via an Agitator/Signal Blocking Mechanism

Allosteric proteins possess dynamically coupled residues for the propagation of input signals to distant target binding sites. The input signals usually correspond to "effector is present" or "effector is not present". Many aspects of allosteric regulation remain incompletely understood. This work focused on S100A11, a dimeric EF-hand protein with two hydrophobic target binding sites. An annexin peptide (Ax) served as the target. Target binding is allosterically controlled by Ca²⁺ over a distance of ∼26 Å. Ca²⁺ promotes formation of a [Ca₄ S100 Ax₂] complex, where the Ax peptides are accommodated between helices III/IV and III'/IV'. Without Ca²⁺ these binding sites are closed, precluding interactions with Ax. The allosteric mechanism was probed by microsecond MD simulations in explicit water, complemented by hydrogen exchange mass spectrometry (HDX/MS). Consistent with experimental data, MD runs in the absence of Ca²⁺ and Ax culminated in target binding site closure. In simulations on [Ca₄ S100] the target binding sites remained open. These results capture the essence of allosteric control, revealing how Ca²⁺ prevents binding site closure. Both HDX/MS and MD data showed that the metalation sites become more dynamic after Ca²⁺ loss. However, these enhanced dynamics do not represent the primary trigger of the allosteric cascade. Instead, a labile salt bridge acts as an incessantly active "agitator" that destabilizes the packing of adjacent residues, causing a domino chain of events that culminates in target binding site closure. This agitator represents the starting point of the allosteric signal propagation pathway. Ca²⁺ binding rigidifies elements along this pathway, thereby blocking signal transmission. This blocking mechanism does not conform to the commonly held view that allosteric communication pathways generally originate at the sites where effectors interact with the protein.

Time-resolved observation of protein allosteric communication

Allostery represents a fundamental mechanism of biological regulation that is mediated via long-range communication between distant protein sites. Although little is known about the underlying dynamical process, recent time-resolved infrared spectroscopy experiments on a photoswitchable PDZ domain (PDZ2S) have indicated that the allosteric transition occurs on multiple timescales. Here, using extensive nonequilibrium molecular dynamics simulations, a time-dependent picture of the allosteric communication in PDZ2S is developed. The simulations reveal that allostery amounts to the propagation of structural and dynamical changes that are genuinely nonlinear and can occur in a nonlocal fashion. A dynamic network model is constructed that illustrates the hierarchy and exceeding structural heterogeneity of the process. In compelling agreement with experiment, three physically distinct phases of the time evolution are identified, describing elastic response ([Formula: see text] ns), inelastic reorganization ([Formula: see text] ns), and structural relaxation ([Formula: see text]s). Issues such as the similarity to downhill folding as well as the interpretation of allosteric pathways are discussed.

Signaling within Allosteric Machines: Signal Transmission Pathways Inside G Protein-Coupled Receptors

In recent years, our understanding of function of G protein-coupled receptors (GPCRs) has changed from a picture of simple signal relays, transmitting only a particular signal to a particular G protein heterotrimer, to versatile machines, capable of various responses to different stimuli and being modulated by various factors. Some recent reports provide not only the data on ligands/modulators and resultant signals induced by them, but also deeper insights into exact pathways of signal migration and mechanisms of signal transmission through receptor structure. Combination of these computational and experimental data sheds more light on underlying mechanisms of signal transmission and signaling bias in GPCRs. In this review we focus on available clues on allosteric pathways responsible for complex signal processing within GPCRs structures, with particular emphasis on linking compatible in silico- and in vitro-derived data on the most probable allosteric connections.

Mutations in Antibody Fragments Modulate Allosteric Response Via Hydrogen-Bond Network Fluctuations

A mechanical perturbation method that locally restricts conformational entropy along the protein backbone is used to identify putative allosteric sites in a series of antibody fragments. The method is based on a distance constraint model that integrates mechanical and thermodynamic viewpoints of protein structure wherein mechanical clamps that mimic substrate or cosolute binding are introduced. Across a set of six single chain-Fv fragments of the anti-lymphotoxin-β receptor antibody, statistically significant responses are obtained by averaging over 10 representative structures sampled from a molecular dynamics simulation. As expected, the introduced clamps locally rigidify the protein, but long-ranged increases in both rigidity and flexibility are also frequently observed. Expanding our analysis to every molecular dynamics frame demonstrates that the allosteric responses are modulated by fluctuations within the hydrogen-bond network where the native ensemble is comprised of conformations that both are, and are not, affected by the perturbation in question. Population shifts induced by the mutations alter the allosteric response by adjusting which hydrogen-bond networks are the most probable. These effects are compared using response maps that track changes across each single chain-Fv fragment, thus providing valuable insight into how sensitive allosteric mechanisms are to mutations.

Gorge Motions of Acetylcholinesterase Revealed by Microsecond Molecular Dynamics Simulations

Acetylcholinesterase, with a deep, narrow active-site gorge, attracts enormous interest due to its particularly high catalytic efficiency and its inhibitors used for treatment of Alzheimer’s disease. To facilitate the massive pass-through of the substrate and inhibitors, “breathing” motions to modulate the size of the gorge are an important prerequisite. However, the molecular mechanism that governs such motions is not well explored. Here, to systematically investigate intrinsic motions of the enzyme, we performed microsecond molecular dynamics simulations on the monomer and dimer of Torpedo californica acetylcholinesterase (TcAChE) as well as the complex of TcAChE bound with the drug E2020. It has been revealed that protein-ligand interactions and dimerization both keep the gorge in bulk, and opening events of the gorge increase dramatically compared to the monomer. Dynamics of three subdomains, S3, S4 and the Ω-loop, are tightly associated with variations of the gorge size while the dynamics can be changed by ligand binding or protein dimerization. Moreover, high correlations among these subdomains provide a basis for remote residues allosterically modulating the gorge motions. These observations are propitious to expand our understanding of protein structure and function as well as providing clues for performing structure-based drug design.

Quantifying Allosteric Communication via Both Concerted Structural Changes and Conformational Disorder with CARDS

Allosteric (i.e., long-range) communication within proteins is crucial for many biological processes, such as the activation of signaling cascades in response to specific stimuli. However, the physical basis for this communication remains unclear. Existing computational methods for identifying allostery focus on the role of concerted structural changes, but recent experimental work demonstrates that disorder is also an important factor. Here, we introduce the Correlation of All Rotameric and Dynamical States (CARDS) framework for quantifying correlations between both the structure and disorder of different regions of a protein. To quantify disorder, we draw inspiration from methods for quantifying "dynamic heterogeneity" from chemical physics to classify segments of a dihedral's time evolution as being in either ordered or disordered regimes. To demonstrate the utility of this approach, we apply CARDS to the Catabolite Activator Protein (CAP), a transcriptional activator that is regulated by Cyclic Adenosine MonoPhosphate (cAMP) binding. We find that CARDS captures allosteric communication between the two cAMP-Binding Domains (CBDs). Importantly, CARDS reveals that this coupling is dominated by disorder-mediated correlations, consistent with NMR experiments that establish allosteric coupling between the CBDs occurs without a concerted structural change. CARDS also recapitulates an enhanced role for disorder in the communication between the DNA-Binding Domains (DBDs) and CBDs in the S62F variant of CAP. Finally, we demonstrate that using CARDS to find communication hotspots identifies regions of CAP that are in allosteric communication without foreknowledge of their identities. Therefore, we expect CARDS to be of great utility for both understanding and predicting allostery.

Entropy Transfer between Residue Pairs and Allostery in Proteins: Quantifying Allosteric Communication in Ubiquitin

It has recently been proposed by Gunasakaran et al. that allostery may be an intrinsic property of all proteins. Here, we develop a computational method that can determine and quantify allosteric activity in any given protein. Based on Schreiber's transfer entropy formulation, our approach leads to an information transfer landscape for the protein that shows the presence of entropy sinks and sources and explains how pairs of residues communicate with each other using entropy transfer. The model can identify the residues that drive the fluctuations of others. We apply the model to Ubiquitin, whose allosteric activity has not been emphasized until recently, and show that there are indeed systematic pathways of entropy and information transfer between residues that correlate well with the activities of the protein. We use 600 nanosecond molecular dynamics trajectories for Ubiquitin and its complex with human polymerase iota and evaluate entropy transfer between all pairs of residues of Ubiquitin and quantify the binding susceptibility changes upon complex formation. We explain the complex formation propensities of Ubiquitin in terms of entropy transfer. Important residues taking part in allosteric communication in Ubiquitin predicted by our approach are in agreement with results of NMR relaxation dispersion experiments. Finally, we show that time delayed correlation of fluctuations of two interacting residues possesses an intrinsic causality that tells which residue controls the interaction and which one is controlled. Our work shows that time delayed correlations, entropy transfer and causality are the required new concepts for explaining allosteric communication in proteins.

Computational and Experimental Characterization of Patient Derived Mutations Reveal an Unusual Mode of Regulatory Spine Assembly and Drug Sensitivity in EGFR Kinase

The catalytic activation of protein kinases requires precise positioning of key conserved catalytic and regulatory motifs in the kinase core. The Regulatory Spine (RS) is one such structural motif that is dynamically assembled upon kinase activation. The RS is also a mutational hotspot in cancers; however, the mechanisms by which cancer mutations impact RS assembly and kinase activity are not fully understood. In this study, through mutational analysis of patient derived mutations in the RS of EGFR kinase, we identify an activating mutation, M766T, at the RS3 position. RS3 is located in the regulatory αC-helix, and a series of mutations at the RS3 position suggest a strong correlation between the amino acid type present at the RS3 position and ligand (EGF) independent EGFR activation. Small polar amino acids increase ligand independent activity, while large aromatic amino acids decrease kinase activity. M766T relies on the canonical asymmetric dimer for full activation. Molecular modeling and molecular dynamics simulations of WT and mutant EGFR suggest a model in which M766T activates the kinase domain by disrupting conserved autoinhibitory interactions between M766 and hydrophobic residues in the activation segment. In addition, a water mediated hydrogen bond network between T766, the conserved K745-E762 salt bridge, and the backbone amide of the DFG motif is identified as a key determinant of M766T-mediated activation. M766T is resistant to FDA approved EGFR inhibitors such as gefitinib and erlotinib, and computational estimation of ligand binding free energy identifies key residues associated with drug sensitivity. In sum, our studies suggest an unusual mode of RS assembly and oncogenic EGFR activation, and provide new clues for the design of allosteric protein kinase inhibitors.

Sampling conformational space of intrinsically disordered proteins in explicit solvent: Comparison between well-tempered ensemble approach and solute tempering method

Intrinsically disordered proteins (IDPs) are a class of proteins that expected to be largely unstructured under physiological conditions. Due to their heterogeneous nature, experimental characterization of IDP is challenging. Temperature replica exchange molecular dynamics (T-REMD) is a widely used enhanced sampling method to probe structural characteristics of these proteins. However, its application has been hindered due to its tremendous computational cost, especially when simulating large systems in explicit solvent. Two methods, parallel tempering well-tempered ensemble (PT-WTE) and replica exchange with solute tempering (REST), have been proposed to alleviate the computational expense of T-REMD. In this work, we select three different IDP systems to compare the sampling characteristics and efficiencies of the two methods Both the two methods could efficiently sample the conformational space of IDP and yield highly consistent results for all the three IDPs. The efficiencies of the two methods: are compatible, with about 5-6 times better than the plain T-REMD. Besides, the advantages and disadvantages of each method are also discussed. Specially, the PT-WTE method could provide temperature dependent data of the system which could not be achieved by REST, while the REST method could readily be used to a part of the system, which is quite efficient to simulate some biological processes.

Molecular Basis of S100A1 Activation at Saturating and Subsaturating Calcium Concentrations

The S100A1 protein mediates a wide variety of physiological processes through its binding of calcium (Ca(2+)) and endogenous target proteins. S100A1 presents two Ca(2+)-binding domains: a high-affinity "canonical" EF (cEF) hand and a low-affinity "pseudo" EF (pEF) hand. Accumulating evidence suggests that both Ca(2+)-binding sites must be saturated to stabilize an open state conducive to peptide recognition, yet the pEF hand's low affinity limits Ca(2+) binding at normal physiological concentrations. To understand the molecular basis of Ca(2+) binding and open-state stabilization, we performed 100 ns molecular dynamics simulations of S100A1 in the apo/holo (Ca(2+)-free/bound) states and a half-saturated state, for which only the cEF sites are Ca(2+)-bound. Our simulations indicate that the pattern of oxygen coordination about Ca(2+) in the cEF relative to the pEF site contributes to the former's higher affinity, whereas Ca(2+) binding strongly reshapes the protein's conformational dynamics by disrupting β-sheet coupling between EF hands. Moreover, modeling of the half-saturated configuration suggests that the open state is unstable and reverts toward a closed state in the absence of the pEF Ca(2+) ion. These findings indicate that Ca(2+) binding at the cEF site alone is insufficient to stabilize opening; thus, posttranslational modification of the protein may be required for target peptide binding at subsaturating intracellular Ca(2+) levels.

Decoding the Interactions Regulating the Active State Mechanics of Eukaryotic Protein Kinases

Eukaryotic protein kinases regulate most cellular functions by phosphorylating targeted protein substrates through a highly conserved catalytic core. In the active state, the catalytic core oscillates between open, intermediate, and closed conformations. Currently, the intramolecular interactions that regulate the active state mechanics are not well understood. Here, using cAMP-dependent protein kinase as a representative model coupled with biochemical, biophysical, and computational techniques, we define a set of highly conserved electrostatic and hydrophobic interactions working harmoniously to regulate these mechanics. These include the previously identified salt bridge between a lysine from the β3-strand and a glutamate from the αC-helix as well as an electrostatic interaction between the phosphorylated activation loop and αC-helix and an ensemble of hydrophobic residues of the Regulatory spine and Shell. Moreover, for over three decades it was thought that the highly conserved β3-lysine was essential for phosphoryl transfer, but our findings show that the β3-lysine is not required for phosphoryl transfer but is essential for the active state mechanics.

Eukaryotic protein kinases (EPKs) regulate over a third of the human proteome by transferring the γ-phosphate from adenosine triphosphate (ATP) to a protein substrate in a process known as protein phosphorylation. Biochemical and biophysical studies have shown that EPKs undergo multiconformational rearrangements in which the catalytic core is oscillating between open, intermediate, and closed conformations when active. Presently, the intramolecular interactions that regulate this dynamic process are not well understood. In this paper, we show how a set of conserved electrostatic and hydrophobic interactions harmoniously regulate the active state mechanics. The electrostatic interactions involve the highly conserved salt bridge between the lysine from subdomain-II and glutamate from subdomain-III as well as an interaction between the activation loop and αC-helix. The hydrophobic interactions include the nonlinear motifs known as the Regulatory spine and Shell that traverse both lobes of the catalytic core. Furthermore, our findings show that the highly conserved “catalytic lysine” is not directly required for phosphoryl transfer but rather serves as a hub that aligns and positions the dynamic core elements required for catalysis.

Nonlinear backbone torsional pair correlations in proteins

Protein allostery requires dynamical structural correlations. Physical origin of which, however, remain elusive despite intensive studies during last two and half decades. Based on analysis of molecular dynamics (MD) simulation trajectories for ten proteins with different sizes and folds, we found that nonlinear backbone torsional pair (BTP) correlations, which are mainly spatially long-ranged and are dominantly executed by loop residues, exist extensively in most analyzed proteins. Examination of torsional motion for correlated BTPs suggested that such nonlinear correlations are mainly associated aharmonic torsional state transitions and in some cases strongly anisotropic local torsional motion of participating torsions, and occur on widely different and relatively longer time scales. In contrast, correlations between backbone torsions in stable α helices and β strands are mainly linear and spatially short-ranged, and are more likely to associate with harmonic local torsional motion. Further analysis revealed that the direct cause of nonlinear contributions are heterogeneous linear correlations. These findings implicate a general search strategy for novel allosteric modulation sites of protein activities.

Allosteric communication pipelines in G-protein-coupled receptors

The binding of ligands to G-protein-coupled receptors (GPCRs) in the extracellular region transmits the signal to the intracellular region to initiate coupling to effector proteins. The mechanism of this allosteric communication remains largely unexplored. Knowledge of the residues involved in the pipeline of the allosteric communication from the extracellular to the intracellular region will provide means to (a) design ligands with bias in potency towards one signaling pathway over others, and (b) design allosteric modulators that show subtype selectivity in GPCRs. In this review we describe the current state of the computational methods that provide insights into the allosteric communication in GPCRs and elucidate how this information can be used to design allosteric modulators.

Disulfide-Trapping Identifies a New, Effective Chemical Probe for Activating the Nuclear Receptor Human LRH-1 (NR5A2)

Conventional efforts relying on high-throughput physical and virtual screening of large compound libraries have failed to yield high-efficiency chemical probes for many of the 48 human nuclear receptors. Here, we investigated whether disulfide-trapping, an approach new to nuclear receptors, would provide effective lead compounds targeting human liver receptor homolog 1 (hLRH-1, NR5A2). Despite the fact that hLRH-1 contains a large ligand binding pocket and binds phospholipids with high affinity, existing synthetic hLRH-1 ligands are of limited utility due to poor solubility, low efficacy or significant off-target effects. Using disulfide-trapping, we identified a lead compound that conjugates with remarkably high-efficiency to a native cysteine residue (Cys³⁴⁶) lining the hydrophobic cavity in the ligand binding domain of hLRH-1. Guided by computational modeling and cellular assays, the lead compound was elaborated into ligands PME8 and PME9 that bind hLRH-1 reversibly (no cysteine reactivity) and increase hLRH-1 activity in cells. When compared with the existing hLRH-1 synthetic agonist RJW100, both PME8 and PME9 showed comparable induction of the LRH-1 dependent target gene CYP24A1 in human HepG2 cells, beginning as early as 3 h after drug treatment. The induction is specific as siRNA-mediated knock-down of hLRH-1 renders both PME8 and PME9 ineffective. These data show that PME8 and PME9 are potent activators of hLRH-1 and suggest that with further development this lead series may yield useful chemical probes for manipulating LRH-1 activity in vivo.

The allosteric switching mechanism in bacteriophage MS2

We use all-atom simulations to elucidate the mechanisms underlying conformational switching and allostery within the coat protein of the bacteriophage MS2. Assembly of most icosahedral virus capsids requires that the capsid protein adopts different conformations at precise locations within the capsid. It has been shown that a 19 nucleotide stem loop (TR) from the MS2 genome acts as an allosteric effector, guiding conformational switching of the coat protein during capsid assembly. Since the principal conformational changes occur far from the TR binding site, it is important to understand the molecular mechanism underlying this allosteric communication. To this end, we use all-atom simulations with explicit water combined with a path sampling technique to sample the MS2 coat protein conformational transition, in the presence and absence of TR-binding. The calculations find that TR binding strongly alters the transition free energy profile, leading to a switch in the favored conformation. We discuss changes in molecular interactions responsible for this shift. We then identify networks of amino acids with correlated motions to reveal the mechanism by which effects of TR binding span the protein. We find that TR binding strongly affects residues located at the 5-fold and quasi-sixfold interfaces in the assembled capsid, suggesting a mechanism by which the TR binding could direct formation of the native capsid geometry. The analysis predicts amino acids whose substitution by mutagenesis could alter populations of the conformational substates or their transition rates.