Quantifying Correlations Between Allosteric Sites in Thermodynamic Ensembles

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.

Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems

Mixed-solvent molecular dynamics (MixMD) is a hotspot-mapping technique that relies on molecular dynamics simulations of proteins in binary solvent mixtures. Previous work on MixMD has established the technique's effectiveness in capturing binding sites of small organic compounds. In this work, we show that MixMD can identify both competitive and allosteric sites on proteins. The MixMD approach embraces full protein flexibility and allows competition between solvent probes and water. Sites preferentially mapped by probe molecules are more likely to be binding hotspots. There are two important requirements for the identification of ligand-binding hotspots: (1) hotspots must be mapped at very high signal-to-noise ratio and (2) the hotspots must be mapped by multiple probe types. We have developed our mapping protocol around acetonitrile, isopropanol, and pyrimidine as probe solvents because they allowed us to capture hydrophilic, hydrophobic, hydrogen-bonding, and aromatic interactions. Charged probes were needed for mapping one target, and we introduce them in this work. In order to demonstrate the robust nature and wide applicability of the technique, a combined total of 5 μs of MixMD was applied across several protein targets known to exhibit allosteric modulation. Most notably, all the protein crystal structures used to initiate our simulations had no allosteric ligands bound, so there was no preorganization of the sites to predispose the simulations to find the allosteric hotspots. The protein test cases were ABL Kinase, Androgen Receptor, CHK1 Kinase, Glucokinase, PDK1 Kinase, Farnesyl Pyrophosphate Synthase, and Protein-Tyrosine Phosphatase 1B. The success of the technique is demonstrated by the fact that the top-four sites solely map the competitive and allosteric sites. Lower-ranked sites consistently map other biologically relevant sites, multimerization interfaces, or crystal-packing interfaces. Lastly, we highlight the importance of including protein flexibility by demonstrating that MixMD can map allosteric sites that are not detected in half the systems using FTMap applied to the same crystal structures.

Revealing Atomic-Level Mechanisms of Protein Allostery with Molecular Dynamics Simulations

Molecular dynamics (MD) simulations have become a powerful and popular method for the study of protein allostery, the widespread phenomenon in which a stimulus at one site on a protein influences the properties of another site on the protein. By capturing the motions of a protein's constituent atoms, simulations can enable the discovery of allosteric binding sites and the determination of the mechanistic basis for allostery. These results can provide a foundation for applications including rational drug design and protein engineering. Here, we provide an introduction to the investigation of protein allostery using molecular dynamics simulation. We emphasize the importance of designing simulations that include appropriate perturbations to the molecular system, such as the addition or removal of ligands or the application of mechanical force. We also demonstrate how the bidirectional nature of allostery-the fact that the two sites involved influence one another in a symmetrical manner-can facilitate such investigations. Through a series of case studies, we illustrate how these concepts have been used to reveal the structural basis for allostery in several proteins and protein complexes of biological and pharmaceutical interest.

Allosteric sites can be identified based on the residue-residue interaction energy difference

Allosteric drugs act at a distance to regulate protein functions. They have several advantages over conventional orthosteric drugs, including diverse regulation types and fewer side effects. However, the rational design of allosteric ligands remains a challenge, especially when it comes to the identification allosteric binding sites. As the binding of allosteric ligands may induce changes in the pattern of residue-residue interactions, we calculated the residue-residue interaction energies within the allosteric site based on the molecular mechanics generalized Born surface area energy decomposition scheme. Using a dataset of 17 allosteric proteins with structural data for both the apo and the ligand-bound state available, we used conformational ensembles generated by molecular dynamics simulations to compute the differences in the residue-residue interaction energies in known allosteric sites from both states. For all the known sites, distinct interaction energy differences (>25%) were observed. We then used CAVITY, a binding site detection program to identify novel putative allosteric sites in the same proteins. This yielded a total of 31 "druggable binding sites," of which 21 exhibited >25% difference in residue interaction energies, and were hence predicted as novel allosteric sites. Three of the predicted allosteric sites were supported by recent experimental studies. All the predicted sites may serve as novel allosteric sites for allosteric ligand design. Our study provides a computational method for identifying novel allosteric sites for allosteric drug design.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Conformational heterogeneity of the calmodulin binding interface

Calmodulin (CaM) is a ubiquitous Ca(2+) sensor and a crucial signalling hub in many pathways aberrantly activated in disease. However, the mechanistic basis of its ability to bind diverse signalling molecules including G-protein-coupled receptors, ion channels and kinases remains poorly understood. Here we harness the high resolution of molecular dynamics simulations and the analytical power of Markov state models to dissect the molecular underpinnings of CaM binding diversity. Our computational model indicates that in the absence of Ca(2+), sub-states in the folded ensemble of CaM's C-terminal domain present chemically and sterically distinct topologies that may facilitate conformational selection. Furthermore, we find that local unfolding is off-pathway for the exchange process relevant for peptide binding, in contrast to prior hypotheses that unfolding might account for binding diversity. Finally, our model predicts a novel binding interface that is well-populated in the Ca(2+)-bound regime and, thus, a candidate for pharmacological intervention.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Computational approaches to detect allosteric pathways in transmembrane molecular machines

Many of the functions of transmembrane proteins involved in signal processing and transduction across the cell membrane are determined by allosteric couplings that propagate the functional effects well beyond the original site of activation. Data gathered from breakthroughs in biochemistry, crystallography, and single molecule fluorescence have established a rich basis of information for the study of molecular mechanisms in the allosteric couplings of such transmembrane proteins. The mechanistic details of these couplings, many of which have therapeutic implications, however, have only become accessible in synergy with molecular modeling and simulations. Here, we review some recent computational approaches that analyze allosteric coupling networks (ACNs) in transmembrane proteins, and in particular the recently developed Protein Interaction Analyzer (PIA) designed to study ACNs in the structural ensembles sampled by molecular dynamics simulations. The power of these computational approaches in interrogating the functional mechanisms of transmembrane proteins is illustrated with selected examples of recent experimental and computational studies pursued synergistically in the investigation of secondary active transporters and GPCRs. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

The role of side chain entropy and mutual information for improving the de novo design of Kemp eliminases KE07 and KE70

Side chain entropy and mutual entropy information between residue pairs have been calculated for two de novo designed Kemp eliminase enzymes, KE07 and KE70, and for their most improved versions at the end of laboratory directed evolution (LDE).

Mechanisms of Differential Allosteric Modulation in Homologous Proteins: Insights from the Analysis of Internal Dynamics and Energetics of PDZ Domains

Allostery is a general phenomenon in proteins whereby a perturbation at one site reverberates into a functional change at another one, through modulation of its conformational dynamics. Herein, we address the problem of how the molecular signal encoded by a ligand is differentially transmitted through the structures of two homologous PDZ proteins: PDZ2, which responds to binding with structural and dynamical changes in regions distal from the ligand site, and PDZ3, which is characterized by less-intense dynamical variations. We use novel methods of analysis of MD simulations in the unbound and bound states to investigate the determinants of the differential allosteric behavior of the two proteins. The analysis of the correlations between the redistribution of stabilization energy and local fluctuation patterns highlights the nucleus of residues responsible for the stabilization of the 3D fold, the stability core, as the substructure that defines the difference in the allosteric response: in PDZ2, it undergoes a consistent dynamic and energetic reorganization, whereas in PDZ3, it remains largely unperturbed. Specifically, we observe for PDZ2 a significant anticorrelation between the motions of distal loops and residues of the stability core and differences in the correlation patterns between the bound and unbound states. Such variation is not observed in PDZ3, indicating that its energetics and internal dynamics are less affected by the presence/absence of the ligand. Finally, we propose a model with a direct link between the modulation of the structural, energetic and dynamic properties of a protein, and its allosteric response to a perturbation.

R248Q mutation--Beyond p53-DNA binding

R248 in the DNA binding domain (DBD) of p53 interacts directly with the minor groove of DNA. Earlier nuclear magnetic resonance (NMR) studies indicated that the R248Q mutation resulted in conformation changes in parts of DBD far from the mutation site. However, how information propagates from the mutation site to the rest of the DBD is still not well understood. We performed a series of all-atom molecular dynamics (MD) simulations to dissect sterics and charge effects of R248 on p53-DBD conformation: (i) wild-type p53 DBD; (ii) p53 DBD with an electrically neutral arginine side-chain; (iii) p53 DBD with R248A; (iv) p53 DBD with R248W; and (v) p53 DBD with R248Q. Our results agree well with experimental observations of global conformational changes induced by the R248Q mutation. Our simulations suggest that both charge- and sterics are important in the dynamics of the loop (L3) where the mutation resides. We show that helix 2 (H2) dynamics is altered as a result of a change in the hydrogen bonding partner of D281. In turn, neighboring L1 dynamics is altered: in mutants, L1 predominantly adopts the recessed conformation and is unable to interact with the major groove of DNA. We focused our attention the R248Q mutant that is commonly found in a wide range of cancer and observed changes at the zinc-binding pocket that might account for the dominant negative effects of R248Q. Furthermore, in our simulations, the S6/S7 turn was more frequently solvent exposed in R248Q, suggesting that there is a greater tendency of R248Q to partially unfold and possibly lead to an increased aggregation propensity. Finally, based on the observations made in our simulations, we propose strategies for the rescue of R248Q mutants.

Dynamics-Driven Allostery in Protein Kinases

Protein kinases have very dynamic structures and their functionality strongly depends on their dynamic state. Active kinases reveal a dynamic pattern with residues clustering into semirigid communities that move in μs-ms timescale. Previously detected hydrophobic spines serve as connectors between communities. Communities do not follow the traditional subdomain structure of the kinase core or its secondary structure elements. Instead they are organized around main functional units. Integration of the communities depends on the assembly of the hydrophobic spine and phosphorylation of the activation loop. Single mutations can significantly disrupt the dynamic infrastructure and thereby interfere with long-distance allosteric signaling that propagates throughout the whole molecule. Dynamics is proposed to be the underlying mechanism for allosteric regulation in protein kinases.

Small Molecule Targeting of Protein-Protein Interactions through Allosteric Modulation of Dynamics

The protein–protein interaction (PPI) target class is particularly challenging, but offers potential for “first in class” therapies. Most known PPI small molecules are orthosteric inhibitors but many PPI sites may be fundamentally intractable to this approach. One potential alternative is to consider more attractive, remote small molecule pockets; however, on the whole, allostery is poorly understood and difficult to discover and develop. Here we review the literature in order to understand the basis for allostery, especially as it can apply to PPIs. We suggest that the upfront generation of sophisticated and experimentally validated dynamic models of target proteins can aid in target choice and strategy for allosteric intervention to produce the required functional effect.

(Dis)similarity Index To Compare Correlated Motions in Molecular Simulations

Molecular dynamics (MD) simulations are widely used to complement or guide experimental studies in the characterization of protein dynamics, thanks to improvements in force-field accuracy, along with in the software and hardware to sample the conformational landscape of proteins. Among the different applications of MD simulations, the study of correlated motions is largely employed for different purposes. Several metrics have been developed to describe correlated motions in the MD ensemble, such as methods based on Pearson Correlation or Mutual Information. Cross-correlation analysis of MD trajectories is indeed appealing not only to identify residues characterized by coupled fluctuations in protein structures but also since it can be used to extrapolate motions along directions in which major conformational changes should occur, for example on longer time scales than the ones that are actually simulated. Nevertheless, most of the MD studies employ average correlation maps and mostly in a qualitative way, even when different systems or different replicates of the same system are compared. The broad application of correlation metrics in the analysis of MD simulations, especially for comparative purposes, requires a step forward toward more quantitative and accurate comparisons. We thus here employed a simple but effective index, which is based on a normalized Frobenius norm of the differences between protein correlation maps, to compare correlated motions. We applied this index for a quantitative comparison of correlated motions from MD simulations of seven proteins of different size and fold. We also employed the index to assess the robustness of correlation description when multi-replicate MD simulations of a same system are used, and we compared our index to metrics for comparison of structural ensembles such as Root Mean Square Inner Product and the Bhattacharyya Coefficient.

Differences in allosteric communication pipelines in the inactive and active states of a GPCR

G-protein-coupled receptors (GPCRs) are membrane proteins that allosterically transduce the signal of ligand binding in the extracellular (EC) domain to couple to proteins in the intracellular (IC) domain. However, the complete pathway of allosteric communication from the EC to the IC domain, including the role of individual amino acids in the pathway is not known. Using the correlation in torsion angle movements calculated from microseconds-long molecular-dynamics simulations, we elucidated the allosteric pathways in three different conformational states of β2-adrenergic receptor (β2AR): 1), the inverse-agonist-bound inactive state; 2), the agonist-bound intermediate state; and (3), the agonist- and G-protein-bound fully active state. The inactive state is less dynamic compared with the intermediate and active states, showing dense clusters of allosteric pathways (allosteric pipelines) connecting the EC with the IC domain. The allosteric pipelines from the EC domain to the IC domain are weakened in the intermediate state, thus decoupling the EC domain from the IC domain and making the receptor more dynamic compared with the other states. Also, the orthosteric ligand-binding site becomes the initiator region for allosteric communication in the intermediate state. This finding agrees with the paradigm that the nature of the agonist governs the specific signaling state of the receptor. These results provide an understanding of the mechanism of allosteric communication in class A GPCRs. In addition, our analysis shows that mutations that affect the ligand efficacy, but not the binding affinity, are located in the allosteric pipelines. This clarifies the role of such mutations, which has hitherto been unexplained.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

NMR and MD Studies Reveal That the Isolated Dengue NS3 Protease Is an Intrinsically Disordered Chymotrypsin Fold Which Absolutely Requests NS2B for Correct Folding and Functional Dynamics

Dengue genome encodes a two component protease complex (NS2B-NS3pro) essential for the viral maturation/infectivity, thus representing a key drug target. Previously, due to its “complete insolubility”, the isolated NS3pro could not be experimentally studied and it remains elusive what structure it adopts without NS2B and why NS2B is indispensable. Here as facilitated by our previous discovery, the isolated NS3pro has been surprisingly deciphered by NMR to be the first intrinsically-disordered chymotrypsin-like fold, which exists in a loosely-packed state with non-native long-range interactions as revealed by paramagnetic relaxation enhancement (PRE). The disordered NS3pro appears to be needed for binding a human host factor to trigger the membrane remodeling. Moreover, we have in vitro refolded the NS3pro in complex with either NS2B (48–100) or the full-length NS2B (1–130) anchored into the LMPC micelle, and the two complexes have similar activities but different dynamics. We also performed molecular dynamics (MD) simulations and the results revealed that NS2B shows the highest structural fluctuations in the complex, thus providing the dynamic basis for the observation on its conformational exchange between open and closed states. Remarkably, the NS2B cofactor plays a central role in maintaining the correlated motion network required for the catalysis as we previously decoded for the SARS 3CL protease. Indeed, a truncated NS2B (48–100;Δ77–84) with the flexible loop deleted is able to trap the NS2B-NS3pro complex in a highly dynamic and catalytically-impotent state. Taken together, our study implies potential strategies to perturb the NS2B-NS3pro interface for design of inhibitors for treating dengue infection.

Investigating Dynamic Interdomain Allostery in Pin1

Signaling proteins often sequester complementary functional sites in separate domains. How do the different domains communicate with one another? An attractive system to address this question is the mitotic regulator, human Pin1 (Lu et al. 1996). Pin-1 consists of two tethered domains: a WW domain for substrate binding, and a catalytic domain for peptidyl-prolyl isomerase (PPIase) activity. Pin1 accelerates the cis-trans isomerization of phospho-Ser/Thr-Pro (pS/T-P) motifs within proteins regulating the cell cycle and neuronal development. The early x-ray (Ranganathan et al. 1997; Verdecia et al. 2000) and solution NMR studies (Bayer et al. 2003; Jacobs et al. 2003) of Pin1 indicated inter- and intradomain motion. We became interested in exploring how such motions might affect interdomain communication, using NMR. Our accumulated results indicate substrate binding to Pin1 WW domain changes the intra/inter domain mobility, thereby altering substrate activity in the distal PPIase domain catalytic site. Thus, Pin1 shows evidence of dynamic allostery, in the sense of Cooper and Dryden (Cooper and Dryden 1984). We highlight our results supporting this conclusion, and summarize them via a simple speculative model of conformational selection.

Fluctuations within folded proteins: implications for thermodynamic and allosteric regulation

Folded protein structures are both stable and dynamic. Historically, our clearest window into these structures came from X-ray crystallography, which generally provided a static image of each protein's singular "folded state", highlighting its stability. Deviations away from that crystallographic structure were difficult to quantify, and as a result, their potential functional consequences were often neglected. However, several dynamical and statistical studies now highlight the structural variability that is present within the protein's folded state. Here we review mounting evidence of the importance of these structural rearrangements; both experiment and computation indicate that folded proteins undergo substantial fluctuations that can greatly influence their function. Crucially, recent studies have shown that structural elements of proteins, especially their side-chain degrees of freedom, fluctuate in ways that generate significant conformational heterogeneity. The entropy associated with these motions contributes to the folded structure's thermodynamic stability. In addition, since these fluctuations can shift in response to perturbations such as ligand binding, they may play an important role in the protein's capacity to respond to environmental cues. In one compelling example, the entropy associated with side-chain fluctuations contributes significantly to regulating the binding of calmodulin to a set of peptide ligands. The neglect of fluctuations within proteins' native states was often justified by the dense packing within folded proteins, which has inspired comparisons with crystalline solids. Many liquids, however, can achieve similarly dense packing yet fluidity is maintained through correlated molecular motions. Indeed, the studies we discuss favor comparison of folded proteins not with solids but instead with dense liquids, where the internal side chain fluidity is facilitated by collective motions that are correlated over long distances. These correlated rearrangements can enable allosteric communication between different parts of a protein, through subtle and varied channels. Such long-range correlations appear to be an innate feature of proteins in general, manifest even in molecules lacking known allosteric regulators and arising robustly from the physical nature of their internal environment. Given their ubiquity, it is only to be expected that, over time, nature has refined some subset of these correlated motions and put them to use. Native state fluctuations increasingly appear to be vital for proteins' natural functions. Understanding the diversity, origin, and range of these rearrangements may provide novel routes for rationally manipulating biomolecular activity.

Molecular dynamics study of naturally existing cavity couplings in proteins

Couplings between protein sub-structures are a common property of protein dynamics. Some of these couplings are especially interesting since they relate to function and its regulation. In this article we have studied the case of cavity couplings because cavities can host functional sites, allosteric sites, and are the locus of interactions with the cell milieu. We have divided this problem into two parts. In the first part, we have explored the presence of cavity couplings in the natural dynamics of 75 proteins, using 20 ns molecular dynamics simulations. For each of these proteins, we have obtained two trajectories around their native state. After applying a stringent filtering procedure, we found significant cavity correlations in 60% of the proteins. We analyze and discuss the structure origins of these correlations, including neighbourhood, cavity distance, etc. In the second part of our study, we have used longer simulations (≥100 ns) from the MoDEL project, to obtain a broader view of cavity couplings, particularly about their dependence on time. Using moving window computations we explored the fluctuations of cavity couplings along time, finding that these couplings could fluctuate substantially during the trajectory, reaching in several cases correlations above 0.25/0.5. In summary, we describe the structural origin and the variations with time of cavity couplings. We complete our work with a brief discussion of the biological implications of these results.

Dihedral angle entropy measures for intrinsically disordered proteins

Protein stability is based on a delicate balance between energetic and entropic factors. Intrinsically disordered proteins (IDPs) interacting with a folded partner protein in the act of binding can order the IDP to form the correct functional interface by decrease in the overall free energy. In this work, we evaluate the part of the entropic cost of ordering an IDP arising from their dihedral states. The IDP studied is a leucine zipper dimer that we simulate with molecular dynamics and find that it does show disorder in six phi and psi dihedral angles of the N terminal sequence of one monomer. Essential to ascertain is the degree of disorder in the IDP, and we do so by considering the entire, discretized probability distribution function of N dihedrals with M conformers per dihedral. A compositional clustering method is introduced, whereby the NS = N(M) states are formed from the Cartesian product of each dihedral's conformational space. Clustering is carried out with a version of a k-means algorithm that accounts for the circular nature of dihedral angles. For the 12 dihedrals each found to have three conformers, among the resulting 531441 states, their populations show that the first 100 (500) most populated states account for ∼65% (∼90%) of the entire population, indicating that there are strong dependencies among the dihedrals' conformations. These state populations are used to evaluate a Kullback-Leibler divergence entropy measure and obtain the dihedral configurational entropy S. At 300 K, TS ∼ 3 kcal/mol, showing that IDP entropy, while roughly half that would be expected from independently distributed dihedrals, can be a decisive contributor to the free energy of this IDP binding and ordering.

Discovery of multiple hidden allosteric sites by combining Markov state models and experiments

The discovery of drug-like molecules that bind pockets in proteins that are not present in crystallographic structures yet exert allosteric control over activity has generated great interest in designing pharmaceuticals that exploit allosteric effects. However, there have only been a small number of successes, so the therapeutic potential of these pockets--called hidden allosteric sites--remains unclear. One challenge for assessing their utility is that rational drug design approaches require foreknowledge of the target site, but most hidden allosteric sites are only discovered when a small molecule is found to stabilize them. We present a means of decoupling the identification of hidden allosteric sites from the discovery of drugs that bind them by drawing on new developments in Markov state modeling that provide unprecedented access to microsecond- to millisecond-timescale fluctuations of a protein's structure. Visualizing these fluctuations allows us to identify potential hidden allosteric sites, which we then test via thiol labeling experiments. Application of these methods reveals multiple hidden allosteric sites in an important antibiotic target--TEM-1 β-lactamase. This result supports the hypothesis that there are many as yet undiscovered hidden allosteric sites and suggests our methodology can identify such sites, providing a starting point for future drug design efforts. More generally, our results demonstrate the power of using Markov state models to guide experiments.

A Fragment-Based Ligand Screen Against Part of a Large Protein Machine: The ND1 Domains of the AAA+ ATPase p97/VCP

The ubiquitous AAA+ ATPase p97 functions as a dynamic molecular machine driving several cellular processes. It is essential in regulating protein homeostasis, and it represents a potential drug target for cancer, particularly when there is a greater reliance on the endoplasmic reticulum-associated protein degradation pathway and ubiquitin-proteasome pathway to degrade an overabundance of secreted proteins. Here, we report a case study for using fragment-based ligand design approaches against this large and dynamic hexamer, which has multiple potential binding sites for small molecules. A screen of a fragment library was conducted by surface plasmon resonance (SPR) and followed up by nuclear magnetic resonance (NMR), two complementary biophysical techniques. Virtual screening was also carried out to examine possible binding sites for the experimental hits and evaluate the potential utility of fragment docking for this target. Out of this effort, 13 fragments were discovered that showed reversible binding with affinities between 140 µM and 1 mM, binding stoichiometries of 1:1 or 2:1, and good ligand efficiencies. Structural data for fragment-protein interactions were obtained with residue-specific [U-(2)H] (13)CH3-methyl-labeling NMR strategies, and these data were compared to poses from docking. The combination of virtual screening, SPR, and NMR enabled us to find and validate a number of interesting fragment hits and allowed us to gain an understanding of the structural nature of fragment binding.

Application of information theory to a three-body coarse-grained representation of proteins in the PDB: insights into the structural and evolutionary roles of residues in protein structure

Knowledge-based methods for analyzing protein structures, such as statistical potentials, primarily consider the distances between pairs of bodies (atoms or groups of atoms). Considerations of several bodies simultaneously are generally used to characterize bonded structural elements or those in close contact with each other, but historically do not consider atoms that are not in direct contact with each other. In this report, we introduce an information-theoretic method for detecting and quantifying distance-dependent through-space multibody relationships between the sidechains of three residues. The technique introduced is capable of producing convergent and consistent results when applied to a sufficiently large database of randomly chosen, experimentally solved protein structures. The results of our study can be shown to reproduce established physico-chemical properties of residues as well as more recently discovered properties and interactions. These results offer insight into the numerous roles that residues play in protein structure, as well as relationships between residue function, protein structure, and evolution. The techniques and insights presented in this work should be useful in the future development of novel knowledge-based tools for the evaluation of protein structure.

Local packing modulates diversity of iron pathways and cooperative behavior in eukaryotic and prokaryotic ferritins

Ferritin-like molecules show a remarkable combination of the evolutionary conserved activity of iron uptake and release that engage different pores in the conserved ferritin shell. It was hypothesized that pore selection and iron traffic depend on dynamic allostery with no conformational changes in the backbone. In this study, we detect the allosteric networks in Pseudomonas aeruginosa bacterioferritin (BfrB), bacterial ferritin (FtnA), and bullfrog M and L ferritins (Ftns) by a network-weaving algorithm (NWA) that passes threads of an allosteric network through highly correlated residues using hierarchical clustering. The residue-residue correlations are calculated in the packing-on elastic network model that introduces atom packing into the common packing-off model. Applying NWA revealed that each of the molecules has an extended allosteric network mostly buried inside the ferritin shell. The structure of the networks is consistent with experimental observations of iron transport: The allosteric networks in BfrB and FtnA connect the ferroxidase center with the 4-fold pores and B-pores, leaving the 3-fold pores unengaged. In contrast, the allosteric network directly links the 3-fold pores with the 4-fold pores in M and L Ftns. The majority of the network residues are either on the inner surface or buried inside the subunit fold or at the subunit interfaces. We hypothesize that the ferritin structures evolved in a way to limit the influence of functionally unrelated events in the cytoplasm on the allosteric network to maintain stability of the translocation mechanisms. We showed that the residue-residue correlations and the resultant long-range cooperativity depend on the ferritin shell packing, which, in turn, depends on protein sequence composition. Switching from the packing-on to the packing-off model reduces correlations by 35%-38% so that no allosteric network can be found. The influence of the side-chain packing on the allosteric networks explains the diversity in mechanisms of iron traffic suggested by experimental approaches.

Synchronous opening and closing motions are essential for cAMP-dependent protein kinase A signaling

Conformational fluctuations play a central role in enzymatic catalysis. However, it is not clear how the rates and the coordination of the motions affect the different catalytic steps. Here, we used NMR spectroscopy to analyze the conformational fluctuations of the catalytic subunit of the cAMP-dependent protein kinase (PKA-C), a ubiquitous enzyme involved in a myriad of cell signaling events. We found that the wild-type enzyme undergoes synchronous motions involving several structural elements located in the small lobe of the kinase, which is responsible for nucleotide binding and release. In contrast, a mutation (Y204A) located far from the active site desynchronizes the opening and closing of the active cleft without changing the enzyme's structure, rendering it catalytically inefficient. Since the opening and closing motions govern the rate-determining product release, we conclude that optimal and coherent conformational fluctuations are necessary for efficient turnover of protein kinases.

Dynamic architecture of a protein kinase

Protein kinases are dynamically regulated signaling proteins that act as switches in the cell by phosphorylating target proteins. To establish a framework for analyzing linkages between structure, function, dynamics, and allostery in protein kinases, we carried out multiple microsecond-scale molecular-dynamics simulations of protein kinase A (PKA), an exemplar active kinase. We identified residue-residue correlated motions based on the concept of mutual information and used the Girvan-Newman method to partition PKA into structurally contiguous "communities." Most of these communities included 40-60 residues and were associated with a particular protein kinase function or a regulatory mechanism, and well-known motifs based on sequence and secondary structure were often split into different communities. The observed community maps were sensitive to the presence of different ligands and provide a new framework for interpreting long-distance allosteric coupling. Communication between different communities was also in agreement with the previously defined architecture of the protein kinase core based on the "hydrophobic spine" network. This finding gives us confidence in suggesting that community analyses can be used for other protein kinases and will provide an efficient tool for structural biologists. The communities also allow us to think about allosteric consequences of mutations that are linked to disease.

Probing structural adaptivity at PPI interfaces with small molecules

There is strong interest in developing small molecules that modulate protein-protein interactions (PPI), since such compounds could serve as drug leads or as probes of protein function. Fragment-based ligand discovery has been a particularly useful approach for modulating PPI. Fragments are typically <250 Da compounds that bind to proteins with weak affinity but high ligand efficiency. Here, we review a method for fragment- based ligand discovery using covalent disulfide trapping (Tethering). Tethering uses a native or engineered cysteine residue to select thiol-containing fragments that bind to the protein near the tethering cysteine. Taking advantage of the site-directed nature of Tethering, one can investigate the 'druggability' of particular binding sites on a protein surface; furthermore, Tethering has been used to find new binding sites and to stabilize allosteric conformations. We review the principles of Tethering and discuss two examples where disulfide trapping has expanded our understanding of PPI. For the cytokine interleukin-2 (IL2), Tethering identified a binding site adjacent to the IL2/IL2- receptor and a new site allosterically coupled to this PPI. For the kinase PDK1, Tethering identified ligands that activated or inhibited enzymatic activity by bind-ing to a single allosteric site. These examples provide a context for successful fragment-discovery projects, in which complementary technologies work together to identify starting points for chemical biology and drug discovery.

Allosteric mechanisms of nuclear receptors: insights from computational simulations

The traditional structural view of allostery defines this key regulatory mechanism as the ability of one conformational event (allosteric site) to initiate another in a separate location (active site). In recent years computational simulations conducted to understand how this phenomenon occurs in nuclear receptors (NRs) has gained significant traction. These results have yield insights into allosteric changes and communication mechanisms that underpin ligand binding, coactivator binding site formation, post-translational modifications, and oncogenic mutations. Moreover, substantial efforts have been made in understanding the dynamic processes involved in ligand binding and coregulator recruitment to different NR conformations in order to predict cell/tissue-selective pharmacological outcomes of drugs. They also have improved the accuracy of in silico screening protocols so that nowadays they are becoming part of optimisation protocols for novel therapeutics. Here we summarise the important contributions that computational simulations have made towards understanding the structure/function relationships of NRs and how these can be exploited for rational drug design.

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

Dynamically-driven enhancement of the catalytic machinery of the SARS 3C-like protease by the S284-T285-I286/A mutations on the extra domain

Previously we revealed that the extra domain of SARS 3CLpro mediated the catalysis via different mechanisms. While the R298A mutation completely abolished the dimerization, thus resulting in the inactive catalytic machinery, N214A inactivated the enzyme by altering its dynamics without significantly perturbing its structure. Here we studied another mutant with S284-T285-I286 replaced by Ala (STI/A) with a 3.6-fold activity increase and slightly enhanced dimerization. We determined its crystal structure, which still adopts the dimeric structure almost identical to that of the wild-type (WT), except for slightly tighter packing between two extra-domains. We then conducted 100-ns molecular dynamics (MD) simulations for both STI/A and WT, the longest reported so far for 3CLpro. In the simulations, two STI/A extra domains become further tightly packed, leading to a significant volume reduction of the nano-channel formed by residues from both catalytic and extra domains. The enhanced packing appears to slightly increase the dynamic stability of the N-finger and the first helix residues, which subsequently triggers the redistribution of dynamics over residues directly contacting them. This ultimately enhances the dynamical stability of the residues constituting the catalytic dyad and substrate-binding pockets. Further correlation analysis reveals that a global network of the correlated motions exists in the protease, whose components include all residues identified so far to be critical for the dimerization and catalysis. Most strikingly, the N214A mutation globally decouples this network while the STI/A mutation alters the correlation pattern. Together with previous results, the present study establishes that besides the classic structural allostery, the dynamic allostery also operates in the SARS 3CLpro, which is surprisingly able to relay the perturbations on the extra domain onto the catalytic machinery to manifest opposite catalytic effects. Our results thus imply a promising avenue to design specific inhibitors for 3CL proteases by disrupting their dynamic correlation network.

Analysis on long-range residue-residue communication using molecular dynamics

We investigated the possibility of inter-residue communication of side chains in barstar, an 89 residue protein, using mutual information theory. The normalized mutual information (NMI) of the dihedral angles of the side chains was obtained from all-atom molecular dynamics simulations. The accumulated NMI from an explicit solvent equilibrated trajectory (600 ns) with free backbone exhibits a parabola-shaped distribution over the inter-residue distances (0-36 Å): smaller at the end regimes but larger in the middle regime. This analysis, plus several other measures, does not find unusual long-range communication for free backbone in explicit solvent simulations.

Case study on temperature-accelerated molecular dynamics simulation of ligand dissociation: inducer dissociation from the Lac repressor protein

We studied ligand dissociation from the inducer-binding domain of the Lac repressor protein using temperature-accelerated molecular dynamics (TAMD) simulations. With TAMD, ligand dissociation could be observed within relatively short simulation time. This allowed many dissociation trajectories to be sampled. Under the adiabatic approximation of TAMD, all but one degree of freedom of the system were sampled from usual canonical ensembles at room temperature. Thus, meaningful statistical analyses could be carried out on the trajectories. A systematic approach was proposed to analyze possible correlations between ligand dissociation and fluctuations of various protein conformational coordinates. These analyses employed relative entropies, allowing both linear and nonlinear correlations to be considered. Applying the simulation and analysis methods to the inducer binding domain of the Lac repressor protein, we found that ligand dissociation from this protein correlated mainly with fluctuations of side-chain conformations of a few residues that surround the binding pocket. In addition, the two binding sites of the dimeric protein were dynamically coupled: occupation of one site by an inducer molecule could significantly reduce or slow down conformational dynamics around the other binding pocket.

Achieving peptide binding specificity and promiscuity by loops: case of the forkhead-associated domain

The regulation of a series of cellular events requires specific protein–protein interactions, which are usually mediated by modular domains to precisely select a particular sequence from diverse partners. However, most signaling domains can bind to more than one peptide sequence. How do proteins create promiscuity from precision? Moreover, these complex interactions typically occur at the interface of a well-defined secondary structure, α helix and β sheet. However, the molecular recognition primarily controlled by loop architecture is not fully understood. To gain a deep understanding of binding selectivity and promiscuity by the conformation of loops, we chose the forkhead-associated (FHA) domain as our model system. The domain can bind to diverse peptides via various loops but only interact with sequences containing phosphothreonine (pThr). We applied molecular dynamics (MD) simulations for multiple free and bound FHA domains to study the changes in conformations and dynamics. Generally, FHA domains share a similar folding structure whereby the backbone holds the overall geometry and the variety of sidechain atoms of multiple loops creates a binding surface to target a specific partner. FHA domains determine the specificity of pThr by well-organized binding loops, which are rigid to define a phospho recognition site. The broad range of peptide recognition can be attributed to different arrangements of the loop interaction network. The moderate flexibility of the loop conformation can help access or exclude binding partners. Our work provides insights into molecular recognition in terms of binding specificity and promiscuity and helpful clues for further peptide design.

Correlation as a determinant of configurational entropy in supramolecular and protein systems

For biomolecules in solution, changes in configurational entropy are thought to contribute substantially to the free energies of processes like binding and conformational change. In principle, the configurational entropy can be strongly affected by pairwise and higher-order correlations among conformational degrees of freedom. However, the literature offers mixed perspectives regarding the contributions that changes in correlations make to changes in configurational entropy for such processes. Here we take advantage of powerful techniques for simulation and entropy analysis to carry out rigorous in silico studies of correlation in binding and conformational changes. In particular, we apply information-theoretic expansions of the configurational entropy to well-sampled molecular dynamics simulations of a model host–guest system and the protein bovine pancreatic trypsin inhibitor. The results bear on the interpretation of NMR data, as they indicate that changes in correlation are important determinants of entropy changes for biologically relevant processes and that changes in correlation may either balance or reinforce changes in first-order entropy. The results also highlight the importance of main-chain torsions as contributors to changes in protein configurational entropy. As simulation techniques grow in power, the mathematical techniques used here will offer new opportunities to answer challenging questions about complex molecular systems.

Unraveling structural mechanisms of allosteric drug action

Orthosteric drugs block the active site to obstruct function; allosteric drugs modify the population of the active state, to modulate function. Available data lead us to propose that allosteric drugs can constitute anchors and drivers. The anchor docks into an allosteric pocket. The conformation with which it interacts is unchanged during the transition between the inactive and active states. The anchor provides the foundation that allows the driver to exert a 'pull' and/or 'push' action that shifts the receptor population from the inactive to the active state. The presence or absence of driver atom in an allosteric drug can exert opposite agonism. We map a strategy for driver identification and expect the allosteric trigger concept to transform agonist/antagonist drug discovery.

Computational approaches to mapping allosteric pathways

Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.

Current and emerging opportunities for molecular simulations in structure-based drug design

An overview of the current capabilities and limitations of molecular simulation of biomolecular complexes in the context of computer-aided drug design is provided. Steady improvements in computer hardware coupled with more refined representations of energetics are leading to a new appreciation of the driving forces of molecular recognition. Molecular simulations are poised to more frequently guide the interpretation of biophysical measurements of biomolecular complexes. Ligand design strategies emerge from detailed analyses of computed structural ensembles. The feasibility of routine applications to ligand optimization problems hinges upon successful extensive large scale validation studies and the development of protocols to intelligently automate computations.

Extensive conformational heterogeneity within protein cores

Basic principles of statistical mechanics require that proteins sample an ensemble of conformations at any nonzero temperature. However, it is still common to treat the crystallographic structure of a protein as the structure of its native state, largely because high-resolution structural characterization of protein flexibility remains a profound challenge. To assess the typical degree of conformational heterogeneity within folded proteins, we construct Markov state models describing the thermodynamics and kinetics of proteins ranging from 72 to 263 residues in length. Each of these models is built from hundreds of microseconds of atomically detailed molecular dynamics simulations. Examination of the side-chain degrees of freedom reveals that almost every residue visits at least two rotameric states over this time frame, with rotamer transition rates spanning a wide range of time scales (from nanoseconds to tens of microseconds). We also report substantial backbone dynamics on time scales longer than are typically addressed by experimental measures of protein flexibility, such as NMR order parameters. Finally, we demonstrate that these extensive rearrangements are consistent with NMR and crystallographic data, which supports the validity of our models. Altogether, these results depict the interior of proteins not as well-ordered solids, as is often imagined, but instead as dense fluids, which undergo substantial structural fluctuations despite their high packing fraction.

Activation pathway of Src kinase reveals intermediate states as targets for drug design

Unregulated activation of Src kinases leads to aberrant signaling, uncontrolled growth, and differentiation of cancerous cells. Reaching a complete mechanistic understanding of large scale conformational transformations underlying the activation of kinases could greatly help in the development of therapeutic drugs for the treatment of these pathologies. In principle, the nature of conformational transition could be modeled in silico via atomistic molecular dynamics simulations, although this is very challenging due to the long activation timescales. Here, we employ a computational paradigm that couples transition pathway techniques and Markov state model-based massively distributed simulations for mapping the conformational landscape of c-src tyrosine kinase. The computations provide the thermodynamics and kinetics of kinase activation for the first time, and help identify key structural intermediates. Furthermore, the presence of a novel allosteric site in an intermediate state of c-src that could be potentially utilized for drug design is predicted.

Two polymorphisms facilitate differences in plasticity between two chicken major histocompatibility complex class I proteins

Major histocompatibility complex class I molecules (MHC I) present peptides to cytotoxic T-cells at the surface of almost all nucleated cells. The function of MHC I molecules is to select high affinity peptides from a large intracellular pool and they are assisted in this process by co-factor molecules, notably tapasin. In contrast to mammals, MHC homozygous chickens express a single MHC I gene locus, termed BF2, which is hypothesised to have co-evolved with the highly polymorphic tapasin within stable haplotypes. The BF2 molecules of the B15 and B19 haplotypes have recently been shown to differ in their interactions with tapasin and in their peptide selection properties. This study investigated whether these observations might be explained by differences in the protein plasticity that is encoded into the MHC I structure by primary sequence polymorphisms. Furthermore, we aimed to demonstrate the utility of a complimentary modelling approach to the understanding of complex experimental data. Combining mechanistic molecular dynamics simulations and the primary sequence based technique of statistical coupling analysis, we show how two of the eight polymorphisms between BF2*15∶01 and BF2*19∶01 facilitate differences in plasticity. We show that BF2*15∶01 is intrinsically more plastic than BF2*19∶01, exploring more conformations in the absence of peptide. We identify a protein sector of contiguous residues connecting the membrane bound α3 domain and the heavy chain peptide binding site. This sector contains two of the eight polymorphic residues. One is residue 22 in the peptide binding domain and the other 220 is in the α3 domain, a putative tapasin binding site. These observations are in correspondence with the experimentally observed functional differences of these molecules and suggest a mechanism for how modulation of MHC I plasticity by tapasin catalyses peptide selection allosterically.