Computation of conformational coupling in allosteric proteins

RNA's Dynamic Conformational Selection and Entropic Allosteric Mechanism in Controlling Cascade Protein Binding Events

In the TAR RNA of immunodeficiency viruses, an allosteric communication exists between a distant loop and a bulge. The bulge interacts with the TAT protein vital for transactivating viral RNA, while the loop interacts with cyclin-T1, contingent on TAT binding. Through extensive atomistic and free energy simulations, we investigate TAR-TAT binding in nonpathogenic bovine immunodeficiency virus (BIV) and pathogenic human immunodeficiency virus (HIV). Thermodynamic analysis reveals enthalpically driven binding in BIV and entropically favored binding in HIV. The broader global basin in HIV is attributed to binding-induced loop fluctuation, corroborated by nuclear magnetic resonance (NMR), indicating classical entropic allostery onset. While this loop fluctuation affects the TAT binding affinity, it generates a binding-competent conformation that aids subsequent effector (cyclin-T1) binding. This study underscores how two structurally similar apo-RNA scaffolds adopt distinct conformational selection mechanisms to drive enthalpic and entropic allostery, influencing protein affinity in the signaling cascade.

Sparse group selection and analysis of function-related residue for protein-state recognition

Machine learning methods have helped to advance wide range of scientific and technological field in recent years, including computational chemistry. As the chemical systems could become complex with high dimension, feature selection could be critical but challenging to develop reliable machine learning based prediction models, especially for proteins as bio-macromolecules. In this study, we applied sparse group lasso (SGL) method as a general feature selection method to develop classification model for an allosteric protein in different functional states. This results into a much improved model with comparable accuracy (Acc) and only 28 selected features comparing to 289 selected features from a previous study. The Acc achieves 91.50% with 1936 selected feature, which is far higher than that of baseline methods. In addition, grouping protein amino acids into secondary structures provides additional interpretability of the selected features. The selected features are verified as associated with key allosteric residues through comparison with both experimental and computational works about the model protein, and demonstrate the effectiveness and necessity of applying rigorous feature selection and evaluation methods on complex chemical systems.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

Structure and Dynamics of the EGF Receptor as Revealed by Experiments and Simulations and Its Relevance to Non-Small Cell Lung Cancer

The epidermal growth factor receptor (EGFR) is historically the prototypical receptor tyrosine kinase, being the first cloned and the first where the importance of ligand-induced dimer activation was ascertained. However, many years of structure determination has shown that EGFR is not completely understood. One challenge is that the many structure fragments stored at the PDB only provide a partial view because full-length proteins are flexible entities and dynamics play a key role in their functionality. Another challenge is the shortage of high-resolution data on functionally important higher-order complexes. Still, the interest in the structure/function relationships of EGFR remains unabated because of the crucial role played by oncogenic EGFR mutants in driving non-small cell lung cancer (NSCLC). Despite targeted therapies against EGFR setting a milestone in the treatment of this disease, ubiquitous drug resistance inevitably emerges after one year or so of treatment. The magnitude of the challenge has inspired novel strategies. Among these, the combination of multi-disciplinary experiments and molecular dynamic (MD) simulations have been pivotal in revealing the basic nature of EGFR monomers, dimers and multimers, and the structure-function relationships that underpin the mechanisms by which EGFR dysregulation contributes to the onset of NSCLC and resistance to treatment.

Glareosin: a novel sexually dimorphic urinary lipocalin in the bank vole, Myodes glareolus

The urine of bank voles (Myodes glareolus) contains substantial quantities of a small protein that is expressed at much higher levels in males than females, and at higher levels in males in the breeding season. This protein was purified and completely sequenced at the protein level by mass spectrometry. Leucine/isoleucine ambiguity was completely resolved by metabolic labelling, monitoring the incorporation of dietary deuterated leucine into specific sites in the protein. The predicted mass of the sequenced protein was exactly consonant with the mass of the protein measured in bank vole urine samples, correcting for the formation of two disulfide bonds. The sequence of the protein revealed that it was a lipocalin related to aphrodisin and other odorant-binding proteins (OBPs), but differed from all OBPs previously described. The pattern of secretion in urine used for scent marking by male bank voles, and the similarity to other lipocalins used as chemical signals in rodents, suggest that this protein plays a role in male sexual and/or competitive communication. We propose the name glareosin for this novel protein to reflect the origin of the protein and to emphasize the distinction from known OBPs.

Recognition of protein allosteric states and residues: Machine learning approaches

Allostery is a process by which proteins transmit the effect of perturbation at one site to a distal functional site upon certain perturbation. As an intrinsically global effect of protein dynamics, it is difficult to associate protein allostery with individual residues, hindering effective selection of key residues for mutagenesis studies. The machine learning models including decision tree (DT) and artificial neural network (ANN) models were applied to develop classification model for a cell signaling allosteric protein with two states showing extremely similar tertiary structures in both crystallographic structures and molecular dynamics simulations. Both DT and ANN models were developed with 75% and 80% of predicting accuracy, respectively. Good agreement between machine learning models and previous experimental as well as computational studies of the same protein validates this approach as an alternative way to analyze protein dynamics simulations and allostery. In addition, the difference of distributions of key features in two allosteric states also underlies the population shift hypothesis of dynamics-driven allostery model. © 2018 Wiley Periodicals, Inc.

Dehydration and Cognition in Geriatrics: A Hydromolecular Hypothesis

Dehydration is one of the ten most frequent diagnoses responsible for the hospital admission of elderly in the United States. It is associated with increased mortality, morbidity and an estimated cost of 1.14 billion per year (Xiao et al., 2004; Schlanger et al., 2010; Pretorius et al., 2013; Frangeskou et al., 2015). Older individuals are predisposed to dehydration encephalopathy as a result of decreased total body water (TBW) and diminished sensation of thirst. We hypothesize that thirst blunting in older individuals is the result of a defective microRNA-6842-3p failing to silence the expression of the vesicular GABA transporters (VGAT) and alpha 7 cholinergic nicotinic receptors in the subfornical organ (SFO) of the hypothalamus. We hypothesize further that resultant dehydration facilitates protein misfolding and aggregation, predisposing to neurocognitive disorders. We completed a search of predicted microRNA targets, utilizing the public domain tool miRDB and found that microRNA-6842-3p modulates the SLC6A1 and CHRNA7 genes both of which were previously hypothesized to inhibit the thirst sensation by their action on SFO. The primary aim of this article is to answer two questions: Can prevention and correction of dehydration in elderly lower age-related cognitive deterioration? Can exosomal miR-6842 in the peripheral blood predict dehydration encephalopathy in elderly?

Rigid Residue Scan Simulations Systematically Reveal Residue Entropic Roles in Protein Allostery

Intra-protein information is transmitted over distances via allosteric processes. This ubiquitous protein process allows for protein function changes due to ligand binding events. Understanding protein allostery is essential to understanding protein functions. In this study, allostery in the second PDZ domain (PDZ2) in the human PTP1E protein is examined as model system to advance a recently developed rigid residue scan method combining with configurational entropy calculation and principal component analysis. The contributions from individual residues to whole-protein dynamics and allostery were systematically assessed via rigid body simulations of both unbound and ligand-bound states of the protein. The entropic contributions of individual residues to whole-protein dynamics were evaluated based on covariance-based correlation analysis of all simulations. The changes of overall protein entropy when individual residues being held rigid support that the rigidity/flexibility equilibrium in protein structure is governed by the La Châtelier's principle of chemical equilibrium. Key residues of PDZ2 allostery were identified with good agreement with NMR studies of the same protein bound to the same peptide. On the other hand, the change of entropic contribution from each residue upon perturbation revealed intrinsic differences among all the residues. The quasi-harmonic and principal component analyses of simulations without rigid residue perturbation showed a coherent allosteric mode from unbound and bound states, respectively. The projection of simulations with rigid residue perturbation onto coherent allosteric modes demonstrated the intrinsic shifting of ensemble distributions supporting the population-shift theory of protein allostery. Overall, the study presented here provides a robust and systematic approach to estimate the contribution of individual residue internal motion to overall protein dynamics and allostery.

The Conformational Variability of FimH: Which Conformation Represents the Therapeutic Target?

FimH is a bacterial lectin found at the tips of type 1 pili of uropathogenic Escherichia coli (UPEC). It mediates shear-enhanced adhesion to mannosylated surfaces. Binding of UPEC to urothelial cells initiates the infection cycle leading to urinary tract infections (UTIs). Antiadhesive glycomimetics based on α-d-mannopyranose offer an attractive alternative to the conventional antibiotic treatment because they do not induce a selection pressure and are therefore expected to have a reduced resistance potential. Genetic variation of the fimH gene in clinically isolated UPEC has been associated with distinct mannose binding phenotypes. For this reason, we investigated the mannose binding characteristics of four FimH variants with mannose-based ligands under static and hydrodynamic conditions. The selected FimH variants showed individually different binding behavior under both sets of conditions as a result of the conformational variability of FimH. Clinically relevant FimH variants typically exist in a dynamic conformational equilibrium. Additionally, we evaluated inhibitory potencies of four FimH antagonists representing different structural classes. Inhibitory potencies of three of the tested antagonists were dependent on the binding phenotype and hence on the conformational equilibrium of the FimH variant. However, the squarate derivative was the notable exception and inhibited FimH variants irrespective of their binding phenotype. Information on antagonist affinities towards various FimH variants has remained largely unconsidered despite being essential for successful antiadhesion therapy.

DIRECT-ID: An automated method to identify and quantify conformational variations--application to β2 -adrenergic GPCR

The conformational dynamics of a macromolecule can be modulated by a number of factors, including changes in environment, ligand binding, and interactions with other macromolecules, among others. We present a method that quantifies the differences in macromolecular conformational dynamics and automatically extracts the structural features responsible for these changes. Given a set of molecular dynamics (MD) simulations of a macromolecule, the norms of the differences in covariance matrices are calculated for each pair of trajectories. A matrix of these norms thus quantifies the differences in conformational dynamics across the set of simulations. For each pair of trajectories, covariance difference matrices are parsed to extract structural elements that undergo changes in conformational properties. As a demonstration of its applicability to biomacromolecular systems, the method, referred to as DIRECT-ID, was used to identify relevant ligand-modulated structural variations in the β2 -adrenergic (β2 AR) G-protein coupled receptor. Micro-second MD simulations of the β2 AR in an explicit lipid bilayer were run in the apo state and complexed with the ligands: BI-167107 (agonist), epinephrine (agonist), salbutamol (long-acting partial agonist), or carazolol (inverse agonist). Each ligand modulated the conformational dynamics of β2 AR differently and DIRECT-ID analysis of the inverse-agonist vs. agonist-modulated β2 AR identified residues known through previous studies to selectively propagate deactivation/activation information, along with some previously unidentified ligand-specific microswitches across the GPCR. This study demonstrates the utility of DIRECT-ID to rapidly extract functionally relevant conformational dynamics information from extended MD simulations of large and complex macromolecular systems.

AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis

BACKGROUND

Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery.

RESULTS

AlloPred, a novel method to predict allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. In 28 of 40 cases an allosteric pocket was ranked first or second. The AlloPred web server, freely available at http://www.sbg.bio.ic.ac.uk/allopred/home, allows visualisation and analysis of predictions. The source code and dataset information are also available from this site.

CONCLUSIONS

Perturbation of normal modes can enhance our ability to predict allosteric sites on proteins. Computational methods such as AlloPred assist drug discovery efforts by suggesting sites on proteins for further experimental study.

Using kernelized partial canonical correlation analysis to study directly coupled side chains and allostery in small G proteins

Motivation: Inferring structural dependencies among a protein’s side chains helps us understand their coupled motions. It is known that coupled fluctuations can reveal pathways of communication used for information propagation in a molecule. Side-chain conformations are commonly represented by multivariate angular variables, but existing partial correlation methods that can be applied to this inference task are not capable of handling multivariate angular data. We propose a novel method to infer direct couplings from this type of data, and show that this method is useful for identifying functional regions and their interactions in allosteric proteins.

Results: We developed a novel extension of canonical correlation analysis (CCA), which we call ‘kernelized partial CCA’ (or simply KPCCA), and used it to infer direct couplings between side chains, while disentangling these couplings from indirect ones. Using the conformational information and fluctuations of the inactive structure alone for allosteric proteins in the Ras and other Ras-like families, our method identified allosterically important residues not only as strongly coupled ones but also in densely connected regions of the interaction graph formed by the inferred couplings. Our results were in good agreement with other empirical findings. By studying distinct members of the Ras, Rho and Rab sub-families, we show further that KPCCA was capable of inferring common allosteric characteristics in the small G protein super-family.

Availability and implementation:https://github.com/lsgh/ismb15

Contact:lsoltang@uwaterloo.ca

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.

An allosteric signaling pathway of human 3-phosphoglycerate kinase from force distribution analysis

3-Phosphogycerate kinase (PGK) is a two domain enzyme, which transfers a phosphate group between its two substrates, 1,3-bisphosphoglycerate bound to the N-domain and ADP bound to the C-domain. Indispensable for the phosphoryl transfer reaction is a large conformational change from an inactive open to an active closed conformation via a hinge motion that should bring substrates into close proximity. The allosteric pathway resulting in the active closed conformation has only been partially uncovered. Using Molecular Dynamics simulations combined with Force Distribution Analysis (FDA), we describe an allosteric pathway, which connects the substrate binding sites to the interdomain hinge region. Glu192 of alpha-helix 7 and Gly394 of loop L14 act as hinge points, at which these two secondary structure elements straighten, thereby moving the substrate-binding domains towards each other. The long-range allosteric pathway regulating hPGK catalytic activity, which is partially validated and can be further tested by mutagenesis, highlights the virtue of monitoring internal forces to reveal signal propagation, even if only minor conformational distortions, such as helix bending, initiate the large functional rearrangement of the macromolecule.

3-Phosphoglycerate kinase (PGK) is an essential enzyme for living organisms. It catalyzes the phospho-transfer reaction between two catabolites during carbohydrate metabolism. In addition to this physiological role, human PGK has been shown to phosphorylate L-nucleoside analogues, potential drugs against viral infection and cancer. PGK is a two domain enzyme, with the two substrates bound to the two separate domains. In order to perform its function the enzyme has to undergo a large conformational change involving a hinge bending to bring the substrates into close proximity. The allosteric pathway from the open non-reactive state of PGK to the closed reactive state as triggered by substrate binding has only been partially uncovered by experimental studies. Here we describe a complete allosteric pathway, which connects the substrate binding sites to the interdomain hinge region using Molecular Dynamics simulations combined with Force Distribution Analysis (FDA). While previously identified key residues involved in PGK domain closure are part of this pathway, we here fill the numerous gaps in the pathway by identifying newly uncovered residues and interesting candidates for future mutational studies.

The designability of protein switches by chemical rescue of structure: mechanisms of inactivation and reactivation

The ability to selectively activate function of particular proteins via pharmacological agents is a longstanding goal in chemical biology. Recently, we reported an approach for designing a de novo allosteric effector site directly into the catalytic domain of an enzyme. This approach is distinct from traditional chemical rescue of enzymes in that it relies on disruption and restoration of structure, rather than active site chemistry, as a means to achieve modulate function. However, rationally identifying analogous de novo binding sites in other enzymes represents a key challenge for extending this approach to introduce allosteric control into other enzymes. Here we show that mutation sites leading to protein inactivation via tryptophan-to-glycine substitution and allowing (partial) reactivation by the subsequent addition of indole are remarkably frequent. Through a suite of methods including a cell-based reporter assay, computational structure prediction and energetic analysis, fluorescence studies, enzymology, pulse proteolysis, X-ray crystallography, and hydrogen-deuterium mass spectrometry, we find that these switchable proteins are most commonly modulated indirectly, through control of protein stability. Addition of indole in these cases rescues activity not by reverting a discrete conformational change, as we had observed in the sole previously reported example, but rather rescues activity by restoring protein stability. This important finding will dramatically impact the design of future switches and sensors built by this approach, since evaluating stability differences associated with cavity-forming mutations is a far more tractable task than predicting allosteric conformational changes. By analogy to natural signaling systems, the insights from this study further raise the exciting prospect of modulating stability to design optimal recognition properties into future de novo switches and sensors built through chemical rescue of structure.

Automated identification of functional dynamic contact networks from X-ray crystallography

Protein function often depends on the exchange between conformational substates. Allosteric ligand binding or distal mutations can stabilize specific active site conformations and consequently alter protein function. In addition to comparing independently determined X-ray crystal structures, alternative conformations observed at low levels of electron density have the potential to provide mechanistic insights into conformational dynamics. Here, we report a new multi-conformer contact network algorithm (CONTACT) that identifies networks of conformationally heterogeneous residues directly from high-resolution X-ray crystallography data. Contact networks in Escherichia coli dihydrofolate reductase (ecDHFR) predict the long-range pattern of NMR chemical shift perturbations of an allosteric mutation. A comparison of contact networks in wild type and mutant ecDHFR suggests how mutations that alter optimized networks of coordinated motions can impair catalytic function. Thus, CONTACT-guided mutagenesis will allow the structure-dynamics-function relationship to be exploited in protein engineering and design.

SPACER: Server for predicting allosteric communication and effects of regulation

The SPACER server provides an interactive framework for exploring allosteric communication in proteins with different sizes, degrees of oligomerization and function. SPACER uses recently developed theoretical concepts based on the thermodynamic view of allostery. It proposes easily tractable and meaningful measures that allow users to analyze the effect of ligand binding on the intrinsic protein dynamics. The server shows potential allosteric sites and allows users to explore communication between the regulatory and functional sites. It is possible to explore, for instance, potential effector binding sites in a given structure as targets for allosteric drugs. As input, the server only requires a single structure. The server is freely available at http://allostery.bii.a-star.edu.sg/.

Ensemble-based characterization of unbound and bound states on protein energy landscape

Physicochemical description of numerous cell processes is fundamentally based on the energy landscapes of protein molecules involved. Although the whole energy landscape is difficult to reconstruct, increased attention to particular targets has provided enough structures for mapping functionally important subspaces associated with the unbound and bound protein structures. The subspace mapping produces a discrete representation of the landscape, further called energy spectrum. We compiled and characterized ensembles of bound and unbound conformations of six small proteins and explored their spectra in implicit solvent. First, the analysis of the unbound-to-bound changes points to conformational selection as the binding mechanism for four proteins. Second, results show that bound and unbound spectra often significantly overlap. Moreover, the larger the overlap the smaller the root mean square deviation (RMSD) between the bound and unbound conformational ensembles. Third, the center of the unbound spectrum has a higher energy than the center of the corresponding bound spectrum of the dimeric and multimeric states for most of the proteins. This suggests that the unbound states often have larger entropy than the bound states. Fourth, the exhaustively long minimization, making small intrarotamer adjustments (all-atom RMSD ≤ 0.7 Å), dramatically reduces the distance between the centers of the bound and unbound spectra as well as the spectra extent. It condenses unbound and bound energy levels into a thin layer at the bottom of the energy landscape with the energy spacing that varies between 0.8-4.6 and 3.5-10.5 kcal/mol for the unbound and bound states correspondingly. Finally, the analysis of protein energy fluctuations showed that protein vibrations itself can excite the interstate transitions, including the unbound-to-bound ones.

A fast approximate method of identifying paths of allosteric communication in proteins

Fluctuations of the distance between a pair of residues i and j may be correlated with the fluctuations of the distance between another pair k and l. In this case, information may be transmitted among these four residues. Allosteric activity is postulated to proceed through such correlated paths. In this short communication a fast method for calculating correlations among all possible pairs ij and kl leading to a pathway of correlated residues of a protein is proposed. The method is based on the alpha carbon centered Gaussian Network Model. The model is applied to Glutamine Amidotransferase and pathways of allosteric activity are identified and compared with literature.

Rational design of a ligand-controlled protein conformational switch

Design of a regulatable multistate protein is a challenge for protein engineering. Here we design a protein with a unique topology, called uniRapR, whose conformation is controlled by the binding of a small molecule. We confirm switching and control ability of uniRapR in silico, in vitro, and in vivo. As a proof of concept, uniRapR is used as an artificial regulatory domain to control activity of kinases. By activating Src kinase using uniRapR in single cells and whole organism, we observe two unique phenotypes consistent with its role in metastasis. Activation of Src kinase leads to rapid induction of protrusion with polarized spreading in HeLa cells, and morphological changes with loss of cell-cell contacts in the epidermal tissue of zebrafish. The rational creation of uniRapR exemplifies the strength of computational protein design, and offers a powerful means for targeted activation of many pathways to study signaling in living organisms.

Thermodynamics of allostery paves a way to allosteric drugs

We overview here our recent work on the thermodynamic view of allosteric regulation and communication. Starting from the geometry-based prediction of regulatory binding sites in a static structure, we move on to exploring a connection between ligand binding and the intrinsic dynamics of the protein molecule. We describe here two recently introduced measures, binding leverage and leverage coupling, which allow one to analyze the molecular basis of allosteric regulation. We discuss the advantages of these measures and show that they work universally in proteins of different sizes, oligomeric states, and functions. We also point the problems that have to be solved before completing an atomic level description of allostery, and briefly discuss ideas for computational design of allosteric drugs. This article is part of a Special Issue entitled: The emerging dynamic view of proteins: Protein plasticity in allostery, evolution and self-assembly.

Toward an understanding of the sequence and structural basis of allosteric proteins

Allostery is the most efficient means of regulating protein functions, ranging from the control of metabolic mechanisms to signal transduction pathways. Although allosteric regulation has been recognized for half a century, our knowledge is limited to the characteristics of allosteric proteins and the structural coupling of allosteric sites and modulators. In this paper, we present a comprehensive analysis of allosteric proteins that provides insight into the foundation of allosteric interactions by revealing a series of common features in the allosteric proteins. Allosteric proteins mainly appear in transferases, and phosphorylation is the most common type of modification found in allosteric proteins. Disorders related to allosteric proteins primarily comprise metabolic diseases and cancers. In general, allosteric proteins prefer to exist as monomers or even-numbered multimers. Greater stability and hydrophobicity are observed in allosteric proteins than in general proteins. Further analysis of the allosteric sites reveals a series of buried and compact pockets composed of significantly greater hydrophobic surface area than the corresponding orthosteric sites. The hydrophobicity of the allosteric sites plays a dominant role in the binding of allosteric modulators as observed in the analysis of 106 diverse allosteric protein-modulator pairs. These results may be of great significance in predicting which proteins are allosteric and in designing novel triggers to inhibit or activate proteins of interest.

Impact of mutations on the allosteric conformational equilibrium

Allostery in a protein involves effector binding at an allosteric site that changes the structure and/or dynamics at a distant, functional site. In addition to the chemical equilibrium of ligand binding, allostery involves a conformational equilibrium between one protein substate that binds the effector and a second substate that less strongly binds the effector. We run molecular dynamics simulations using simple, smooth energy landscapes to sample specific ligand-induced conformational transitions, as defined by the effector-bound and effector-unbound protein structures. These simulations can be performed using our web server (http://salilab.org/allosmod/). We then develop a set of features to analyze the simulations and capture the relevant thermodynamic properties of the allosteric conformational equilibrium. These features are based on molecular mechanics energy functions, stereochemical effects, and structural/dynamic coupling between sites. Using a machine-learning algorithm on a data set of 10 proteins and 179 mutations, we predict both the magnitude and the sign of the allosteric conformational equilibrium shift by the mutation; the impact of a large identifiable fraction of the mutations can be predicted with an average unsigned error of 1k(B)T. With similar accuracy, we predict the mutation effects for an 11th protein that was omitted from the initial training and testing of the machine-learning algorithm. We also assess which calculated thermodynamic properties contribute most to the accuracy of the prediction.

Force distribution reveals signal transduction in E. coli Hsp90

Heat-shock protein 90 (Hsp90) is an ubiquitous chaperone that is essential for cell function in that it promotes client-protein folding and stabilization. Its function is tightly controlled by an ATP-dependent large conformational transition between the open and closed states of the Hsp90 dimer. The underlying allosteric pathway has remained largely unknown, but it is revealed here in atomistic detail for the Escherichia coli homolog HtpG. Using force-distribution analysis based on molecular-dynamics simulations (>1 μs in total), we identify an internal signaling pathway that spans from the nucleotide-binding site to an ~2.3-nm-distant region in the HtpG middle domain, that serves as a dynamic hinge region, and to a putative client-protein-binding site in the middle domain. The force transmission is triggered by ATP capturing a magnesium ion and thereby rotating and bending a proximal long α-helix, which represents the major force channel into the middle domain. This allosteric mechanism is, with statistical significance, distinct from the dynamics in the ADP and apo states. Tracking the distribution of forces is likely to be a promising tool for understanding and guiding experiments of complex allosteric proteins in general.

Exploiting protein flexibility to predict the location of allosteric sites

BACKGROUND

Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure.

RESULTS

By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity), by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing that this simple coarse-grained methodology is able to capture the effects triggered by allosteric ligands already described in the literature.

CONCLUSIONS

We introduce a simple computational approach to predict the presence and position of allosteric sites in a protein based on the analysis of changes in protein normal modes upon the binding of a coarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newly curated non-redundant set of 91 proteins with reported allosteric properties. The software developed in this work is available upon request from the authors.

Designing allosteric control into enzymes by chemical rescue of structure

Ligand-dependent activity has been engineered into enzymes for purposes ranging from controlling cell morphology to reprogramming cellular signaling pathways. Where these successes have typically fused a naturally allosteric domain to the enzyme of interest, here we instead demonstrate an approach for designing a de novo allosteric effector site directly into the catalytic domain of an enzyme. This approach is distinct from traditional chemical rescue of enzymes in that it relies on disruption and restoration of structure, rather than active site chemistry, as a means to achieve modulate function. We present two examples, W33G in a β-glycosidase enzyme (β-gly) and W492G in a β-glucuronidase enzyme (β-gluc), in which we engineer indole-dependent activity into enzymes by removing a buried tryptophan side chain that serves as a buttress for the active site architecture. In both cases, we observe a loss of function, and in both cases we find that the subsequent addition of indole can be used to restore activity. Through a detailed analysis of β-gly W33G kinetics, we demonstrate that this rescued enzyme is fully functionally equivalent to the corresponding wild-type enzyme. We then present the apo and indole-bound crystal structures of β-gly W33G, which together establish the structural basis for enzyme inactivation and rescue. Finally, we use this designed switch to modulate β-glycosidase activity in living cells using indole. Disruption and recovery of protein structure may represent a general technique for introducing allosteric control into enzymes, and thus may serve as a starting point for building a variety of bioswitches and sensors.

Probing molecular mechanisms of the Hsp90 chaperone: biophysical modeling identifies key regulators of functional dynamics

Deciphering functional mechanisms of the Hsp90 chaperone machinery is an important objective in cancer biology aiming to facilitate discovery of targeted anti-cancer therapies. Despite significant advances in understanding structure and function of molecular chaperones, organizing molecular principles that control the relationship between conformational diversity and functional mechanisms of the Hsp90 activity lack a sufficient quantitative characterization. We combined molecular dynamics simulations, principal component analysis, the energy landscape model and structure-functional analysis of Hsp90 regulatory interactions to systematically investigate functional dynamics of the molecular chaperone. This approach has identified a network of conserved regions common to the Hsp90 chaperones that could play a universal role in coordinating functional dynamics, principal collective motions and allosteric signaling of Hsp90. We have found that these functional motifs may be utilized by the molecular chaperone machinery to act collectively as central regulators of Hsp90 dynamics and activity, including the inter-domain communications, control of ATP hydrolysis, and protein client binding. These findings have provided support to a long-standing assertion that allosteric regulation and catalysis may have emerged via common evolutionary routes. The interaction networks regulating functional motions of Hsp90 may be determined by the inherent structural architecture of the molecular chaperone. At the same time, the thermodynamics-based "conformational selection" of functional states is likely to be activated based on the nature of the binding partner. This mechanistic model of Hsp90 dynamics and function is consistent with the notion that allosteric networks orchestrating cooperative protein motions can be formed by evolutionary conserved and sparsely connected residue clusters. Hence, allosteric signaling through a small network of distantly connected residue clusters may be a rather general functional requirement encoded across molecular chaperones. The obtained insights may be useful in guiding discovery of allosteric Hsp90 inhibitors targeting protein interfaces with co-chaperones and protein binding clients.

Structure-based model of allostery predicts coupling between distant sites

Allostery is a phenomenon that couples effector ligand binding at an allosteric site to a structural and/or dynamic change at a distant regulated site. To study an allosteric transition, we vary the size of the allosteric site and its interactions to construct a series of energy landscapes with pronounced minima corresponding to both the effector bound and unbound crystal structures. We use molecular dynamics to sample these landscapes. The degree of perturbation by the effector, modeled by the size of the allosteric site, provides an order parameter for allostery that allows us to determine how microscopic motions give rise to commonly discussed macroscopic mechanisms: (i) induced fit, (ii) population shift, and (iii) entropy driven. These mechanisms involve decreasing structural differences between the effector bound and unbound populations. A metric (ligand-induced cooperativity) can measure how cooperatively a given regulated site responds to effector binding and therefore what kind of allosteric mechanism is involved. We apply the model to three proteins with experimentally characterized transitions: (i) calmodulin-GFP Ca(2+) sensor protein, (ii) maltose binding protein, and (iii) CSL transcription factor. Remarkably, the model is able to reproduce allosteric motion and predict coupling in a manner consistent with experiment.

The two-pathway model of the biological catch-bond as a limit of the allosteric model

Catch-binding is a counterintuitive phenomenon in which the lifetime of a receptor/ligand bond increases when a force is applied to break the bond. Several mechanisms have been proposed to rationalize catch-binding. In the two-pathway model, the force drives the system away from its native dissociation pathway into an alternative pathway involving a higher energy barrier. Here, we analyze an allosteric model suggesting that a force applied to the complex alters the distribution of receptor conformations, and as a result, induces changes in the ligand-binding site. The model assumes explicitly that the allosteric transitions govern the properties of the ligand site. We demonstrate that the dynamics of the ligand is described by two relaxation times, one of which arises from the allosteric site. Therefore, we argue that one can characterize the allosteric transitions by studying the receptor/ligand binding. We show that the allosteric description reduces to the two-pathway model in the limit when the allosteric transitions are faster than the bond dissociation. The formal results are illustrated with two systems, P-selectin/PSGL-1 and FimH/mannose, subjected to both constant and time-dependent forces. The report advances our understanding of catch-binding by combining alternative physical models into a unified description and makes the problem more tractable for the bond mechanics community.

Detection of allosteric signal transmission by information-theoretic analysis of protein dynamics

Allostery offers a highly specific way to modulate protein function. Therefore, understanding this mechanism is of increasing interest for protein science and drug discovery. However, allosteric signal transmission is difficult to detect experimentally and to model because it is often mediated by local structural changes propagating along multiple pathways. To address this, we developed a method to identify communication pathways by an information-theoretical analysis of molecular dynamics simulations. Signal propagation was described as information exchange through a network of correlated local motions, modeled as transitions between canonical states of protein fragments. The method was used to describe allostery in two-component regulatory systems. In particular, the transmission from the allosteric site to the signaling surface of the receiver domain NtrC was shown to be mediated by a layer of hub residues. The location of hubs preferentially connected to the allosteric site was found in close agreement with key residues experimentally identified as involved in the signal transmission. The comparison with the networks of the homologues CheY and FixJ highlighted similarities in their dynamics. In particular, we showed that a preorganized network of fragment connections between the allosteric and functional sites exists already in the inactive state of all three proteins.

Structural position correlation analysis (SPCA) for protein family

Background

The proteins in a family, which perform the similar biological functions, may have very different amino acid composition, but they must share the similar 3D structures, and keep a stable central region. In the conservative structure region similar biological functions are performed by two or three catalytic residues with the collaboration of several functional residues at key positions. Communication signals are conducted in a position network, adjusting the biological functions in the protein family.

Methodology

A computational approach, namely structural position correlation analysis (SPCA), is developed to analyze the correlation relationship between structural segments (or positions). The basic hypothesis of SPCA is that in a protein family the structural conservation is more important than the sequence conservation, and the local structural changes may contain information of biology functional evolution. A standard protein P⁽⁰⁾ is defined in a protein family, which consists of the most-frequent amino acids and takes the average structure of the protein family. The foundational variables of SPCA is the structural position displacements between the standard protein P⁽⁰⁾ and individual proteins P_i of the family. The structural positions are organized as segments, which are the stable units in structural displacements of the protein family. The biological function differences of protein members are determined by the position structural displacements of individual protein P_i to the standard protein P⁽⁰⁾. Correlation analysis is used to analyze the communication network among segments.

Conclusions

The structural position correlation analysis (SPCA) is able to find the correlation relationship among the structural segments (or positions) in a protein family, which cannot be detected by the amino acid sequence and frequency-based methods. The functional communication network among the structural segments (or positions) in protein family, revealed by SPCA approach, well illustrate the distantly allosteric interactions, and contains valuable information for protein engineering study.

Allostery in protein domains reflects a balance of steric and hydrophobic effects

Allosteric conformational change underlies biological function in many proteins. Allostery refers to a conformational event in which one region of a protein undergoes structural rearrangement in response to a stimulus applied to a different region of the same protein. Here, I show for a variety of proteins that a simple, phenomenological model of the dependence of protein conformation on hydrophobic burial energy allows one to compute low-energy conformational fluctuations for a given sequence by using linear programming to find optimized combinations of sequence-specific hydrophobic burial modes that satisfy steric constraints. From these fluctuations one may calculate allosteric couplings between different sites in a protein domain. Although the physical basis of protein structure is complex and multifactorial, a simplified description of conformational energy in terms of the hydrophobic effect alone is sufficient to give a mechanistic explanation for many biologically important allosteric events.

Computational modeling of allosteric communication reveals organizing principles of mutation-induced signaling in ABL and EGFR kinases

The emerging structural information about allosteric kinase complexes and the growing number of allosteric inhibitors call for a systematic strategy to delineate and classify mechanisms of allosteric regulation and long-range communication that control kinase activity. In this work, we have investigated mechanistic aspects of long-range communications in ABL and EGFR kinases based on the results of multiscale simulations of regulatory complexes and computational modeling of signal propagation in proteins. These approaches have been systematically employed to elucidate organizing molecular principles of allosteric signaling in the ABL and EGFR multi-domain regulatory complexes and analyze allosteric signatures of the gate-keeper cancer mutations. We have presented evidence that mechanisms of allosteric activation may have universally evolved in the ABL and EGFR regulatory complexes as a product of a functional cross-talk between the organizing αF-helix and conformationally adaptive αI-helix and αC-helix. These structural elements form a dynamic network of efficiently communicated clusters that may control the long-range interdomain coupling and allosteric activation. The results of this study have unveiled a unifying effect of the gate-keeper cancer mutations as catalysts of kinase activation, leading to the enhanced long-range communication among allosterically coupled segments and stabilization of the active kinase form. The results of this study can reconcile recent experimental studies of allosteric inhibition and long-range cooperativity between binding sites in protein kinases. The presented study offers a novel molecular insight into mechanistic aspects of allosteric kinase signaling and provides a quantitative picture of activation mechanisms in protein kinases at the atomic level.

Despite recent progress in computational and experimental studies of dynamic regulation in protein kinases, a mechanistic understanding of long-range communication and mechanisms of mutation-induced signaling controlling kinase activity remains largely qualitative. In this study, we have performed a systematic modeling and analysis of allosteric activation in ABL and EGFR kinases at the increasing level of complexity - from catalytic domain to multi-domain regulatory complexes. The results of this study have revealed organizing structural and mechanistic principles of allosteric signaling in protein kinases. Although activation mechanisms in ABL and EGFR kinases have evolved through acquisition of structurally different regulatory complexes, we have found that long-range interdomain communication between common functional segments (αF-helix and αC-helix) may be important for allosteric activation. The results of study have revealed molecular signatures of activating cancer mutations and have shed the light on general mechanistic aspects of mutation-induced signaling in protein kinases. An advanced understanding and further characterization of molecular signatures of kinase mutations may aid in a better rationalization of mutational effects on clinical outcomes and facilitate molecular-based therapeutic strategies to combat kinase mutation-dependent tumorigenesis.

The energy landscape analysis of cancer mutations in protein kinases

The growing interest in quantifying the molecular basis of protein kinase activation and allosteric regulation by cancer mutations has fueled computational studies of allosteric signaling in protein kinases. In the present study, we combined computer simulations and the energy landscape analysis of protein kinases to characterize the interplay between oncogenic mutations and locally frustrated sites as important catalysts of allostetric kinase activation. While structurally rigid kinase core constitutes a minimally frustrated hub of the catalytic domain, locally frustrated residue clusters, whose interaction networks are not energetically optimized, are prone to dynamic modulation and could enable allosteric conformational transitions. The results of this study have shown that the energy landscape effect of oncogenic mutations may be allosteric eliciting global changes in the spatial distribution of highly frustrated residues. We have found that mutation-induced allosteric signaling may involve a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. The presented study has demonstrated that activation cancer mutations may affect the thermodynamic equilibrium between kinase states by allosterically altering the distribution of locally frustrated sites and increasing the local frustration in the inactive form, while eliminating locally frustrated sites and restoring structural rigidity of the active form. The energy landsape analysis of protein kinases and the proposed role of locally frustrated sites in activation mechanisms may have useful implications for bioinformatics-based screening and detection of functional sites critical for allosteric regulation in complex biomolecular systems.

Molecular evolution of protein conformational changes revealed by a network of evolutionarily coupled residues

An improved understanding of protein conformational changes has broad implications for elucidating the mechanisms of various biological processes and for the design of protein engineering experiments. Understanding rearrangements of residue interactions is a key component in the challenge of describing structural transitions. Evolutionary properties of protein sequences and structures are extensively studied; however, evolution of protein motions, especially with respect to interaction rearrangements, has yet to be explored. Here, we investigated the relationship between sequence evolution and protein conformational changes and discovered that structural transitions are encoded in amino acid sequences as coevolving residue pairs. Furthermore, we found that highly coevolving residues are clustered in the flexible regions of proteins and facilitate structural transitions by forming and disrupting their interactions cooperatively. Our results provide insight into the evolution of protein conformational changes and help to identify residues important for structural transitions.

Side-chain conformational changes upon Protein-Protein Association

Conformational changes upon protein-protein association are the key element of the binding mechanism. The study presents a systematic large-scale analysis of such conformational changes in the side chains. The results indicate that short and long side chains have different propensities for the conformational changes. Long side chains with three or more dihedral angles are often subject to large conformational transition. Shorter residues with one or two dihedral angles typically undergo local conformational changes not leading to a conformational transition. A relationship between the local readjustments and the equilibrium fluctuations of a side chain around its unbound conformation is suggested. Most of the side chains undergo larger changes in the dihedral angle most distant from the backbone. The frequencies of the core-to-surface interface transitions of six nonpolar residues and Tyr are larger than the frequencies of the opposite surface-to-core transitions. The binding increases both polar and nonpolar interface areas. However, the increase of the nonpolar area is larger for all considered classes of protein complexes, suggesting that the protein association perturbs the unbound interfaces to increase the hydrophobic contribution to the binding free energy. To test modeling approaches to side-chain flexibility in protein docking, conformational changes in the X-ray set were compared with those in the docking decoy sets. The results lead to a better understanding of the conformational changes in proteins and suggest directions for efficient conformational sampling in docking protocols.

Regulation of catch binding by allosteric transitions

An allosteric model is used to describe changes in lifetimes of biological receptor-ligand bonds subjected to an external force. Force-induced transitions between the two states of the allosteric site lead to changes in the receptor conformation. The ligand bound to the receptor fluctuates between two different potentials formed by the two receptor conformations. The effect of the force on the receptor-ligand interaction potential is described by the Bell mechanism. The probability of detecting the ligand in the bound state is found to depend on the relaxation times of both ligand and allosteric sites. An analytic expression for the bond lifetime is derived as a function of force. The formal theoretical results are used to explain the anomalous force and time dependences of the integrin-fibronectin bond lifetimes measured by atomic force microscopy (Kong, F.; et al J. Cell Biol. 2009, 185, 1275-1284). The analytic expression and model parameters describe very well all anomalous dependences identified in the experiments.

Assessing the structural conservation of protein pockets to study functional and allosteric sites: implications for drug discovery

Background

With the classical, active-site oriented drug-development approach reaching its limits, protein ligand-binding sites in general and allosteric sites in particular are increasingly attracting the interest of medicinal chemists in the search for new types of targets and strategies to drug development. Given that allostery represents one of the most common and powerful means to regulate protein function, the traditional drug discovery approach of targeting active sites can be extended by targeting allosteric or regulatory protein pockets that may allow the discovery of not only novel drug-like inhibitors, but activators as well. The wealth of available protein structural data can be exploited to further increase our understanding of allosterism, which in turn may have therapeutic applications. A first step in this direction is to identify and characterize putative effector sites that may be present in already available structural data.

Results

We performed a large-scale study of protein cavities as potential allosteric and functional sites, by integrating publicly available information on protein sequences, structures and active sites for more than a thousand protein families. By identifying common pockets across different structures of the same protein family we developed a method to measure the pocket's structural conservation. The method was first parameterized using known active sites. We characterized the predicted pockets in terms of sequence and structural conservation, backbone flexibility and electrostatic potential. Although these different measures do not tend to correlate, their combination is useful in selecting functional and regulatory sites, as a detailed analysis of a handful of protein families shows. We finally estimated the numbers of potential allosteric or regulatory pockets that may be present in the data set, finding that pockets with putative functional and effector characteristics are widespread across protein families.

Conclusions

Our results show that structurally conserved pockets are a common feature of protein families. The structural conservation of protein pockets, combined with other characteristics, can be exploited in drug discovery procedures, in particular for the selection of the most appropriate target protein and pocket for the design of drugs against entire protein families or subfamilies (e.g. for the development of broad-spectrum antimicrobials) or against a specific protein (e.g. in attempting to reduce side effects).