Exploring allosteric coupling in the alpha-subunit of heterotrimeric G proteins using evolutionary and ensemble-based approaches

Applications of sequence coevolution in membrane protein biochemistry

Recently, protein sequence coevolution analysis has matured into a predictive powerhouse for protein structure and function. Direct methods, which use global statistical models of sequence coevolution, have enabled the prediction of membrane and disordered protein structures, protein complex architectures, and the functional effects of mutations in proteins. The field of membrane protein biochemistry and structural biology has embraced these computational techniques, which provide functional and structural information in an otherwise experimentally-challenging field. Here we review recent applications of protein sequence coevolution analysis to membrane protein structure and function and highlight the promising directions and future obstacles in these fields. We provide insights and guidelines for membrane protein biochemists who wish to apply sequence coevolution analysis to a given experimental system.

The nucleotide-free state of heterotrimeric G proteins α-subunit adopts a highly stable conformation

Deciphering the mechanism of activation of heterotrimeric G proteins by their cognate receptors continues to be an intriguing area of research. The recently solved crystal structure of the ternary complex captured the receptor-bound α-subunit in an open conformation, without bound nucleotide has improved our understanding of the activation process. Despite these advancements, the mechanism by which the receptor causes GDP release from the α-subunit remains elusive. To elucidate the mechanism of activation, we studied guanine nucleotide-induced structural stability of the α-subunit (in response to thermal/chaotrope-mediated stress). Inherent stabilities of the inactive (GDP-bound) and active (GTP-bound) forms contribute antagonistically to the difference in conformational stability whereas the GDP-bound protein is able to switch to a stable intermediate state, GTP-bound protein loses this ability. Partial perturbation of the protein fold reveals the underlying influence of the bound nucleotide providing an insight into the mechanism of activation. An extra stable, pretransition intermediate, 'empty pocket' state (conformationally active-state like) in the unfolding pathway of GDP-bound protein mimics a gating system - the activation process having to overcome this stable intermediate state. We demonstrate that a relatively more complex conformational fold of the GDP-bound protein is at the core of the gating system. We report capturing this threshold, 'metastable empty pocket' conformation (the gate) of α-subunit of G protein and hypothesize that the receptor activates the G protein by enabling it to achieve this structure through mild structural perturbation.

A physiologically required G protein-coupled receptor (GPCR)-regulator of G protein signaling (RGS) interaction that compartmentalizes RGS activity

G protein-coupled receptors (GPCRs) can interact with regulator of G protein signaling (RGS) proteins. However, the effects of such interactions on signal transduction and their physiological relevance have been largely undetermined. Ligand-bound GPCRs initiate by promoting exchange of GDP for GTP on the Gα subunit of heterotrimeric G proteins. Signaling is terminated by hydrolysis of GTP to GDP through intrinsic GTPase activity of the Gα subunit, a reaction catalyzed by RGS proteins. Using yeast as a tool to study GPCR signaling in isolation, we define an interaction between the cognate GPCR (Mam2) and RGS (Rgs1), mapping the interaction domains. This reaction tethers Rgs1 at the plasma membrane and is essential for physiological signaling response. In vivo quantitative data inform the development of a kinetic model of the GTPase cycle, which extends previous attempts by including GPCR-RGS interactions. In vivo and in silico data confirm that GPCR-RGS interactions can impose an additional layer of regulation through mediating RGS subcellular localization to compartmentalize RGS activity within a cell, thus highlighting their importance as potential targets to modulate GPCR signaling pathways.

Structure-based network analysis of an evolved G protein-coupled receptor homodimer interface

Crystallographic structures and experimental assays of human CXC chemokine receptor type 4 (CXCR4) provide strong evidence for the capacity to homodimerize, potentially as a means of allosteric regulation. Even so, how this homodimer forms and its biological significance has yet to be fully characterized. By applying principles from network analysis, sequence-based approaches such as statistical coupling analysis to determine coevolutionary residues, can be used in conjunction with molecular dynamics simulations to identify residues relevant to dimerization. Here, the predominant coevolution sector lies along the observed dimer interface, suggesting functional relevance. Furthermore, coevolution scoring provides a basis for determining significant nodes, termed hubs, in the network formed by residues found along the interface of the homodimer. These node residues coincide with hotspots indicating potential druggability. Drug design efforts targeting such key residues could potentially result in modulation of binding and therapeutic benefits for disease states, such as lung cancers, lymphomas and latent HIV-1 infection. Furthermore, this method may be applied to any protein-protein interaction.

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.

The role of protein conformational fluctuations in allostery, function, and evolution

It is now well-known that proteins exist at equilibrium as ensembles of conformational states rather than as unique static structures. Here we review from an ensemble perspective important biological effects of such spontaneous fluctuations on protein allostery, function, and evolution. However, rather than present a thorough literature review on each subject, we focus instead on connecting these phenomena through the ensemble-based experimental, theoretical, and computational investigations from our laboratory over the past decade. Special emphasis is given to insights that run counter to some of the prevailing ideas that have emerged over the past 40 years of structural biology research. For instance, when proteins are viewed as conformational ensembles rather than as single structures, the commonly held notion of an allosteric pathway as an obligate series of individual structural distortions loses its meaning. Instead, allostery can result from energetic linkage between distal sites as one Boltzmann distribution of states transitions to another. Additionally, the emerging principles from this ensemble view of proteins have proven surprisingly useful in describing the role of intrinsic disorder in inter-domain communication, functional adaptation mediated by mutational control of fluctuations, and evolutionary conservation of the energetics of protein stability.

Nucleotide binding switches the information flow in ras GTPases

The Ras superfamily comprises many guanine nucleotide-binding proteins (G proteins) that are essential to intracellular signal transduction. The guanine nucleotide-dependent intrinsic flexibility patterns of five G proteins were investigated in atomic detail through Molecular Dynamics simulations of the GDP- and GTP-bound states (S^GDP and S^GTP, respectively). For all the considered systems, the intrinsic flexibility of S^GDP was higher than that of S^GTP, suggesting that Guanine Exchange Factor (GEF) recognition and nucleotide switch require higher amplitude motions than effector recognition or GTP hydrolysis. Functional mode, dynamic domain, and interaction energy correlation analyses highlighted significant differences in the dynamics of small G proteins and Gα proteins, especially in the inactive state. Indeed, S^GDP of Gα_t, is characterized by a more extensive energy coupling between nucleotide binding site and distal regions involved in GEF recognition compared to small G proteins, which attenuates in the active state. Moreover, mechanically distinct domains implicated in nucleotide switch could be detected in the presence of GDP but not in the presence of GTP. Finally, in small G proteins, functional modes are more detectable in the inactive state than in the active one and involve changes in solvent exposure of two highly conserved amino acids in switches I and II involved in GEF recognition. The average solvent exposure of these amino acids correlates in turn with the rate of GDP release, suggesting for them either direct or indirect roles in the process of nucleotide switch. Collectively, nucleotide binding changes the information flow through the conserved Ras-like domain, where GDP enhances the flexibility of mechanically distinct portions involved in nucleotide switch, and favors long distance allosteric communication (in Gα proteins), compared to GTP.

The Ras superfamily comprises many guanine nucleotide-binding proteins (G proteins) that are essential to intracellular signal transduction. These proteins act biologically as molecular switches cycling between ON and OFF states, thereby controlling a variety of processes ranging from cell growth and differentiation to vesicular and nuclear transport. In spite of the extremely high biological and medical relevance of the Ras GTPase superfamily, a comprehensive structural/dynamic view of the trans-family and family-specific functioning mechanisms is still lacking. In this study, we gained insights into the functional dynamics of Ras GTPases by deciphering the dynamic information encrypted in the topology of these proteins depending on the nucleotide-bound state, i.e. GDP- or GTP-bound (S^GDP and S^GTP, respectively). Collectively, nucleotide binding changes the information flow through the conserved Ras-like domain, where GDP enhances the flexibility of mechanically distinct portions involved in nucleotide switch, and favors long distance allosteric communication (in Gα proteins), compared to GTP. Functional dynamics is instrumental in GDP switch, which for the members of the Gα family, different from small G proteins, requires allosteric communication between nucleotide and Guanine Exchange Factor binding sites.

Identification of functional motions in the adenylate kinase (ADK) protein family by computational hybrid approaches

Based on integrative computational hybrid approaches that combined statistical coupling analysis (SCA), molecular dynamics (MD), and normal mode analysis (NMA), evolutionarily coupled residues involved in functionally relevant motion in the adenylate kinase protein family were identified. The hybrids identified four top-ranking site pairs that belong to a conserved hydrogen bond network that is involved in the enzyme's flexibility. A second group of top-ranking site pairs was identified in critical regions for functional dynamics, such as those related to enzymatic turnover. The high consistency of the results obtained by SCA with NMA (SCA.NMA) and by SCA.MD hybrid analyses suggests that suitable replacement of the matrix of cross-correlation analysis of atomic fluctuations (derived by using NMA) with those based on MD contributes to the identification of such sites by means of a fast computational calculation. The analysis presented here strongly supports the hypothesis that evolutionary forces, such as coevolution at the sequence level, have promoted functional dynamic properties of the adenylate kinase protein family. Finally, these hybrid approaches can be used to identify, at the residue level, protein motion coordination patterns not previously observed, such as in hinge regions.

An energetic representation of protein architecture that is independent of primary and secondary structure

Protein fold classification often assumes that similarity in primary, secondary, or tertiary structure signifies a common evolutionary origin. However, when similarity is not obvious, it is sometimes difficult to conclude that particular proteins are completely unrelated. Clearly, a set of organizing principles that is independent of traditional classification could be valuable in linking different structural motifs and identifying common ancestry from seemingly disparate folds. Here, a four-dimensional ensemble-based energetic space spanned by a diverse set of proteins was defined and its characteristics were contrasted with those of Cartesian coordinate space. Eigenvector decomposition of this energetic space revealed the dominant physical processes contributing to the more or less stable regions of a protein. Unexpectedly, those processes were identical for proteins with different secondary structure content and were also identical among different amino-acid types. The implications of these results are twofold. First, it indicates that excited conformational states comprising the protein native state ensemble, largely invisible upon inspection of the high-resolution structure, are the major determinant of the energetic space. Second, it suggests that folds dissimilar in sequence or structure could nonetheless be energetically similar if their respective excited conformational states are considered, one example of which was observed in the N-terminal region of the Arc repressor switch mutant. Taken together, these results provide a surface area-based framework for understanding folds in energetic terms, a framework that may eventually yield a means of identifying common ancestry among structurally dissimilar proteins.

Quantifying Correlations Between Allosteric Sites in Thermodynamic Ensembles

Allostery describes altered protein function at one site due to a perturbation at another site. One mechanism of allostery involves correlated motions, which can occur even in the absence of substantial conformational change. We present a novel method, "MutInf", to identify statistically significant correlated motions from equilibrium molecular dynamics simulations. Our approach analyzes both backbone and sidechain motions using internal coordinates to account for the gear-like twists that can take place even in the absence of the large conformational changes typical of traditional allosteric proteins. We quantify correlated motions using a mutual information metric, which we extend to incorporate data from multiple short simulations and to filter out correlations that are not statistically significant. Applying our approach to uncover mechanisms of cooperative small molecule binding in human interleukin-2, we identify clusters of correlated residues from 50 ns of molecular dynamics simulations. Interestingly, two of the clusters with the strongest correlations highlight known cooperative small-molecule binding sites and show substantial correlations between these sites. These cooperative binding sites on interleukin-2 are correlated not only through the hydrophobic core of the protein but also through a dynamic polar network of hydrogen bonding and electrostatic interactions. Since this approach identifies correlated conformations in an unbiased, statistically robust manner, it should be a useful tool for finding novel or "orphan" allosteric sites in proteins of biological and therapeutic importance.

Correlated mutation analysis on the catalytic domains of serine/threonine protein kinases

Background

Protein kinases (PKs) have emerged as the largest family of signaling proteins in eukaryotic cells and are involved in every aspect of cellular regulation. Great progresses have been made in understanding the mechanisms of PKs phosphorylating their substrates, but the detailed mechanisms, by which PKs ensure their substrate specificity with their structurally conserved catalytic domains, still have not been adequately understood. Correlated mutation analysis based on large sets of diverse sequence data may provide new insights into this question.

Methodology/Principal Findings

Statistical coupling, residue correlation and mutual information analyses along with clustering were applied to analyze the structure-based multiple sequence alignment of the catalytic domains of the Ser/Thr PK family. Two clusters of highly coupled sites were identified. Mapping these positions onto the 3D structure of PK catalytic domain showed that these two groups of positions form two physically close networks. We named these two networks as θ-shaped and γ-shaped networks, respectively.

Conclusions/Significance

The θ-shaped network links the active site cleft and the substrate binding regions, and might participate in PKs recognizing and interacting with their substrates. The γ-shaped network is mainly situated in one side of substrate binding regions, linking the activation loop and the substrate binding regions. It might play a role in supporting the activation loop and substrate binding regions before catalysis, and participate in product releasing after phosphoryl transfer. Our results exhibit significant correlations with experimental observations, and can be used as a guide to further experimental and theoretical studies on the mechanisms of PKs interacting with their substrates.