Recent advances in targeting the "undruggable" proteins: from drug discovery to clinical trials

Undruggable proteins are a class of proteins that are often characterized by large, complex structures or functions that are difficult to interfere with using conventional drug design strategies. Targeting such undruggable targets has been considered also a great opportunity for treatment of human diseases and has attracted substantial efforts in the field of medicine. Therefore, in this review, we focus on the recent development of drug discovery targeting "undruggable" proteins and their application in clinic. To make this review well organized, we discuss the design strategies targeting the undruggable proteins, including covalent regulation, allosteric inhibition, protein-protein/DNA interaction inhibition, targeted proteins regulation, nucleic acid-based approach, immunotherapy and others.

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

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

psnGPCRdb: The Structure-network Database of G Protein Coupled Receptors

G protein coupled receptors (GPCRs) are critical eukaryotic signal transduction gatekeepers and represent the largest protein superfamily in the human proteome, with more than 800 members. They share seven transmembrane helices organized in an up-down bundle architecture. GPCR-mediated signaling pathways have been linked to numerous human diseases, and GPCRs are the targets of approximately 35% of all drugs currently on the market. Structure network analysis, a graph theory-based approach, represents a cutting-edge tool to deeply understand GPCR function, which strongly relies on communication between the extracellular and intracellular poles of their structure. psnGPCRdb stores the structure networks (i.e., linked nodes, hubs, communities and communication pathways) computed on all updated GPCR structures in the Protein Data Bank, in their isolated states or in complex with extracellular and/or intracellular molecules. The structure communication signatures of a sub-family or family of GPCRs as well as of their small-molecule activators or inhibitors are stored as consensus networks. The database stores also all meaningful structure network-based comparisons (i.e., difference networks) of functionally different states (i.e., inactive or active) of a given receptor sub-type, or of consensus networks representative of a receptor sub-type, type, sub-family or family. Single or consensus GPCR networks hold also information on amino acid conservation. The database allows to graphically analyze 3D structure networks together with interactive data-tables. Ligand-centric networks can be analyzed as well. psnGPCRdb is unique and represents a powerful resource to unravel GPCR function with important implications in cell signaling and drug design. psnGPCRdb is freely available at: http://webpsn.hpc.unimo.it/psngpcr.php.

Prediction of Allosteric Communication Pathways in Proteins

MOTIVATION

Allostery in proteins is an essential phenomenon in biological processes. In this paper, we present a computational model to predict paths of maximum information transfer between active and allosteric sites. In this information theoretic study, we use mutual information as the measure of information transfer, where transition probability of information from one residue to its contacting neighbors is proportional to the magnitude of mutual information between the two residues. Starting from a given residue and using a Hidden Markov Model, we successively determine the neighboring residues that eventually lead to a path of optimum information transfer. The Gaussian approximation of mutual information between residue pairs is adopted. The limits of validity of this approximation are discussed in terms of a nonlinear theory of mutual information and its reduction to the Gaussian form.

RESULTS

Predictions of the model are tested on six widely studied cases, CheY Bacterial Chemotaxis, B-cell Lymphoma extra-large Bcl-xL, Human proline isomerase cyclophilin A (CypA), Dihydrofolate reductase DHFR, HRas GTPase, and Caspase-1. The communication transmission rendering the propagation of local fluctuations from the active sites throughout the structure in multiple paths correlate well with the known experimental data. Distinct paths originating from the active site may likely represent a multi functionality such as involving more than one allosteric site and/or preexistence of some other functional states. Our model is computationally fast and simple, and can give allosteric communication pathways, which are crucial for the understanding and control of protein functionality.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PSNtools for standalone and web-based structure network analyses of conformational ensembles

Structure graphs, in which interacting amino acids/nucleotides correspond to linked nodes, represent cutting-edge tools to investigate macromolecular function.

The graph-based approach defined as Protein Structure Network (PSN) was initially implemented in the Wordom software and subsequently in the webPSN server. PSNs are computed either on a molecular dynamics (MD) trajectory (PSN-MD) or on a single structure. In the latter case, information on atomic fluctuations is inferred from the Elastic Network Model-Normal Mode Analysis (ENM-NMA) (PSN-ENM). While Wordom performs both PSN-ENM and PSN-MD analyses but without output post-processing, the webPSN server performs only single-structure PSN-EMN but assisting the user in input setup and output analysis.

Here we release for the first time the standalone software PSNtools, which allows calculation and post-processing of PSN analyses carried out either on single structures or on conformational ensembles. Relevant unique and novel features of PSNtools are either comparisons of two networks or computations of consensus networks on sets of homologous/analogous macromolecular structures or conformational ensembles. Network comparisons and consensus serve to infer differences in functionally different states of the same system or network-based signatures in groups of bio-macromolecules sharing either the same functionality or the same fold.

In addition to the new software, here we release also an updated version of the webPSN server, which allows performing an interactive graphical analysis of PSN-MD, following the upload of the PSNtools output.

PSNtools, the auxiliary binary version of Wordom software, and the WebPSN server are freely available at http://webpsn.hpc.unimo.it/wpsn3.php.

Identification of Allosteric Effects in Proteins by Elastic Network Models

Allostery is a fundamental regulatory mechanism in the majority of biological processes of molecular machines. Allostery is well-known as a dynamic-driven process, and thus, the molecular mechanism of allosteric signal transmission needs to be established. Elastic network models (ENMs) provide efficient methods for investigating the intrinsic dynamics and allosteric communication pathways in proteins. In this chapter, two ENM methods including Gaussian network model (GNM) coupled with Markovian stochastic model, as well as the anisotropic network model (ANM), were introduced to identify allosteric effects in hemoglobins. Techniques on model parameters, scripting and calculation, analysis, and visualization are shown step by step.

Local and Global Motions Underlying Antibiotic Binding in Bacterial Ribosome

The bacterial ribosome is one of the most important targets in the treatment of infectious diseases. As antibiotic resistance in bacteria poses a growing threat, a significant amount of effort is concentrated on exploring new drug-binding sites where testable predictions are of significance. Here, we study the dynamics of a ribosomal complex and 67 small and large subunits of the ribosomal crystal structures (64 antibiotic-bound, 3 antibiotic-free) from Deinococcus radiodurans, Escherichia coli, Haloarcula marismortui, and Thermus thermophilus by the Gaussian network model. Interestingly, a network of nucleotides coupled in high-frequency fluctuations reveals known antibiotic-binding sites. These sites are seen to locate at the interface of dynamic domains that have an intrinsic dynamic capacity to interfere with functional globular motions. The nucleotides and the residues fluctuating in the fast and slow modes of motion thus have promise for plausible antibiotic-binding and allosteric sites that can alter antibiotic binding and resistance. Overall, the present analysis brings a new dynamic perspective to the long-discussed link between small-molecule binding and large conformational changes of the supramolecule.

webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules

A mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy (i.e. PSN-ENM) was developed to investigate structural communication in bio-macromolecules. Protein Structure Graphs (PSGs) are computed on a single structure, whereas information on system dynamics is supplied by ENM-NMA. The approach was implemented in a webserver (webPSN), which was significantly updated herein. The webserver now handles both proteins and nucleic acids and relies on an internal upgradable database of network parameters for ions and small molecules in all PDB structures. Apart from the radical restyle of the server and some changes in the calculation setup, other major novelties concern the possibility to: a) compute the differences in nodes, links, and communication pathways between two structures (i.e. network difference) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a number of structures. These new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems. The output analysis relies on 3D-representations, interactive tables and graphs, also available for download. Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems. URL: http://webpsn.hpc.unimore.it.

Identification of potential allosteric binding sites in cathepsin K based on intramolecular communication

Network theory methods and molecular dynamics (MD) simulations are accepted tools to study allosteric regulation. Indeed, dynamic networks built upon correlation analysis of MD trajectories provide detailed information about communication paths between distant sites. In this context, we aimed to understand whether the efficiency of intramolecular communication could be used to predict the allosteric potential of a given site. To this end, we performed MD simulations and network theory analyses in cathepsin K (catK), whose allosteric sites are well defined. To obtain a quantitative measure of the efficiency of communication, we designed a new protocol that enables the comparison between properties related to ensembles of communication paths obtained from different sites. Further, we applied our strategy to evaluate the allosteric potential of different catK cavities not yet considered for drug design. Our predictions of the allosteric potential based on intramolecular communication correlate well with previous catK experimental and theoretical data. We also discuss the possibility of applying our approach to other proteins from the same family.

An investigation into the allosteric mechanism of GPCR A2A adenosine receptor with trajectory-based information theory and complex network model

G protein-coupled receptors (GPCRs), a large superfamily of transmembrane (TM) proteins, allosterically transduce the signal of ligand binding in the extracellular (EC) domain to couple to effector proteins in the intracellular (IC) domain, therefore forming the largest class of drug targets. The A_2A adenosine receptor (A_2AAR), a class-A GPCR, has been extensively studied as it offers numerous possibilities for therapeutic applications. However, the mechanism of allosteric communication between EC and IC domains is not completely clear. In this work, we utilize torsional mutual information to quantify the correlated motions of residue pairs from its molecular dynamics (MD) simulation trajectories, and further use the complex network model to obtain allosteric pipelines and hubs. The identified allosteric communication pipelines mainly transmit the signal from EC domain to the cytoplasmic ends of TM helix 5 (TM5), TM6 and TM7. The allosteric hubs, mostly located at TM5, TM6 and TM7, play an important role in mediating allosteric signal transmission to keep the receptor rigid and prevent G protein from binding to IC domain, which can explain the reason why their mutations distant from ligand-binding site do not affect the ligand binding affinity but affect the ligand efficacy. Additionally, we identify the key residues located in antagonist ZM241385 binding pocket which mediate multiple allosteric pathways and have been experimentally proven to play a critical role in affecting the ligand potency. This study is helpful for understanding the allosteric communication mechanism of A_2AAR, and can provide valuable information for the structure-based drug design of GPCRs.Communicated by Ramaswamy H. Sarma.

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

Variance of Atomic Coordinates as a Dynamical Metric to Distinguish Proteins and Protein-Protein Interactions in Molecular Dynamics Simulations

Protein dynamics is a manifestation of the complex trajectories of these biomolecules on a multidimensional rugged potential energy surface (PES) driven by thermal energy. At present, computational methods such as atomistic molecular dynamics (MD) simulations can describe thermal protein conformational changes in fully solvated environments over millisecond timescales. Despite these advances, a quantitative assessment of protein dynamics remains a complicated topic, intricately linked to issues such as sampling convergence and the identification of appropriate reaction coordinates/structural features to describe protein conformational states and motions. Here, we present the cumulative variance of atomic coordinate fluctuations (CVCF) along trajectories as an intuitive PES sensitive metric to assess both the extent of sampling and protein dynamics captured in MD simulations. We first examine the sampling problem in model one- (1D) and two-dimensional (2D) PES to demonstrate that the CVCF when traced as a function of the sampling variable (time in MD simulations) can identify local and global equilibria. Further, even far from global equilibrium, a situation representative of standard MD trajectories of proteins, the CVCF can distinguish different PES and therefore resolve the resultant protein dynamics. We demonstrate the utility of our CVCF analysis by applying it to distinguish the dynamics of structurally homologous proteins from the ubiquitin family (ubiquitin, SUMO1, SUMO2) and ubiquitin protein-protein interactions. Our CVCF analysis reveals that differential side-chain dynamics from the structured part of the protein (the conserved β-grasp fold) present distinct protein PES to distinguish ubiquitin from SUMO isoforms. Upon binding to two functionally distinct protein partners (UBCH5A and UEV), intrinsic ubiquitin dynamics changes to reflect the binding context even though the two proteins have similar binding modes, which lead to negligible (sub-angstrom scale) structural changes.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Ligand binding effects on the activation of the EGFR extracellular domain

The epidermal growth factor receptor (EGFR) is one of the most common target proteins in anti-cancer therapy. The binding of the EGF ligand to the EGFR extracellular domain (EGFR-ECD) promotes its inactive-to-active conformational transition (activation) but the relevant detailed mechanism remains elusive still. Here, the structural characterization and energetics of the EGFR-ECD conformational transition with and without the binding of the EGF are quantitatively explored using an innovative enhanced sampling MD simulation method. Intriguingly, the EGF offers hydrophobic interactions (e.g., EGF residues of Tyr44 and Leu47) and electrostatic interactions (e.g., the EGF residues of Glu5, Asp11, Asp17, and Arg41) to play a dominant role in dragging domain III to close the ligand binding domain gap. Subsequently, the correlation between domains III and II is enhanced through salt-bridges among Glu376, Arg403, and Arg405 from domain III and Glu293, Glu295, and Arg300 from domain II. Finally, the structural bending of domain II is regulated to facilitate the disengagement of domain II from domain IV. In this regard, the functional conformational transition of EGFR-ECD is a consequence of the cooperative motion of protein domains driven by the EGF ligand binding. The present study shows a detailed scenario of the EGF induced activation of EGFR-ECD and provides valuable information for drug discovery targeting the EGFR.

Coarse-Grained Simulation of Full-Length Integrin Activation

Integrin conformational dynamics are critical to their receptor and signaling functions in many cellular processes, including spreading, adhesion, and migration. However, assessing integrin conformations is both experimentally and computationally challenging because of limitations in resolution and dynamic sampling. Thus, structural changes that underlie transitions between conformations are largely unknown. Here, focusing on integrin αvβ₃, we developed a modified form of the coarse-grained heterogeneous elastic network model (hENM), which allows sampling conformations at the onset of activation by formally separating local fluctuations from global motions. Both local fluctuations and global motions are extracted from all-atom molecular dynamics simulations of the full-length αvβ₃ bent integrin conformer, but whereas the former are incorporated in the hENM as effective harmonic interactions between groups of residues, the latter emerge by systematically identifying and treating weak interactions between long-distance domains with flexible and anharmonic connections. The new hENM model allows integrins and single-point mutant integrins to explore various conformational states, including the initiation of separation between α- and β-subunit cytoplasmic regions, headpiece extension, and legs opening.

Ancient thioredoxins evolved to modern-day stability-function requirement by altering native state ensemble

Thioredoxins (THRXs)-small globular proteins that reduce other proteins-are ubiquitous in all forms of life, from Archaea to mammals. Although ancestral thioredoxins share sequential and structural similarity with the modern-day (extant) homologues, they exhibit significantly different functional activity and stability. We investigate this puzzle by comparative studies of their (ancient and modern-day THRXs') native state ensemble, as quantified by the dynamic flexibility index (DFI), a metric for the relative resilience of an amino acid to perturbations in the rest of the protein. Clustering proteins using DFI profiles strongly resemble an alternative classification scheme based on their activity and stability. The DFI profiles of the extant proteins are substantially different around the α3, α4 helices and catalytic regions. Likewise, allosteric coupling of the active site with the rest of the protein is different between ancient and extant THRXs, possibly explaining the decreased catalytic activity at low pH with evolution. At a global level, we note that the population of low-flexibility (called hinges) and high-flexibility sites increases with evolution. The heterogeneity (quantified by the variance) in DFI distribution increases with the decrease in the melting temperature typically associated with the evolution of ancient proteins to their modern-day counterparts.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

Uncovering GPCR and G Protein Function by Protein Structure Network Analysis

Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used for investigating structural communication in biomolecular systems. Information on the system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM-NMA). This chapter reports on selected applications of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs) and G proteins. Strategies to highlight changes in structural communication caused by mutations, ligand and protein binding are described. Conserved amino acids, sites of misfolding mutations, or ligands acting as functional switches tend to behave as hubs in the native structure networks. Densely linked regions in the protein structure graphs could be identified as playing central roles in protein stability and function. Changes in the communication pathway fingerprints depending on the bound ligand or following amino acid mutation could be highlighted as well. A bridge between misfolding and misrouting could be established in rhodopsin mutants linked to inherited blindness. The analysis of native network perturbations by misfolding mutations served to infer key structural elements of protein responsiveness to small chaperones with implications for drug discovery.

Identification of potential allosteric communication pathways between functional sites of the bacterial ribosome by graph and elastic network models

BACKGROUND

Accumulated evidence indicates that bacterial ribosome employs allostery throughout its structure for protein synthesis. The nature of the allosteric communication between remote functional sites remains unclear, but the contact topology and dynamics of residues may play role in transmission of a perturbation to distant sites.

METHODS/RESULTS

We employ two computationally efficient approaches - graph and elastic network modeling to gain insights about the allosteric communication in ribosome. Using graph representation of the structure, we perform k-shortest pathways analysis between peptidyl transferase center-ribosomal tunnel, decoding center-peptidyl transferase center - previously reported functional sites having allosteric communication. Detailed analysis on intact structures points to common and alternative shortest pathways preferred by different states of translation. All shortest pathways capture drug target sites and allosterically important regions. Elastic network model further reveals that residues along all pathways have the ability of quickly establishing pair-wise communication and to help the propagation of a perturbation in long-ranges during functional motions of the complex.

CONCLUSIONS

Contact topology and inherent dynamics of ribosome configure potential communication pathways between functional sites in different translation states. Inter-subunit bridges B2a, B3 and P-tRNA come forward for their high potential in assisting allostery during translation. Especially B3 emerges as a potential druggable site.

GENERAL SIGNIFICANCE

This study indicates that the ribosome topology forms a basis for allosteric communication, which can be disrupted by novel drugs to kill drug-resistant bacteria. Our computationally efficient approach not only overlaps with experimental evidence on allosteric regulation in ribosome but also proposes new druggable sites.

Design of Elastic Networks with Evolutionary Optimized Long-Range Communication as Mechanical Models of Allosteric Proteins

Allosteric effects often underlie the activity of proteins, and elucidating generic design aspects and functional principles unique to allosteric phenomena represent a major challenge. Here an approach consisting of the in silico design of synthetic structures, which, as the principal element of allostery, encode dynamical long-range coupling among two sites, is presented. The structures are represented by elastic networks, similar to coarse-grained models of real proteins. A strategy of evolutionary optimization was implemented to iteratively improve allosteric coupling. In the designed structures, allosteric interactions were analyzed in terms of strain propagation, and simple pathways that emerged during evolution were identified as signatures through which long-range communication was established. Moreover, robustness of allosteric performance with respect to mutations was demonstrated. As it turned out, the designed prototype structures reveal dynamical properties resembling those found in real allosteric proteins. Hence, they may serve as toy models of complex allosteric systems, such as proteins. Application of the developed modeling scheme to the allosteric transition in the myosin V molecular motor was also demonstrated.

Directional Force Originating from ATP Hydrolysis Drives the GroEL Conformational Change

Protein functional mechanisms usually require conformational changes, and often there are known structures for the different conformational states. However, usually neither the origin of the driving force nor the underlying pathways for these conformational transitions is known. Exothermic chemical reactions may be an important source of forces that drive conformational changes. Here we investigate this type of force originating from ATP hydrolysis in the chaperonin GroEL, by applying forces originating from the chemical reaction. Specifically, we apply directed forces to drive the GroEL conformational changes and learn that there is a highly specific direction for applied forces to drive the closed form to the open form. For this purpose, we utilize coarse-grained elastic network models. Principal component analysis on 38 GroEL experimental structures yields the most important motions, and these are used in structural interpolation for the construction of a coarse-grained free energy landscape. In addition, we investigate a more random application of forces with a Monte Carlo method and demonstrate pathways for the closed-open conformational transition in both directions by computing trajectories that are shown upon the free energy landscape. Initial root mean square deviation (RMSD) between the open and closed forms of the subunit is 14.7 Å and final forms from our simulations reach an average RMSD of 3.6 Å from the target forms, closely matching the level of resolution of the coarse-grained model.

Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

An efficient perturbation method to predict the functionally key sites of glutamine binding protein

Glutamine-Binding Protein (GlnBP) of Escherichia coli, an important member of the periplasmic binding protein family, is responsible for the first step in the active transport of glutamine across the cytoplasmic membrane. In this work, the functionally key regulation sites of GlnBP were identified by utilizing a perturbation method proposed by our group, in which the residues whose perturbations markedly change the binding free energy between GlnBP and glutamine are considered to be functionally key residues. The results show that besides the substrate binding sites, some other residues distant from the binding pocket, including the ones in the hinge regions between the two domains, the front- and back- door channels and the exposed region, are important for the function of glutamine binding and transport. The predicted results are well consistent with the theoretical and experimental data, which indicates that our method is an effective approach to identify the key residues important for both ligand binding and long-range allosteric signal transmission. This work can provide some insights into the function performance of GlnBP and the physical mechanism of its allosteric regulation.

Structure network analysis to gain insights into GPCR function

G protein coupled receptors (GPCRs) are allosteric proteins whose functioning fundamentals are the communication between the two poles of the helix bundle. Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM–NMA). The present review article describes the application of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding, dimerization and activation are described. Focus is put on the ENM–NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signalling active (meta II (MII)) states, highlighting changes in structure network and centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.

Controlling Allosteric Networks in Proteins

Allosteric transition, defined as conformational changes induced by ligand binding, is one of the fundamental properties of proteins. Allostery has been observed and characterized in many proteins, and has been recently utilized to control protein function via regulation of protein activity. Here, we review the physical and evolutionary origin of protein allostery, as well as its importance to protein regulation, drug discovery, and biological processes in living systems. We describe recently developed approaches to identify allosteric pathways, connected sets of pairwise interactions that are responsible for propagation of conformational change from the ligand-binding site to a distal functional site. We then present experimental and computational protein engineering approaches for control of protein function by modulation of allosteric sites. As an example of application of these approaches, we describe a synergistic computational and experimental approach to rescue the cystic-fibrosis-associated protein cystic fibrosis transmembrane conductance regulator, which upon deletion of a single residue misfolds and causes disease. This example demonstrates the power of allosteric manipulation in proteins to both elucidate mechanisms of molecular function and to develop therapeutic strategies that rescue those functions. Allosteric control of proteins provides a tool to shine a light on the complex cascades of cellular processes and facilitate unprecedented interrogation of biological systems.

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.

The Role of Conformational Dynamics and Allostery in the Disease Development of Human Ferritin

Determining the three-dimensional structure of myoglobin, the first solved structure of a protein, fundamentally changed the way protein function was understood. Even more revolutionary was the information that came afterward: protein dynamics play a critical role in biological functions. Therefore, understanding conformational dynamics is crucial to obtaining a more complete picture of protein evolution. We recently analyzed the evolution of different protein families including green fluorescent proteins (GFPs), β-lactamase inhibitors, and nuclear receptors, and we observed that the alteration of conformational dynamics through allosteric regulation leads to functional changes. Moreover, proteome-wide conformational dynamics analysis of more than 100 human proteins showed that mutations occurring at rigid residue positions are more susceptible to disease than flexible residue positions. These studies suggest that disease-associated mutations may impair dynamic allosteric regulations, leading to loss of function. Thus, in this study, we analyzed the conformational dynamics of the wild-type light chain subunit of human ferritin protein along with the neutral and disease forms. We first performed replica exchange molecular dynamics simulations of wild-type and mutants to obtain equilibrated dynamics and then used perturbation response scanning (PRS), where we introduced a random Brownian kick to a position and computed the fluctuation response of the chain using linear response theory. Using this approach, we computed the dynamic flexibility index (DFI) for each position in the chain for the wild-type and the mutants. DFI quantifies the resilience of a position to a perturbation and provides a flexibility/rigidity measurement for a given position in the chain. The DFI analysis reveals that neutral variants and the wild-type exhibit similar flexibility profiles in which experimentally determined functionally critical sites act as hinges in controlling the overall motion. However, disease mutations alter the conformational dynamic profile, making hinges more loose (i.e., softening the hinges), thus impairing the allosterically regulated dynamics.

MDN: A Web Portal for Network Analysis of Molecular Dynamics Simulations

We introduce a web portal that employs network theory for the analysis of trajectories from molecular dynamics simulations. Users can create protein energy networks following methodology previously introduced by our group, and can identify residues that are important for signal propagation, as well as measure the efficiency of signal propagation by calculating the network coupling. This tool, called MDN, was used to characterize signal propagation in Escherichia coli heat-shock protein 70-kDa. Two variants of this protein experimentally shown to be allosterically active exhibit higher network coupling relative to that of two inactive variants. In addition, calculations of partial coupling suggest that this quantity could be used as part of the criteria to determine pocket druggability in drug discovery studies.

Computational modeling of allosteric regulation in the hsp90 chaperones: a statistical ensemble analysis of protein structure networks and allosteric communications

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks.

Functional versatility and structural adaptability of the Hsp90 chaperones are regulated by allosteric interactions that allow for diverse functions including modulation of ATP hydrolysis and binding with cochaperones and client proteins. By integrating molecular simulations and network-based approaches we have characterized conformational dynamics and allosteric interactions in different functional forms of Hsp90. The network centrality analysis and structural mapping of allosteric communications have revealed a small-world organization of the interaction network that is mediated by functionally important residues of the Hsp90 activity. We have found that effective allosteric communications in the Hsp90 chaperone may be provided by structurally stable residues that exhibit high centrality properties. Nucleotide-specific rewiring of the network topology and assortative organization of functional residues may protect the active form of the chaperone from random perturbations and detrimental mutations. These results have confirmed that allosteric interactions in the Hsp90 chaperone may be determined by a small-world network of functional residues that can regulate the network efficiency and resiliency by modulating the statistical ensemble of communication pathways in response to functional requirements of the ATPase cycle.

Allosteric pathway identification through network analysis: from molecular dynamics simulations to interactive 2D and 3D graphs

Allostery is a universal phenomenon that couples the information induced by a local perturbation (effector) in a protein to spatially distant regulated sites. Such an event can be described in terms of a large scale transmission of information (communication) through a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. To elaborate a rational description of allosteric coupling, we propose an original approach - MOdular NETwork Analysis (MONETA) - based on the analysis of inter-residue dynamical correlations to localize the propagation of both structural and dynamical effects of a perturbation throughout a protein structure. MONETA uses inter-residue cross-correlations and commute times computed from molecular dynamics simulations and a topological description of a protein to build a modular network representation composed of clusters of residues (dynamic segments) linked together by chains of residues (communication pathways). MONETA provides a brand new direct and simple visualization of protein allosteric communication. A GEPHI module implemented in the MONETA package allows the generation of 2D graphs of the communication network. An interactive PyMOL plugin permits drawing of the communication pathways between chosen protein fragments or residues on a 3D representation. MONETA is a powerful tool for on-the-fly display of communication networks in proteins. We applied MONETA for the analysis of communication pathways (i) between the main regulatory fragments of receptors tyrosine kinases (RTKs), KIT and CSF-1R, in the native and mutated states and (ii) in proteins STAT5 (STAT5a and STAT5b) in the phosphorylated and the unphosphorylated forms. The description of the physical support for allosteric coupling by MONETA allowed a comparison of the mechanisms of (a) constitutive activation induced by equivalent mutations in two RTKs and (b) allosteric regulation in the activated and non-activated STAT5 proteins. Our theoretical prediction based on results obtained with MONETA was validated for KIT by in vitro experiments. MONETA is a versatile analytical and visualization tool entirely devoted to the understanding of the functioning/malfunctioning of allosteric regulation in proteins - a crucial basis to guide the discovery of next-generation allosteric drugs.

Differential effects of CSF-1R D802V and KIT D816V homologous mutations on receptor tertiary structure and allosteric communication

The colony stimulating factor-1 receptor (CSF-1R) and the stem cell factor receptor KIT, type III receptor tyrosine kinases (RTKs), are important mediators of signal transduction. The normal functions of these receptors can be compromised by gain-of-function mutations associated with different physiopatological impacts. Whereas KIT D816V/H mutation is a well-characterized oncogenic event and principal cause of systemic mastocytosis, the homologous CSF-1R D802V has not been identified in human cancers. The KIT D816V oncogenic mutation triggers resistance to the RTK inhibitor Imatinib used as first line treatment against chronic myeloid leukemia and gastrointestinal tumors. CSF-1R is also sensitive to Imatinib and this sensitivity is altered by mutation D802V. Previous in silico characterization of the D816V mutation in KIT evidenced that the mutation caused a structure reorganization of the juxtamembrane region (JMR) and facilitated its departure from the kinase domain (KD). In this study, we showed that the equivalent CSF-1R D802V mutation does not promote such structural effects on the JMR despite of a reduction on some key H-bonds interactions controlling the JMR binding to the KD. In addition, this mutation disrupts the allosteric communication between two essential regulatory fragments of the receptors, the JMR and the A-loop. Nevertheless, the mutation-induced shift towards an active conformation observed in KIT D816V is not observed in CSF-1R D802V. The distinct impact of equivalent mutation in two homologous RTKs could be associated with the sequence difference between both receptors in the native form, particularly in the JMR region. A local mutation-induced perturbation on the A-loop structure observed in both receptors indicates the stabilization of an inactive non-inhibited form, which Imatinib cannot bind.

Exploiting conformational dynamics in drug discovery: design of C-terminal inhibitors of Hsp90 with improved activities

The interaction that occurs between molecules is a dynamic process that impacts both structural and conformational properties of the ligand and the ligand binding site. Herein, we investigate the dynamic cross-talk between a protein and the ligand as a source for new opportunities in ligand design. Analysis of the formation/disappearance of protein pockets produced in response to a first-generation inhibitor assisted in the identification of functional groups that could be introduced onto scaffolds to facilitate optimal binding, which allowed for increased binding with previously uncharacterized regions. MD simulations were used to elucidate primary changes that occur in the Hsp90 C-terminal binding pocket in the presence of first-generation ligands. This data was then used to design ligands that adapt to these receptor conformations, which provides access to an energy landscape that is not visible in a static model. The newly synthesized compounds demonstrated antiproliferative activity at ∼150 nM concentration. The method identified herein may be used to design chemical probes that provide additional information on structural variations of Hsp90 C-terminal binding site.

Allosteric conformational barcodes direct signaling in the cell

The cellular network is highly interconnected. Pathways merge and diverge. They proceed through shared proteins and may change directions. How are cellular pathways controlled and their directions decided, coded, and read? These questions become particularly acute when we consider that a small number of pathways, such as signaling pathways that regulate cell fates, cell proliferation, and cell death in development, are extensively exploited. This review focuses on these signaling questions from the structural standpoint and discusses the literature in this light. All co-occurring allosteric events (including posttranslational modifications, pathogen binding, and gain-of-function mutations) collectively tag the protein functional site with a unique barcode. The barcode shape is read by an interacting molecule, which transmits the signal. A conformational barcode provides an intracellular address label, which selectively favors binding to one partner and quenches binding to others, and, in this way, determines the pathway direction, and, eventually, the cell's response and fate.

Mapping the intramolecular signal propagation pathways in protein using Bayesian change point analysis of atomic motions

We propose to use change points of atomic positions in the molecular dynamics trajectory as indicators of the propagating signals in protein. We designate these changes as signals because they can propagate within the molecule in the form of "perturbation wave", transmit energy or information between different parts of protein, and serve as allosteric signals. We found that change points can distinguish between thermal fluctuations of atoms (noise) and signals in a protein despite the differences in the motility of amino acid residues. Clustering of the spatially close residues that were experiencing change points close in time, allowed us to map pathways of signal propagation in a protein at the atomic level of resolution. We propose a potential mechanism for the origin of the signal and its propagation that relies on the autonomic coherence resonance in atomic fluctuations. According to this mechanism, random synchronization of fluctuations of neighboring atoms results in a resonance, which increases amplitude of vibration of these atoms. This increase can be transmitted to the atoms colliding with the resonant atoms, leading to the propagating signal. The wavelet-based coherence analysis of the inter-atomic distances between carbon-alpha atoms and surrounding atoms for the residue pairs that belong to the same communication pathway allowed us to find time periods with temporarily locked phases, confirming the occurrence of conditions for resonance. Analysis of the mapped pathways demonstrated that they form a network that connects different regions of the protein.

The spatial structure of cell signaling systems

The spatial structure of the cell is highly organized at all levels: from small complexes and assemblies, to local nano- and microclusters, to global, micrometer scales across and between cells. We suggest that this multiscale spatial cell organization also organizes signaling and coordinates cellular behavior. We propose a new view of the spatial structure of cell signaling systems. This new view describes cell signaling in terms of dynamic allosteric interactions within and among distinct, spatially organized transient clusters. The clusters vary over time and space and are on length scales from nanometers to micrometers. When considered across these length scales, primary factors in the spatial organization are cell membrane domains and the actin cytoskeleton, both also highly dynamic. A key challenge is to understand the interplay across these multiple scales, link it to the physicochemical basis of the conformational behavior of single molecules and ultimately relate it to cellular function. Overall, our premise is that at these scales, cell signaling should be thought of not primarily as a sequence of diffusion-controlled molecular collisions, but instead transient, allostery-driven cluster re-forming interactions.

Multiple pathways promote dynamical coupling between catalytic domains in Escherichia coli prolyl-tRNA synthetase

Aminoacyl-tRNA synthetases are multidomain enzymes that catalyze covalent attachment of amino acids to their cognate tRNA. Cross-talk between functional domains is a prerequisite for this process. In this study, we investigate the molecular mechanism of site-to-site communication in Escherichia coli prolyl-tRNA synthetase (Ec ProRS). Earlier studies have demonstrated that evolutionarily conserved and/or co-evolved residues that are engaged in correlated motion are critical for the propagation of functional conformational changes from one site to another in modular proteins. Here, molecular simulation and bioinformatics-based analysis were performed to identify dynamically coupled and evolutionarily constrained residues that form contiguous pathways of residue-residue interactions between the aminoacylation and editing domains of Ec ProRS. The results of this study suggest that multiple pathways exist between these two domains to maintain the dynamic coupling essential for enzyme function. Moreover, residues in these interaction networks are generally highly conserved. Site-directed changes of on-pathway residues have a significant impact on enzyme function and dynamics, suggesting that any perturbation along these pathways disrupts the native residue-residue interactions that are required for effective communication between the two functional domains. Free energy analysis revealed that communication between residues within a pathway and cross-talk between pathways are important for coordinating functions of different domains of Ec ProRS for efficient catalysis.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

A Mixed Protein Structure Network and Elastic Network Model Approach to Predict the Structural Communication in Biomolecular Systems: The PDZ2 Domain from Tyrosine Phosphatase 1E As a Case Study

Graph theory is being increasingly used to study the structural communication in biomolecular systems. This requires incorporating information on the system's dynamics, which is time-consuming and not suitable for high-throughput investigations. We propose a mixed Protein Structure Network (PSN) and Elastic Network Model (ENM)-based strategy, i.e., PSN-ENM, for fast investigation of allosterism in biological systems. PSN analysis and ENM-Normal Mode Analysis (ENM-NMA) are implemented in the structural analysis software Wordom, freely available at http://wordom.sourceforge.net/ . The method performs a systematic search of the shortest communication pathways that traverse a protein structure. A number of strategies to compare the structure networks of a protein in different functional states and to get a global picture of communication pathways are presented as well. The approach was validated on the PDZ2 domain from tyrosine phosphatase 1E (PTP1E) in its free (APO) and peptide-bound states. PDZ domains are, indeed, the systems whose structural communication and allosteric features are best characterized both in vitro and in silico. The agreement between predictions by the PSN-ENM method and in vitro evidence is remarkable and comparable to or higher than that reached by more time-consuming computational approaches tested on the same biological system. Finally, the PSN-ENM method was able to reproduce the salient communication features of unbound and bound PTP1E inferred from molecular dynamics simulations. High speed makes this method suitable for high throughput investigation of the communication pathways in large sets of biomolecular systems in different functional states.

The underappreciated role of allostery in the cellular network

Allosteric propagation results in communication between distinct sites in the protein structure; it also encodes specific effects on cellular pathways, and in this way it shapes cellular response. One example of long-range effects is binding of morphogens to cell surface receptors, which initiates a cascade of protein interactions that leads to genome activation and specific cellular action. Allosteric propagation results from combinations of multiple factors, takes place through dynamic shifts of conformational ensembles, and affects the equilibria of macromolecular interactions. Here, we (a) emphasize the well-known yet still underappreciated role of allostery in conveying explicit signals across large multimolecular assemblies and distances to specify cellular action; (b) stress the need for quantitation of the allosteric effects; and finally, (c) propose that each specific combination of allosteric effectors along the pathway spells a distinct function. The challenges are colossal; the inspiring reward will be predicting function, misfunction, and outcomes of drug regimes.

Weak intra-ring allosteric communications of the archaeal chaperonin thermosome revealed by normal mode analysis

Chaperonins are molecular machines that use ATP-driven cycles to assist misfolded substrate proteins to reach the native state. During the functional cycle, these machines adopt distinct nucleotide-dependent conformational states, which reflect large-scale allosteric changes in individual subunits. Distinct allosteric kinetics has been described for the two chaperonin classes. Bacterial (group I) chaperonins, such as GroEL, undergo concerted subunit motions within each ring, whereas archaeal and eukaryotic chaperonins (group II) undergo sequential subunit motions. We study these distinct mechanisms through a comparative normal mode analysis of monomer and double-ring structures of the archaeal chaperonin thermosome and GroEL. We find that thermosome monomers of each type exhibit common low-frequency behavior of normal modes. The observed distinct higher-frequency modes are attributed to functional specialization of these subunit types. The thermosome double-ring structure has larger contribution from higher-frequency modes, as it is found in the GroEL case. We find that long-range intersubunit correlation of amino-acid pairs is weaker in the thermosome ring than in GroEL. Overall, our results indicate that distinct allosteric behavior of the two chaperonin classes originates from different wiring of individual subunits as well as of the intersubunit communications.

Network analysis to uncover the structural communication in GPCRs

Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system dynamics can be provided by atomistic molecular dynamics simulations or coarse-grained Elastic Network Models paired with Normal Mode Analysis (ENM-NMA). This chapter describes the application of PSN analysis to uncover the structural communication in G protein-coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding mutations, dimerization, and activation are described. Focus is put on the ENM-NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signaling active (meta II (MII)) states, highlighting clear changes in the PSN and the centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.

Normal-mode-analysis-guided investigation of crucial intersubunit contacts in the cAMP-dependent gating in HCN channels

Protein structures define a complex network of atomic interactions in three dimensions. Direct visualization of the structure and analysis of the interaction potential energy are not straightforward approaches to pinpoint the atomic contacts that are crucial for protein function. We used the tetrameric hyperpolarization-activated cAMP-regulated (HCN) channel as a model system to study the intersubunit contacts in cAMP-dependent gating. To obtain a systematic survey of the contacts between each pair of residues, we used normal-mode analysis, a computational approach for studying protein dynamics, and constructed the covariance matrix for C-α atoms. The significant contacts revealed by covariance analysis were further investigated by means of mutagenesis and functional assays. Among the mutant channels that show phenotypes different from those of the wild-type, we focused on two mutant channels that express opposite changes in cAMP-dependent gating. Subsequent biochemical assays on isolated C-terminal fragments, including the cAMP binding domain, revealed only minimal effects on cAMP binding, suggesting the necessity of interpreting the cAMP-dependent allosteric regulation at the whole-channel level. For this purpose, we applied the patch-clamp fluorometry technique and observed correlated changes in the dynamic, state-dependent cAMP binding in the mutant channels. This study not only provides further understanding of the intersubunit contacts in allosteric coupling in the HCN channel, it also illustrates an effective strategy for delineating important atomic contacts within a structure.

Allosteric communication across the native and mutated KIT receptor tyrosine kinase

A fundamental goal in cellular signaling is to understand allosteric communication, the process by which signals originated at one site in a protein propagate dependably to affect remote functional sites. Here, we describe the allosteric regulation of the receptor tyrosine kinase KIT. Our analysis evidenced that communication routes established between the activation loop (A-loop) and the distant juxtamembrane region (JMR) in the native protein were disrupted by the oncogenic mutation D816V positioned in the A-loop. In silico mutagenesis provided a plausible way of restoring the protein communication detected in the native KIT by introducing a counter-balancing second mutation D792E. The communication patterns observed in the native and mutated KIT correlate perfectly with the structural and dynamical features of these proteins. Particularly, a long-distance effect of the D816V mutation manifested as an important structural re-organization of the JMR in the oncogenic mutant was completely vanished in the double mutant D816V/D792E. This detailed characterization of the allosteric communication in the different forms of KIT, native and mutants, was performed by using a modular network representation composed of communication pathways and independent dynamic segments. Such representation permits to enrich a purely mechanistic interaction-based model of protein communication by the introduction of concerted local atomic fluctuations. This method, validated on KIT receptor, may guide a rational modulation of the physiopathological activities of other receptor tyrosine kinases.

The majority of functionally important biological processes are regulated by allosteric communication within individual proteins and across protein complexes. Receptor tyrosine kinases (RTKs) control signal transduction pathways and consequently represent a typical paradigm. The mutation-induced deregulation of RTK activity impairs crucial cellular physiological functions and causes serious human diseases. The present study focuses on the allosteric communication across the three-dimensional structure of the RTK KIT cytoplasmic region. Combining a mechanistic model of information transmission with the analysis of concerted local atomic fluctuations we examined and compared the communication profiles in the native and D816V-mutated proteins. This approach permitted to localize and visualize communication routes in the native KIT and revealed that these routes were disrupted in the mutant D816V. We proposed in silico mutagenesis as a mean to restore the communication detected in the native KIT. Our work sheds light on the allosteric communication in RTKs, a phenomenon playing an essential role in signaling pathways albeit experiments do not provide the atomic details of the path followed in going from one structural element to the other. A rational understanding of the molecular determinants underlying the effects of disease-related kinase mutations may contribute to the improvement of targeted therapies.

Light on the structural communication in Ras GTPases

The graph theory was combined with fluctuation dynamics to investigate the structural communication in four small G proteins, Arf1, H-Ras, RhoA, and Sec4. The topology of small GTPases is such that it requires the presence of the nucleotide to acquire a persistent structural network. The majority of communication paths involves the nucleotide and does not exist in the unbound forms. The latter are almost devoid of high-frequency paths. Thus, small Ras GTPases acquire the ability to transfer signals in the presence of nucleotide, suggesting that it modifies the intrinsic dynamics of the protein through the establishment of regions of hyperlinked nodes with high occurrence of correlated motions. The analysis of communication paths in the inactive (S(GDP)) and active (S(GTP)) states of the four G proteins strengthened the separation of the Ras-like domain into two dynamically distinct lobes, i.e. lobes 1 and 2, representing, respectively, the N-terminal and C-terminal halves of the domain. In the framework of this separation, interfunctional states and interfamily differences could be inferred. The structure network undergoes a reshaping depending on the bound nucleotide. Nucleotide-dependent divergences in structural communication reach the maximum in Arf1 and the minimum in RhoA. In Arf1, the nucleotide-dependent paths essentially express a communication between the G box 4 (G4) and distal portions of lobe 1. In the S(GDP) state, the G4 communicates with the N-term, while, in the S(GTP) state, the G4 communicates with the switch II. Clear differences could be also found between Arf1 and the other three G proteins. In Arf1, the nucleotide tends to communicate with distal portions of lobe 1, whereas in H-Ras, RhoA, and Sec4 it tends to communicate with a cluster of aromatic/hydrophobic amino acids in lobe 2. These differences may be linked, at least in part, to the divergent membrane anchoring modes that would involve the N-term for the Arf family and the C-term for the Rab/Ras/Rho families.