Allosteric Communication In Proteins Research Articles

Information flow and allosteric communication in proteins.

Based on Schreiber's work on transfer entropy, a molecular theory of nonlinear information transfer between residue pairs in proteins is developed. The joint distribution function for residue fluctuations required by the theory is expressed in terms of tensor Hermite polynomials that conveniently separate harmonic and nonlinear contributions to information transfer. The harmonic part of information transfer is expressed as the difference between time dependent and independent mutual information. Third order nonlinearities are discussed in detail. The amount and speed of information transfer between residues, which are important for understanding allosteric activity in proteins, are discussed. Mutual information between two residues is commonly used for information transfer. While mutual information shows the maximum amount of information that may be transferred between two residues, it does not explain the actual amount of transfer nor the transfer rate of information. For this, dynamic equations of the system are needed. The solution of the Langevin equation and molecular dynamics trajectories are used in the present work for this purpose. Allosteric communication in human NAD-dependent isocitrate dehydrogenase is studied as an example. Calculations show that several paths contribute collectively to information transfer. Important residues on these paths are identified. Time resolved information transfer between these residues, their amplitudes, and transfer rates, which are in agreement with time resolved ultraviolet resonance Raman measurements in general, are estimated. Peak values of calculated information transfer, ∼0.01-0.04 bits, are about two orders of magnitude smaller than the information content of residues. They are comparable to mutual information values, however. Estimated transfer rates are in the order of 1-20 megabits per second, and sustained transfer during the activity time-span of proteins may be significant. Information transfer from third order contributions is one to two orders of magnitude smaller than the harmonic terms, showing that harmonic analysis is a good approximation to information transfer.

Extracting Dynamical Correlations and Identifying Key Residues for Allosteric Communication in Proteins by correlationplus.

Extracting dynamical pairwise correlations and identifying key residues from large molecular dynamics trajectories or normal-mode analysis of coarse-grained models are important for explaining various processes like ligand binding, mutational effects, and long-distance interactions. Efficient and flexible tools to perform this task can provide new insights about residues involved in allosteric regulation and protein function. In addition, combining and comparing dynamical coupling information with sequence coevolution data can help to understand better protein function. To this aim, we developed a Python package called correlationplus to calculate, visualize, and analyze pairwise correlations. In this way, the package aids to identify key residues and interactions in proteins. The source code of correlationplus is available under LGPL version 3 at https://github.com/tekpinar/correlationplus. The current version of the package (0.2.0) can be installed with common installation methods like conda or pip in addition to source code installation. Moreover, docker images are also available for usage of the code without installation.

Mutual information analysis of the dynamic correlation between side chains in proteins.

Protein dynamics play an essential role in function regulation. In recent years, many experimental and theoretical studies have shown that changes in protein fluctuations in the backbone and side chains fulfill a pivotal role associated with amino acid mutations, chemical modifications, and ligand binding. The dynamic correlations between protein side chains have not been sufficiently studied, and no reliable analysis method has been available so far. Therefore, we developed a method to evaluate the dynamic correlation between protein side chains using mutual information and molecular dynamics simulations. To eliminate the structural superposition errors dealing with conventional analysis methods, and to accurately extract the intrinsic fluctuation properties of the side chains, we employed distance principal component analysis (distPCA). The motion of the side chain was then projected onto the eigenvector space obtained by distPCA, and the mutual information between the projected motions was calculated. The proposed method was then applied to a small protein "eglin c" and the mutants. The results show that even a single mutation significantly changed the dynamic correlations and also suggest that the dynamic change is deeply related to the stability. Those results indicate that our developed method could be useful for analyzing the molecular mechanism of allosteric communication in proteins.

Allosteric Communication Networks in Proteins Revealed through Pocket Crosstalk Analysis.

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

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

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

Detection of Side Chain Rearrangements Mediating the Motions of Transmembrane Helices in Molecular Dynamics Simulations of G Protein-Coupled Receptors

Structure and dynamics are essential elements of protein function. Protein structure is constantly fluctuating and undergoing conformational changes, which are captured by molecular dynamics (MD) simulations. We introduce a computational framework that provides a compact representation of the dynamic conformational space of biomolecular simulations. This method presents a systematic approach designed to reduce the large MD simulation spatiotemporal datasets into a manageable set in order to guide our understanding of how protein mechanics emerge from side chain organization and dynamic reorganization. We focus on the detection of side chain interactions that undergo rearrangements mediating global domain motions and vice versa. Side chain rearrangements are extracted from side chain interactions that undergo well-defined abrupt and persistent changes in distance time series using Gaussian mixture models, whereas global domain motions are detected using dynamic cross-correlation. Both side chain rearrangements and global domain motions represent the dynamic components of the protein MD simulation, and are both mapped into a network where they are connected based on their degree of coupling. This method allows for the study of allosteric communication in proteins by mapping out the protein dynamics into an intramolecular network to reduce the large simulation data into a manageable set of communities composed of coupled side chain rearrangements and global domain motions. This computational framework is suitable for the study of tightly packed proteins, such as G protein-coupled receptors, and we present an application on a seven microseconds MD trajectory of CC chemokine receptor 7 (CCR7) bound to its ligand CCL21.

Comparing local search paths with global search paths on protein residue networks: allosteric communication

Although proteins have been recognized as small-world networks and their small-world network properties of clustering and short paths have been exploited computationally to produce biologically relevant information, they have not been truly explored as such, i.e. as navigable small-world networks in the original spirit of Milgram's work. This research seeks to fill this gap by exploring local search on a network representation of proteins and to probe the source of navigability in proteins. Previously, we confirmed that proteins are navigable small-world networks and observed that local search paths exhibit different characteristics from global search paths. In this paper, we investigate the biological relevance of the differences in path characteristics on a type III receptor tyrosine kinase (KIT). A chief difference that works in favour of local search paths as intra-protein communication pathways is their weaker proclivity, compared to global search paths, for traversing long-range edges. Long-range edges tend to be less stable and their inclusion tends to decrease the communication propensity of a path. The source of protein navigability is traced to clustering provided by short-range edges. The majority of a protein's short-range edges reside within structures deemed important for long-range energy transport and modulation of allosteric communication in proteins. Therefore, the disruption of intra-protein communication as a result of the destruction of these structures via random rewiring is expected. A local search perspective leads us to this expected conclusion while a global search perspective does not. These findings initiate the compilation of a list of path properties that are characteristic of intra-protein pathways and could suggest fresh avenues for evolving and regulating navigable (small-world) networks.

Coupled Dynamics and Entropic Contribution to the Allosteric Mechanism of Pin1.

Allosteric communication in proteins regulates a plethora of downstream processes in subcellular signaling pathways. Describing the effects of cooperative ligand binding on the atomic level is a key to understanding many regulatory processes involving biomolecules. Here, we use microsecond-long molecular dynamics simulations to investigate the allosteric mechanism of Pin1, a potential therapeutic target and a phosphorylated-Ser/Thr dependent peptidyl-prolyl cis-trans isomerase that regulates several subcellular processes and has been implicated in many diseases, including cancer and Alzheimer's. Experimental studies suggest that the catalytic domain and the noncatalytic WW domain are allosterically coupled; however, an atomic level description of the dynamics associated with the interdomain communication is lacking. We show that binding of the substrate to the WW domain is directly coupled to the dynamics of the catalytic domain, causing rearrangement of the residue-residue contact dynamics from the WW domain to the catalytic domain. The binding affinity of the substrate in the catalytic domain is also enhanced upon binding of the substrate to the WW domain. Modulation of the dynamics of the catalytic domain upon binding of the substrate to the WW domain leads to prepayment of the entropic cost of binding the substrate to the catalytic domain. This study shows that Ile 28 at the interfacial region between the catalytic and WW domains is certainly one of the residues responsible for bridging the communication between the two domains. The results complement previous experiments and provide valuable atomistic insights into the role of dynamics and possible entropic contribution to the allosteric mechanism of proteins.

A Chemical Perspective on Allostery.

Much work has been done in the past decade to quantify the phenomenon of allosteric communication in proteins. Every new study unveils an extra piece of the puzzle in our search for an understanding of allostery that allows us to make predictions on the response of a protein to medically relevant stimuli such as pathological mutations or drug binding. This review summarizes recent advances in the analysis of mechanisms of allosteric communication in proteins, and combines this new knowledge to offer a perspective of allostery which is consistent with chemical views of molecular processes. First, we review recent work, particularly computational, on the characterization of signal propagation and conformational changes in allosteric proteins. We then compare different models of allostery, and discuss the significance of the concept of an allosteric pathway. We argue that allostery can be rationalized in terms of pathways of residues that efficiently transmit energy between different binding sites. We then provide examples that show how this picture could account for most of the observed data, since energy flow may be manifested as changes in both structure and dynamics. We conclude by acknowledging that the proposed view is still a simplification and should not be taken as a rigorous model of allosteric communication in proteins. Nevertheless, simple pictures like this can go a long way in improving our understanding of many complex phenomena observed in nature.

Allosteric communication pathways and thermal rectification in PDZ-2 protein: a computational study.

Allosteric communication in proteins is a fundamental and yet unresolved problem of structural biochemistry. Previous findings, from computational biology ( Ota, N.; Agard, D. A. J. Mol. Biol. 2005 , 351 , 345 - 354 ), have proposed that heat diffuses in a protein through cognate protein allosteric pathways. This work studied heat diffusion in the well-known PDZ-2 protein, and confirmed that this protein has two cognate allosteric pathways and that heat flows preferentially through these. Also, a new property was also observed for protein structures: heat diffuses asymmetrically through the structures. The underling structure of this asymmetrical heat flow was a normal length hydrogen bond (∼2.85 Å) that acted as a thermal rectifier. In contrast, thermal rectification was compromised in short hydrogen bonds (∼2.60 Å), giving rise to symmetrical thermal diffusion. Asymmetrical heat diffusion was due, on a higher scale, to the local, structural organization of residues that, in turn, was also mediated by hydrogen bonds. This asymmetrical/symmetrical energy flow may be relevant for allosteric signal communication directionality in proteins and for the control of heat flow in materials science.

Hydrogen bonds and heat diffusion in α-helices: a computational study.

Recent evidence has shown a correlation between the heat diffusion pathways and the known allosteric communication pathways in proteins. Allosteric communication in proteins is a central, yet unsolved, problem in biochemistry, and the study and characterization of the structural determinants that mediate energy transfer among different parts of proteins is of major importance. In this work, we characterized the role of hydrogen bonds in diffusivity of thermal energy for two sets of α-helices with different abilities to form hydrogen bonds. These hydrogen bonds can be a constitutive part of the α-helices or can arise from the lateral chains. In our in vacuo simulations, it was observed that α-helices with a higher possibility of forming hydrogen bonds also had higher rates of thermalization. Our simulations also revealed that heat readily flowed through atoms involved in hydrogen bonds. As a general conclusion, according to our simulations, hydrogen bonds fulfilled an important role in heat diffusion in structural patters of proteins.

SPACER: server for predicting allosteric communication and effects of regulation

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

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

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

Modeling allosteric signal propagation using protein structure networks

Allosteric communication in proteins can be induced by the binding of effective ligands, mutations or covalent modifications that regulate a site distant from the perturbed region. To understand allosteric regulation, it is important to identify the remote sites that are affected by the perturbation-induced signals and how these allosteric perturbations are transmitted within the protein structure. In this study, by constructing a protein structure network and modeling signal transmission with a Markov random walk, we developed a method to estimate the signal propagation and the resulting effects. In our model, the global perturbation effects from a particular signal initiation site were estimated by calculating the expected visiting time (EVT), which describes the signal-induced effects caused by signal transmission through all possible routes. We hypothesized that the residues with high EVT values play important roles in allosteric signaling. We applied our model to two protein structures as examples, and verified the validity of our model using various types of experimental data. We also found that the hot spots in protein binding interfaces have significantly high EVT values, which suggests that they play roles in mediating signal communication between protein domains.

Explicit Hydrogen-Bond Potentials and Their Application to NMR Scalar Couplings in Proteins.

Hydrogen bonds (H bonds) are fundamental for the stability, structure, and dynamics of chemically and biologically relevant systems. One of the direct means to detect H bonds in proteins is NMR spectroscopy. As H bonds are dynamic in nature, atomistic simulations offer a meaningful way to characterize and analyze properties of hydrogen bonds, provided a sufficiently accurate interaction potential is available. Here, we use explicit H-bond potentials to investigate scalar coupling constants (h3)JNC' and characterize the conformational ensemble for increasingly accurate intermolecular potentials. By considering a range of proteins with different overall topology a general procedure to improve the hydrogen-bonding potential ("morphing potentials") based on experimental information is derived. The robustness of this approach is established through explicit simulations in full solvation and comparison with experimental results. The H-bond potentials used here lead to more directional H bonds than conventional electrostatic representations employed in molecular mechanics potentials. It is found that the optimized potentials lead to H-bond geometries in remarkable agreement with previous ab initio and knowledge-based approaches to H bonds in model systems and in proteins. This suggests that, by combining theory, computation, and experimental data, H-bonding potentials can be improved and are potentially useful to better study coupling, energy transfer, and allosteric communication in proteins.

Allosteric Communication Occurs via Networks of Tertiary and Quaternary Motions in Proteins

Allosteric proteins bind an effector molecule at one site resulting in a functional change at a second site. We hypothesize that allosteric communication in proteins relies upon networks of quaternary (collective, rigid-body) and tertiary (residue–residue contact) motions. We argue that cyclic topology of these networks is necessary for allosteric communication. An automated algorithm identifies rigid bodies from the displacement between the inactive and the active structures and constructs “quaternary networks” from these rigid bodies and the substrate and effector ligands. We then integrate quaternary networks with a coarse-grained representation of contact rearrangements to form “global communication networks” (GCNs). The GCN reveals allosteric communication among all substrate and effector sites in 15 of 18 multidomain and multimeric proteins, while tertiary and quaternary networks exhibit such communication in only 4 and 3 of these proteins, respectively. Furthermore, in 7 of the 15 proteins connected by the GCN, 50% or more of the substrate-effector paths via the GCN are “interdependent” paths that do not exist via either the tertiary or the quaternary network. Substrate-effector “pathways” typically are not linear but rather consist of polycyclic networks of rigid bodies and clusters of rearranging residue contacts. These results argue for broad applicability of allosteric communication based on structural changes and demonstrate the utility of the GCN. Global communication networks may inform a variety of experiments on allosteric proteins as well as the design of allostery into non-allosteric proteins.

Contact rearrangements form coupled networks from local motions in allosteric proteins

Allosteric proteins bind an effector molecule at one site resulting in a functional change at a second site. We hypothesize that networks of contacts altered, formed, or broken are a significant contributor to allosteric communication in proteins. In this work, we identify which interactions change significantly between the residue-residue contact networks of two allosteric structures, and then organize these changes into graphs. We perform the analysis on 15 pairs of allosteric structures with effector and substrate each present in at least one of the two structures. Most proteins exhibit large, dense regions of contact rearrangement, and the graphs form connected paths between allosteric effector and substrate sites in five of these proteins. In the remaining 10 proteins, large-scale conformational changes such as rigid-body motions are likely required in addition to contact rearrangement networks to account for substrate-effector communication. On average, clusters which contain at least one substrate or effector molecule comprise 20% of the protein. These allosteric graphs are small worlds; that is, they typically have mean shortest path lengths comparable to those of corresponding random graphs and average clustering coefficients enhanced relative to those of random graphs. The networks capture 60-80% of known allostery-perturbing mutants in three proteins, and the metrics degree and closeness are statistically good discriminators of mutant residues from nonmutant residues within the networks in two of these three proteins. For two proteins, coevolving clusters of residues which have been hypothesized to be allosterically important differ from the regions with the most contact rearrangement. Residues and contacts which modulate normal mode fluctuations also often participate in the contact rearrangement networks. In summary, residue-residue contact rearrangement networks provide useful representations of the portions of allosteric pathways resulting from coupled local motions.

Erratum: Evolutionarily conserved networks of residues mediate allosteric communication in proteins

Nat. Struct. Biol. 10, 59–69 (2003). Because of resolution mismatch, Figs. 3c, 5c, d, 7b, c of this paper appear blurry in print. The pixels in these matrices have sharp edges, and the correct panels are now reprinted. We apologize for any inconvenience this may have caused.

Evolutionarily conserved networks of residues mediate allosteric communication in proteins.

A fundamental goal in cellular signaling is to understand allosteric communication, the process by which signals originating at one site in a protein propagate reliably to affect distant functional sites. The general principles of protein structure that underlie this process remain unknown. Here, we describe a sequence-based statistical method for quantitatively mapping the global network of amino acid interactions in a protein. Application of this method for three structurally and functionally distinct protein families (G protein-coupled receptors, the chymotrypsin class of serine proteases and hemoglobins) reveals a surprisingly simple architecture for amino acid interactions in each protein family: a small subset of residues forms physically connected networks that link distant functional sites in the tertiary structure. Although small in number, residues comprising the network show excellent correlation with the large body of mechanistic data available for each family. The data suggest that evolutionarily conserved sparse networks of amino acid interactions represent structural motifs for allosteric communication in proteins.