Propagation of Dynamic Changes in Barnase Upon Binding of Barstar: An NMR and Computational Study

Insights into mechanisms of MALT1 allostery from NMR and AlphaFold dynamic analyses

Mucosa-associated lymphoid tissue lymphoma-translocation protein 1 (MALT1) is an attractive target for the development of modulatory compounds in the treatment of lymphoma and other cancers. While the three-dimensional structure of MALT1 has been previously determined through X-ray analysis, its dynamic behaviour in solution has remained unexplored. We present here dynamic analyses of the apo MALT1 form along with the E549A mutation. This investigation used NMR 15N relaxation and NOE measurements between side-chain methyl groups. Our findings confirm that MALT1 exists as a monomer in solution, and demonstrate that the domains display semi-independent movements in relation to each other. Our dynamic study, covering multiple time scales, along with the assessment of conformational populations by Molecular Dynamic simulations, Alpha Fold modelling and PCA analysis, put the side chain of residue W580 in an inward position, shedding light at potential mechanisms underlying the allosteric regulation of this enzyme.

Gauging Dynamics-driven Allostery Using a New Computational Tool: A CAP Case Study

In this study, we utilize Protein Residue Networks (PRNs), constructed using Local Spatial Pattern (LSP) alignment, to explore the dynamic behavior of Catabolite Activator Protein (CAP) upon the sequential binding of cAMP. We employed the Degree Centrality of these PRNs to investigate protein dynamics on a sub-nanosecond time scale, hypothesizing that it would reflect changes in CAP's entropy related to its thermal motions. We show that the binding of the first cAMP led to an increase in stability in the Cyclic-Nucleotide Binding Domain A (CNBD-A) and destabilization in CNBD-B, agreeing with previous reports explaining the negative cooperativity of cAMP binding in terms of an entropy-driven allostery. LSP-based PRNs also allow for the study of Betweenness Centrality, another graph-theoretical characteristic of PRNs, providing insights into global residue connectivity within CAP. Using this approach, we were able to correctly identify amino acids that were shown to be critical in mediating allosteric interactions in CAP. The agreement between our studies and previous experimental reports validates our method, particularly with respect to the reliability of Degree Centrality as a proxy for entropy related to protein thermal dynamics. Because LSP-based PRNs can be easily extended to include dynamics of small organic molecules, polynucleotides, or other allosteric proteins, the methods presented here mark a significant advancement in the field, positioning them as vital tools for a fast, cost-effective, and accurate analysis of entropy-driven allostery and identification of allosteric hotspots.

A Computational Biology Study on the Structure and Dynamics Determinants of Thermal Stability of the Chitosanase from Aspergillus fumigatus

Environmentally friendly and efficient biodegradation with chitosanase for degrading chitosan to oligosaccharide has been gaining more importance. Here, we studied a chitosanase from Aspergillus fumigatus with potential for production, but does not have the ideal thermal stability. The structure predicted by the Alphafold2 model, especially the binding site and two catalytic residues, has been found to have a high similarity with the experimental structure of the chitosanase V-CSN from the same family. The effects of temperature on structure and function were studied by dynamic simulation and the results showed that the binding site had high flexibility. After heating up from 300 K to 350 K, the RMSD and RMSF of the binding site increased significantly, in particular, the downward shift of loop6 closed the binding site, resulting in the spatial hindrance of binding. The time proportions of important hydrogen bonds at the binding site decreased sharply, indicating that serious disruption of hydrogen bonds should be the main interaction factor for conformational changes. The residues contributing energetically to binding were also revealed to be in the highly flexible region, which inevitably leads to the decrease in the activity stability at high temperature. These findings provide directions for the modification of thermal stability and perspectives on the research of proteins without experimental structures.

A beginner's guide to molecular dynamics simulations and the identification of cross-correlation networks for enzyme engineering

The functional properties of proteins are decided not only by their relatively rigid overall structures, but even more importantly, by their dynamic properties. In a protein, some regions of structure exhibit highly correlated or anti-correlated motions with others, some are highly dynamic but uncorrelated, while other regions are relatively static. The residues with correlated or anti-correlated motions can form a so-called dynamic cross-correlation network, through which information can be transmitted. Such networks have been shown to be critical to allosteric transitions, and ligand binding, and have also been shown to be able to mediate epistatic interactions between mutations. As a result, they are likely to play a significant role in the development of new enzyme engineering strategies. In this chapter, protocols are provided for the assessment of dynamic cross-correlation networks, and for their application in protein engineering. Transketolase from E. coli is used as a model and the software GROMACS is applied for carrying out MD simulations to generate trajectories containing structural ensembles. The trajectory is then used for a dynamic cross correlation analysis using the R package, Bio3D. A matrix of all atom-wise cross-correlation coefficients is finally obtained, which can be displayed in a graphical representation termed a dynamical cross-correlation matrix.

Learning the changes of barnase mutants thermostability from structural fluctuations obtained using anisotropic network modeling

In biotechnology applications, rational design of new proteins with improved physico-chemical properties includes a number of important tasks. One of the greatest practical and fundamental challenges is the design of highly thermostable protein enzymes that maintain catalytic activity at high temperatures. This problem may be solved by introducing mutations into the wild-type enzyme protein. In this work, to predict the impact of such mutations in barnase protein we applied the anisotropic network modeling approach, revealing atomic fluctuations in structural regions that are changed in mutants compared to the wild-type protein. A regression model was constructed based on these structural features that can allow one to predict the thermal stability of new barnase mutants. Moreover, the analysis of regression model provides a mechanistic explanation of how the structural features can contribute to the thermal stability of barnase mutants.

Protein-protein binding pathways and calculations of rate constants using fully-continuous, explicit-solvent simulations

A grand challenge in the field of biophysics has been the complete characterization of protein-protein binding processes at atomic resolution. This characterization requires the direct simulation of binding pathways starting from the initial, unbound state and proceeding through states that are too transient to be captured by experiment. Here, we applied the weighted ensemble path sampling strategy to orchestrate atomistic simulation of protein-protein binding pathways. Our simulation generated 203 fully-continuous and independent pathways along with rate constants for the binding process involving the barnase and barstar proteins. Results reveal multiple binding pathways along a "funnel-like" free energy landscape in which the formation of the "encounter complex" intermediate is rate-limiting followed by a relatively rapid rearrangement of the encounter complex to the bound state. Among all diffusional collisions, only ∼11% were productive. In the most probable binding pathways, the proteins rotated to a large extent (likely via electrostatic steering) in order to collide productively followed by "rolling" of the proteins along each other's binding interfaces to reach the bound state. Consistent with experiment, R59 was identified as the most kinetically important barnase residue for the binding process. Furthermore, protein desolvation occurs late in the binding process during the rearrangement of the encounter complex to the bound state. Notably, the positions of crystallographic water molecules that bridge hydrogen bonds between barnase and barstar are occupied in the bound-state ensemble. Our simulation was completed in a month using 1600 CPU cores at a time, demonstrating that it is now practical to carry out atomistic simulations of protein-protein binding.

Why the Energy Landscape of Barnase Is Hierarchical

We have used NMR and computational methods to characterize the dynamics of the ribonuclease barnase over a wide range of timescales in free and inhibitor-bound states. Using temperature- and denaturant-dependent measurements of chemical shift, we show that barnase undergoes frequent and highly populated hinge bending. Using relaxation dispersion, we characterize a slower and less populated motion with a rate of 750 ± 200 s⁻¹, involving residues around the lip of the active site, which occurs in both free and bound states and therefore suggests conformational selection. Normal mode calculations characterize correlated hinge bending motions on a very rapid timescale. These three measurements are combined with previous measurements and molecular dynamics calculations on barnase to characterize its dynamic landscape on timescales from picoseconds to milliseconds and length scales from 0.1 to 2.5 nm. We show that barnase has two different large-scale fluctuations: one on a timescale of 10⁻⁹−10⁻⁶ s that has no free energy barrier and is a hinge bending that is determined by the architecture of the protein; and one on a timescale of milliseconds (i.e., 750 s⁻¹) that has a significant free energy barrier and starts from a partially hinge-bent conformation. These two motions can be described as hierarchical, in that the more highly populated faster motion provides a platform for the slower (less probable) motion. The implications are discussed. The use of temperature and denaturant is suggested as a simple and general way to characterize motions on the intermediate ns-μs timescale.

Coupled molecular dynamics mediate long- and short-range epistasis between mutations that affect stability and aggregation kinetics

Multiple mutations are typically required to significantly improve protein stability or aggregation kinetics. However, when several substitutions are made in a single protein, the mutations can potentially interact in a nonadditive manner, resulting in epistatic effects, which can hamper protein-engineering strategies to improve thermostability or aggregation kinetics. Here, we have examined the role of protein dynamics in mediating epistasis between pairs of mutations. With Escherichia coli transketolase (TK) as a model, we explored the epistatic interactions between two single variants H192P and A282P, and also between the double-mutant H192P/A282P and two single variants, I365L or G506A. Epistasis was determined for several measures of protein stability, including the following: the free-energy barrier to kinetic inactivation, ∆∆G ^‡; thermal transition midpoint temperatures, T _m; and aggregation onset temperatures, T _agg Nonadditive epistasis was observed between neighboring mutations as expected, but also for distant mutations located in the surface and core regions of different domains. Surprisingly, the epistatic behaviors for each measure of stability were often different for any given pairwise recombination, highlighting that kinetic and thermodynamic stabilities do not always depend on the same structural features. Molecular-dynamics simulations and a pairwise cross-correlation analysis revealed that mutations influence the dynamics of their local environment, but also in some cases the dynamics of regions distant in the structure. This effect was found to mediate epistatic interactions between distant mutations and could therefore be exploited in future protein-engineering strategies.

Gaussian network model can be enhanced by combining solvent accessibility in proteins

Gaussian network model (GNM), regarded as the simplest and most representative coarse-grained model, has been widely adopted to analyze and reveal protein dynamics and functions. Designing a variation of the classical GNM, by defining a new Kirchhoff matrix, is the way to improve the residue flexibility modeling. We combined information arising from local relative solvent accessibility (RSA) between two residues into the Kirchhoff matrix of the parameter-free GNM. The undetermined parameters in the new Kirchhoff matrix were estimated by using particle swarm optimization. The usage of RSA was motivated by the fact that our previous work using RSA based linear regression model resulted out higher prediction quality of the residue flexibility when compared with the classical GNM and the parameter free GNM. Computational experiments, conducted based on one training dataset, two independent datasets and one additional small set derived by molecular dynamics simulations, demonstrated that the average correlation coefficients of the proposed RSA based parameter-free GNM, called RpfGNM, were significantly increased when compared with the parameter-free GNM. Our empirical results indicated that a variation of the classical GNMs by combining other protein structural properties is an attractive way to improve the quality of flexibility modeling.

Identification of Hot Spots in Protein Structures Using Gaussian Network Model and Gaussian Naive Bayes

Residue fluctuations in protein structures have been shown to be highly associated with various protein functions. Gaussian network model (GNM), a simple representative coarse-grained model, was widely adopted to reveal function-related protein dynamics. We directly utilized the high frequency modes generated by GNM and further performed Gaussian Naive Bayes (GNB) to identify hot spot residues. Two coding schemes about the feature vectors were implemented with varying distance cutoffs for GNM and sliding window sizes for GNB based on tenfold cross validations: one by using only a single high mode and the other by combining multiple modes with the highest frequency. Our proposed methods outperformed the previous work that did not directly utilize the high frequency modes generated by GNM, with regard to overall performance evaluated using F1 measure. Moreover, we found that inclusion of more high frequency modes for a GNB classifier can significantly improve the sensitivity. The present study provided additional valuable insights into the relation between the hot spots and the residue fluctuations.

Pressure-Dependent Conformation and Fluctuation in Folded Protein Molecules

Hydrostatic pressure leads to nonuniform compression of proteins. The structural change is on average only about 0.1 Å kbar(-1), and is therefore within the range of fluctuations at ambient pressure. The largest changes are around cavities and buried water molecules. Sheets distort much more than helices. Hydrogen bonds compress about 0.012 Å kbar(-1), although there is a wide range, including some hydrogen bonds that lengthen. In the presence of ligands and inhibitors, structural changes are smaller. Pressure has little effect on rapid fluctuations, but with larger scale slower motions, pressure increases the population of excited states (if they have smaller overall volume), and slows the fluctuations. In barnase, pressure is shown to be a useful way to characterise fluctuations on the timescale of microseconds, and helps to show that fluctuations in barnase are hierarchical, with the faster fluctuations providing a platform for the slower ones. The excited states populated at high pressure are probably functionally important.

Investigating the dynamic aspects of drug-protein recognition through a combination of MD and NMR analyses: implications for the development of protein-protein interaction inhibitors

In this paper, we investigate the dynamic aspects of the molecular recognition between a small molecule ligand and a flat, exposed protein surface, representing a typical target in the development of protein-protein interaction inhibitors. Specifically, we analyze the complex between the protein Fibroblast Growth Factor 2 (FGF2) and a recently discovered small molecule inhibitor, labeled sm27 for which the binding site and the residues mainly involved in small molecule recognition have been previously characterized. We have approached this problem using microsecond MD simulations and NMR-based characterizations of the dynamics of the apo and holo states of the system. Using direct combination and cross-validation of the results of the two techniques, we select the set of conformational states that best recapitulate the principal dynamic and structural properties of the complex. We then use this information to generate a multi-structure representation of the sm27-FGF2 interaction. We propose this kind of representation and approach as a useful tool in particular for the characterization of systems where the mutual dynamic influence between the interacting partners is expected to play an important role. The results presented can also be used to generate new rules for the rational expansion of the chemical diversity space of FGF2 inhibitors.

Sequence-based Gaussian network model for protein dynamics

Motivation: Gaussian network model (GNM) is widely adopted to analyze and understand protein dynamics, function and conformational changes. The existing GNM-based approaches require atomic coordinates of the corresponding protein and cannot be used when only the sequence is known.

Results: We report, first of its kind, GNM model that allows modeling using the sequence. Our linear regression-based, parameter-free, sequence-derived GNM (L-pfSeqGNM) uses contact maps predicted from the sequence and models local, in the sequence, contact neighborhoods with the linear regression. Empirical benchmarking shows relatively high correlations between the native and the predicted with L-pfSeqGNM B-factors and between the cross-correlations of residue fluctuations derived from the structure- and the sequence-based GNM models. Our results demonstrate that L-pfSeqGNM is an attractive platform to explore protein dynamics. In contrast to the highly used GNMs that require protein structures that number in thousands, our model can be used to study motions for the millions of the readily available sequences, which finds applications in modeling conformational changes, protein–protein interactions and protein functions.

Contact:zerozhua@126.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Hydrodynamic effects on the relative rotational velocity of associating proteins

Hydrodynamic steering effects on the barnase-barstar association were studied through the analysis of the relative rotational velocity of the proteins. We considered the two proteins approaching each other in response to their electrostatic attraction and employed a method that accounts for the long-range and many-body character of the hydrodynamic interactions, as well as the complicated shapes of the proteins. Hydrodynamic steering effects were clearly seen when attractive forces were applied to the geometric centers of the proteins (resulting in zero torques) and the attraction acted along the line that connects centers of geometry of proteins in their crystallographic complex. When we rotated barstar relative to barnase around this line by an angle in the range from -90° to 60°, the rotational velocity arising solely from hydrodynamic interactions restored the orientation of the proteins in the crystal structure. However, because, in reality, both electrostatic forces and torques act on the proteins and these forces and torques depend on the protein-protein distance and the relative orientation of the binding partners, we also investigated more realistic situations employing continuum electrostatics calculations based on atomistic protein models. Overall, we conclude that hydrodynamic interactions aid barnase and barstar in assuming a proper relative orientation upon complex formation.

Molecular mechanism of allosteric communication in Hsp70 revealed by molecular dynamics simulations

Investigating ligand-regulated allosteric coupling between protein domains is fundamental to understand cell-life regulation. The Hsp70 family of chaperones represents an example of proteins in which ATP binding and hydrolysis at the Nucleotide Binding Domain (NBD) modulate substrate recognition at the Substrate Binding Domain (SBD). Herein, a comparative analysis of an allosteric (Hsp70-DnaK) and a non-allosteric structural homolog (Hsp110-Sse1) of the Hsp70 family is carried out through molecular dynamics simulations, starting from different conformations and ligand-states. Analysis of ligand-dependent modulation of internal fluctuations and local deformation patterns highlights the structural and dynamical changes occurring at residue level upon ATP-ADP exchange, which are connected to the conformational transition between closed and open structures. By identifying the dynamically responsive protein regions and specific cross-domain hydrogen-bonding patterns that differentiate Hsp70 from Hsp110 as a function of the nucleotide, we propose a molecular mechanism for the allosteric signal propagation of the ATP-encoded conformational signal.

Allostery, or the capability of proteins to respond to ligand binding events with a variation in structure or dynamics at a distant site, is a common feature for biomolecular function and regulation in a large number of proteins. Intra-protein connections and inter-residue coordinations underlie allosteric mechanisms and react to binding primarily through a finely tuned modulation of motions and structures at the microscopic scale. Hence, all-atom molecular dynamics simulations are suitable to investigate the molecular basis of allostery. Moreover, understanding intra-protein communication pathways at atomistic resolutions offers unique opportunities in rational drug design. Proteins of the Hsp70 family are allosteric molecular chaperones involved in maintaining cellular protein homeostasis. These proteins are involved in several types of cancer, neurodegenerative diseases, aging and infections and are therefore pharmaceutically relevant targets. In this work we have analyzed, by multiple molecular dynamics simulations, the long-range dynamical and conformational effects of ligands bound to Hsp70, and found relevant differences in comparison to the known non-allosteric structural homolog Hsp110. The resulting model of the mechanism of allosteric propagation offers the opportunity of identifying on-pathway allosteric druggable sites, which we propose could guide rational drug-design efforts targeting Hsp70.

cAMP-dependent allostery and dynamics in Epac: an NMR view

Epac (exchange protein directly activated by cAMP) is a critical cAMP receptor, which senses cAMP and couples the cAMP signal to the catalysis of guanine exchange in the Rap substrate. In the present paper, we review the NMR studies that we have undertaken on the CBD (cyclic-nucleotide-binding domain) of Epac1. Our NMR investigations have shown that cAMP controls distal autoinhibitory interactions through long-range modulations in dynamics. Such dynamically mediated allosteric effects contribute not only to the cAMP-dependent activation of Epac, but also to the selectivity of Epac for cAMP in contrast with cGMP. In addition, we have mapped the interaction networks that couple the cAMP-binding site to the sites involved in the autoinhibitory interactions, using a method based on the covariance analysis of NMR chemical shifts. We anticipate that this approach is generally applicable to dissect allosteric networks in signalling domains.

NMR reveals novel mechanisms of protein activity regulation

NMR spectroscopy is one of the most powerful tools for the characterization of biomolecular systems. A unique aspect of NMR is its capacity to provide an integrated insight into both the structure and intrinsic dynamics of biomolecules. In addition, NMR can provide site-resolved information about the conformation entropy of binding, as well as about energetically excited conformational states. Recent advances have enabled the application of NMR for the characterization of supramolecular systems. A summary of mechanisms underpinning protein activity regulation revealed by the application of NMR spectroscopy in a number of biological systems studied in the lab is provided.

Mapping allostery through the covariance analysis of NMR chemical shifts

Allostery is a fundamental mechanism of regulation in biology. The residues at the end points of long-range allosteric perturbations are commonly identified by the comparative analyses of structures and dynamics in apo and effector-bound states. However, the networks of interactions mediating the propagation of allosteric signals between the end points often remain elusive. Here we show that the covariance analysis of NMR chemical shift changes caused by a set of covalently modified analogs of the allosteric effector (i.e., agonists and antagonists) reveals extended networks of coupled residues. Unexpectedly, such networks reach not only sites subject to effector-dependent structural variations, but also regions that are controlled by dynamically driven allostery. In these regions the allosteric signal is propagated mainly by dynamic rather than structural modulations, which result in subtle but highly correlated chemical shift variations. The proposed chemical shift covariance analysis (CHESCA) identifies interresidue correlations based on the combination of agglomerative clustering (AC) and singular value decomposition (SVD). AC results in dendrograms that define functional clusters of coupled residues, while SVD generates score plots that provide a residue-specific dissection of the contributions to binding and allostery. The CHESCA approach was validated by applying it to the cAMP-binding domain of the exchange protein directly activated by cAMP (EPAC) and the CHESCA results are in full agreement with independent mutational data on EPAC activation. Overall, CHESCA is a generally applicable method that utilizes a selected chemical library of effector analogs to quantitatively decode the binding and allosteric information content embedded in chemical shift changes.

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.

Protein dynamics and allostery: an NMR view

Allostery, the process by which distant sites within a protein system are energetically coupled, is an efficient and ubiquitous mechanism for activity regulation. A purely mechanical view of allostery invoking only structural changes has developed over the decades as the classical view of the phenomenon. However, a fast growing list of examples illustrate the intimate link between internal motions over a wide range of time scales and function in protein-ligand interactions. Proteins respond to perturbations by redistributing their motions and they use fluctuating conformational states for binding and conformational entropy as a carrier of allosteric energy to modulate association with ligands. In several cases allosteric interactions proceed with minimal or no structural changes. We discuss emerging paradigms for the central role of protein dynamics in allostery.

Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework

Energy landscape theories have provided a common ground for understanding the protein folding problem, which once seemed to be overwhelmingly complicated. At the same time, the native state was found to be an ensemble of interconverting states with frustration playing a more important role compared to the folding problem. The landscape of the folded protein – the native landscape – is glassier than the folding landscape; hence, a general description analogous to the folding theories is difficult to achieve. On the other hand, the native basin phase volume is much smaller, allowing a protein to fully sample its native energy landscape on the biological timescales. Current computational resources may also be used to perform this sampling for smaller proteins, to build a ‘topographical map’ of the native landscape that can be used for subsequent analysis. Several major approaches to representing this topographical map are highlighted in this review, including the construction of kinetic networks, hierarchical trees and free energy surfaces with subsequent structural and kinetic analyses. In this review, we extensively discuss the important question of choosing proper collective coordinates characterizing functional motions. In many cases, the substates on the native energy landscape, which represent different functional states, can be used to obtain variables that are well suited for building free energy surfaces and analyzing the protein's functional dynamics. Normal mode analysis can provide such variables in cases where functional motions are dictated by the molecule's architecture. Principal component analysis is a more expensive way of inferring the essential variables from the protein's motions, one that requires a long molecular dynamics simulation. Finally, the two popular models for the allosteric switching mechanism, ‘preexisting equilibrium’ and ‘induced fit’, are interpreted within the energy landscape paradigm as extreme points of a continuum of transition mechanisms. Some experimental evidence illustrating each of these two models, as well as intermediate mechanisms, is presented and discussed.

An online approach for mining collective behaviors from molecular dynamics simulations

Collective behavior involving distally separate regions in a protein is known to widely affect its function. In this article, we present an online approach to study and characterize collective behavior in proteins as molecular dynamics (MD) simulations progress. Our representation of MD simulations as a stream of continuously evolving data allows us to succinctly capture spatial and temporal dependencies that may exist and analyze them efficiently using data mining techniques. By using tensor analysis we identify (a) collective motions (i.e., dynamic couplings) and (b) time-points during the simulation where the collective motions suddenly change. We demonstrate the applicability of this method on two different protein simulations for barnase and cyclophilin A. We characterize the collective motions in these proteins using our method and analyze sudden changes in these motions. Taken together, our results indicate that tensor analysis is well suited to extracting information from MD trajectories in an online fashion.

Referencing strategy for the direct comparison of nuclear magnetic resonance and molecular dynamics motional parameters in RNA

Nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations are both techniques that can be used to characterize the structural dynamics of biomolecules and their underlying time scales. Comparison of relaxation parameters obtained through each methodology allows for cross validation of techniques and for complementarity in the analysis of dynamics. Here we present a combined NMR/MD study of the dynamics of HIV-1 transactivation response (TAR) RNA. We compute relaxation constants (R(1), R(2), and NOE) and model-free parameters (S(2) and tau) from a 65 ns molecular dynamics (MD) trajectory and compare them with the respective parameters measured in a domain-elongation NMR experiment. Using the elongated domain as the frame of reference for all computed parameters allows for a direct comparison between experiment and simulation. We see good agreement for many parameters and gain further insight into the nature of the local and global dynamics of TAR, which are found to be quite complex, spanning multiple time scales. For the few cases where agreement is poor, comparison of the dynamical parameters provides insight into the limits of each technique. We suggest a frequency-matching procedure that yields an upper bound for the time scale of dynamics to which the NMR relaxation experiment is sensitive.

Pressure-dependent structure changes in barnase on ligand binding reveal intermediate rate fluctuations

In this work we measured 1H NMR chemical shifts for the ribonuclease barnase at pressures from 3 MPa to 200 MPa, both free and bound to d(CGAC). Shift changes with pressure were used as restraints to determine the change in structure with pressure. Free barnase is compressed by approximately 0.7%. The largest changes are on the ligand-binding face close to Lys-27, which is the recognition site for the cleaved phosphate bond. This part of the protein also contains the buried water molecules. In the presence of d(CGAC), the compressibility is reduced by approximately 70% and the region of structural change is altered: the ligand-binding face is now almost incompressible, whereas changes occur at the opposite face. Because compressibility is proportional to mean square volume fluctuation, we conclude that in free barnase, volume fluctuation is largest close to the active site, but when the inhibitor is bound, the fluctuations become much smaller and are located mainly on the opposite face. The timescale of the fluctuations is nanoseconds to microseconds, consistent with the degree of ordering required for the fluctuations, which are intermediate between rapid uncorrelated side-chain dynamics and slow conformational transitions. The high-pressure technique is therefore useful for characterizing motions on this relatively inaccessible timescale.

Perturbation-response scanning reveals ligand entry-exit mechanisms of ferric binding protein

We study apo and holo forms of the bacterial ferric binding protein (FBP) which exhibits the so-called ferric transport dilemma: it uptakes iron from the host with remarkable affinity, yet releases it with ease in the cytoplasm for subsequent use. The observations fit the “conformational selection” model whereby the existence of a weakly populated, higher energy conformation that is stabilized in the presence of the ligand is proposed. We introduce a new tool that we term perturbation-response scanning (PRS) for the analysis of remote control strategies utilized. The approach relies on the systematic use of computational perturbation/response techniques based on linear response theory, by sequentially applying directed forces on single-residues along the chain and recording the resulting relative changes in the residue coordinates. We further obtain closed-form expressions for the magnitude and the directionality of the response. Using PRS, we study the ligand release mechanisms of FBP and support the findings by molecular dynamics simulations. We find that the residue-by-residue displacements between the apo and the holo forms, as determined from the X-ray structures, are faithfully reproduced by perturbations applied on the majority of the residues of the apo form. However, once the stabilizing ligand (Fe) is integrated to the system in holo FBP, perturbing only a few select residues successfully reproduces the experimental displacements. Thus, iron uptake by FBP is a favored process in the fluctuating environment of the protein, whereas iron release is controlled by mechanisms including chelation and allostery. The directional analysis that we implement in the PRS methodology implicates the latter mechanism by leading to a few distant, charged, and exposed loop residues. Upon perturbing these, irrespective of the direction of the operating forces, we find that the cap residues involved in iron release are made to operate coherently, facilitating release of the ion.

Upon binding ligands, many proteins undergo structural changes compared to the unbound form. We introduce a methodology to monitor these changes and to study which mechanisms arrange conformational shifts between the liganded and free forms. Our method is simple, yet it efficiently characterizes the response of proteins to a given perturbation on systematically selected residues. The coherent responses predicted are validated by molecular dynamics simulations. The results indicate that the iron uptake by the ferric binding protein is favorable in a thermally fluctuating environment, while release of iron is allosterically moderated. Since ferric binding protein exhibits a high sequence identity with human transferrin whose allosteric anion binding sites generate large conformational changes around the binding region, we suggest mutational studies on remotely controlling sites identified in this work.

Frameworks for understanding long-range intra-protein communication

The phenomenon of intra-protein communication is fundamental to such processes as allostery and signaling, yet comparatively little is understood about its physical origins despite notable progress in recent years. This review introduces contemporary but distinct frameworks for understanding intra-protein communication by presenting both the ideas behind them and a discussion of their successes and shortcomings. The first framework holds that intra-protein communication is accomplished by the sequential mechanical linkage of residues spanning a gap between distal sites. According to the second framework, proteins are best viewed as ensembles of distinct structural microstates, the dynamical and thermodynamic properties of which contribute to the experimentally observable macroscale properties. Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying intra-protein communication, and the insights into both frameworks it provides are presented through a discussion of numerous examples from the literature. Distinct from mechanical and thermodynamic considerations of intra-protein communication are recently applied graph and network theoretic analyses. These computational methods reduce complex three dimensional protein architectures to simple maps comprised of nodes (residues) connected by edges (inter-residue "interactions"). Analysis of these graphs yields a characterization of the protein's topology and network characteristics. These methods have shown proteins to be "small world" networks with moderately high local residue connectivities existing concurrently with a small but significant number of long range connectivities. However, experimental studies of the tantalizing idea that these putative long range interaction pathways facilitate one or several macroscopic protein characteristics are unfortunately lacking at present. This review concludes by comparing and contrasting the presented frameworks and methodologies for studying intra-protein communication and suggests a manner in which they can be brought to bear simultaneously to further enhance our understanding of this important fundamental phenomenon.

Allosteric regulation and catalysis emerge via a common route

Allosteric regulation of protein function is a mechanism by which an event in one place of a protein structure causes an effect at another site, much like the behavior of a telecommunications network in which a collection of transmitters, receivers and transceivers communicate with each other across long distances. For example, ligand binding or an amino acid mutation at an allosteric site can alter enzymatic activity or binding affinity in a distal region such as the active site or a second binding site. The mechanism of this site-to-site communication is of great interest, especially since allosteric effects must be considered in drug design and protein engineering. In this review, conformational mobility as the common route between allosteric regulation and catalysis is discussed. We summarize recent experimental data and the resulting insights into allostery within proteins, and we discuss the nature of future studies and the new applications that may result from increased understanding of this regulatory mechanism.

Conformational and dynamics changes induced by bile acids binding to chicken liver bile acid binding protein

The correlation between protein motions and function is a central problem in protein science. Several studies have demonstrated that ligand binding and protein dynamics are strongly correlated in intracellular lipid binding proteins (iLBPs), in which the high degree of flexibility, principally occurring at the level of helix-II, CD, and EF loops (the so-called portal area), is significantly reduced upon ligand binding. We have recently investigated by NMR the dynamic properties of a member of the iLBP family, chicken liver bile acid binding protein (cL-BABP), in its apo and holo form, as a complex with two bile salts molecules. Binding was found to be regulated by a dynamic process and a conformational rearrangement was associated with this event. We report here the results of molecular dynamics (MD) simulations performed on apo and holo cL-BABP with the aim of further characterizing the protein regions involved in motion propagation and of evaluating the main molecular interactions stabilizing bound ligands. Upon binding, the root mean square fluctuation values substantially decrease for CD and EF loops while increase for the helix-loop-helix region, thus indicating that the portal area is the region mostly affected by complex formation. These results nicely correlate with backbone dynamics data derived from NMR experiments. Essential dynamics analysis of the MD trajectories indicates that the major concerted motions involve the three contiguous structural elements of the portal area, which however are dynamically coupled in different ways whether in the presence or in the absence of the ligands. Motions of the EF loop and of the helical region are part of the essential space of both apo and holo-BABP and sample a much wider conformational space in the apo form. Together with NMR results, these data support the view that, in the apo protein, the flexible EF loop visits many conformational states including those typical of the holo state and that the ligand acts stabilizing one of these pre-existing conformations. The present results, in agreement with data reported for other iLBPs, sharpen our knowledge on the binding mechanism for this protein family.

The solution structure and dynamics of human pancreatic ribonuclease determined by NMR spectroscopy provide insight into its remarkable biological activities and inhibition

Human pancreatic ribonuclease (RNase 1) is expressed in many tissues; has several important enzymatic and biological activities, including efficient cleavage of single-stranded RNA, double-stranded RNA and double-stranded RNA-DNA hybrids, digestion of dietary RNA, regulation of vascular homeostasis, inactivation of the HIV, activation of immature dendritic cells and induction of cytokine production; and furthermore shows potential as an anti-tumor agent. The solution structure and dynamics of uncomplexed, wild-type RNase 1 have been determined by NMR spectroscopy methods to better understand these activities. The family of 20 structures determined on the basis of 6115 unambiguous nuclear Overhauser enhancements is well resolved (pairwise backbone RMSD=1.07 A) and has the classic RNase A type of tertiary structure. Important structural differences compared with previously determined crystal structures of RNase 1 variants or inhibitor-bound complexes are observed in the conformation of loop regions and side chains implicated in the enzymatic as well as biological activities and binding to the cytoplasmic RNase inhibitor. Multiple side chain conformations observed for key surface residues are proposed to be crucial for membrane binding as well as translocation and efficient RNA hydrolysis. (15)N-(1)H relaxation measurements interpreted with the standard and our extended Lipari-Szabo formalism reveal rigid regions and identify more dynamic loop regions. Some of the most dynamic areas are key for binding to the cytoplasmic RNase inhibitor. This finding and the important differences observed between the structure in solution and that bound to the inhibitor are indications that RNase 1 to inhibitor binding can be better described by the "induced fit" model rather than the rigid "lock-into-key" mechanism. Translational diffusion measurements reveal that RNase 1 is predominantly dimeric above 1 mM concentration; the possible implications of this dimeric state for the remarkable biological properties of RNase 1 are discussed.

Allostery: absence of a change in shape does not imply that allostery is not at play

Allostery is essential for controlled catalysis, signal transmission, receptor trafficking, turning genes on and off, and apoptosis. It governs the organism's response to environmental and metabolic cues, dictating transient partner interactions in the cellular network. Textbooks taught us that allostery is a change of shape at one site on the protein surface brought about by ligand binding to another. For several years, it has been broadly accepted that the change of shape is not induced; rather, it is observed simply because a larger protein population presents it. Current data indicate that while side chains can reorient and rewire, allostery may not even involve a change of (backbone) shape. Assuming that the enthalpy change does not reverse the free-energy change due to the change in entropy, entropy is mainly responsible for binding.

Compensatory and long-range changes in picosecond-nanosecond main-chain dynamics upon complex formation: 15N relaxation analysis of the free and bound states of the ubiquitin-like domain of human plexin-B1 and the small GTPase Rac1

The formation of a complex between Rac1 and the cytoplasmic domain of plexin-B1 is one of the first documented cases of a direct interaction between a small guanosine 5'-triphosphatase (GTPase) and a transmembrane receptor. Structural studies have begun to elucidate the role of this interaction for the signal transduction mechanism of plexins. Mapping of the Rac1 GTPase surface that contacts the Rho GTPase binding domain of plexin-B1 by solution NMR spectroscopy confirms the plexin domain as a GTPase effector protein. Regions neighboring the GTPase switch I and II regions are also involved in the interaction and there is considerable interest to examine the changes in protein dynamics that take place upon complex formation. Here we present main-chain nitrogen-15 relaxation measurements for the unbound proteins as well as for the Rho GTPase binding domain and Rac1 proteins in their complexed state. Derived order parameters, S2, show that considerable motions are maintained in the bound state of plexin. In fact, some of the changes in S2 on binding appear compensatory, exhibiting decreased as well as increased dynamics. Fluctuations in Rac1, already a largely rigid protein on the picosecond-nanosecond timescale, are overall diminished, but isomerization dynamics in the switch I and II regions of the GTPase are retained in the complex and appear to be propagated to the bound plexin domain. Remarkably, fluctuations in the GTPase are attenuated at sites, including helices alpha6 (the Rho-specific insert helix), alpha7 and alpha8, that are spatially distant from the interaction region with plexin. This effect of binding on long-range dynamics appears to be communicated by hinge sites and by subtle conformational changes in the protein. Similar to recent studies on other systems, we suggest that dynamical protein features are affected by allosteric mechanisms. Altered protein fluctuations are likely to prime the Rho GTPase-plexin complex for interactions with additional binding partners.

Dynamics of essential collective motions in proteins: theory

A general theoretical background is introduced for characterization of conformational motions in protein molecules, and for building reduced coarse-grained models of proteins, based on the statistical analysis of their phase trajectories. Using the projection operator technique, a system of coupled generalized Langevin equations is derived for essential collective coordinates, which are generated by principal component analysis of molecular dynamic trajectories. The number of essential degrees of freedom is not limited in the theory. An explicit analytic relation is established between the generalized Langevin equation for essential collective coordinates and that for the all-atom phase trajectory projected onto the subspace of essential collective degrees of freedom. The theory introduced is applied to identify correlated dynamic domains in a macromolecule and to construct coarse-grained models representing the conformational motions in a protein through a few interacting domains embedded in a dissipative medium. A rigorous theoretical background is provided for identification of dynamic correlated domains in a macromolecule. Examples of domain identification in protein G are given and employed to interpret NMR experiments. Challenges and potential outcomes of the theory are discussed.