On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method

Principles of protein structural ensemble determination

The biological functions of protein molecules are intimately dependent on their conformational dynamics. This aspect is particularly evident for disordered proteins, which constitute perhaps one-third of the human proteome. Therefore, structural ensembles often offer more useful representations of proteins than individual conformations. Here, we describe how the well-established principles of protein structure determination should be extended to the case of protein structural ensembles determination. These principles concern primarily how to deal with conformationally heterogeneous states, and with experimental measurements that are averaged over such states and affected by a variety of errors. We first review the growing literature of recent methods that combine experimental and computational information to model structural ensembles, highlighting their similarities and differences. We then address some conceptual problems in the determination of structural ensembles and define future goals towards the establishment of objective criteria for the comparison, validation, visualization and dissemination of such ensembles.

Deriving Structural Information from Experimentally Measured Data on Biomolecules

During the past half century, the number and accuracy of experimental techniques that can deliver values of observables for biomolecular systems have been steadily increasing. The conversion of a measured value Q^exp of an observable quantity Q into structural information is, however, a task beset with theoretical and practical problems: 1) insufficient or inaccurate values of Q^exp , 2) inaccuracies in the function Q(r→) used to relate the quantity Q to structure r→ , 3) how to account for the averaging inherent in the measurement of Q^exp , 4) how to handle the possible multiple-valuedness of the inverse r→(Q) of the function Q(r→) , to mention a few. These apply to a variety of observable quantities Q and measurement techniques such as X-ray and neutron diffraction, small-angle and wide-angle X-ray scattering, free-electron laser imaging, cryo-electron microscopy, nuclear magnetic resonance, electron paramagnetic resonance, infrared and Raman spectroscopy, circular dichroism, Förster resonance energy transfer, atomic force microscopy and ion-mobility mass spectrometry. The process of deriving structural information from measured data is reviewed with an eye to non-experts and newcomers in the field using examples from the literature of the effect of the various choices and approximations involved in the process. A list of choices to be avoided is provided.

Bayesian ensemble refinement by replica simulations and reweighting

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Investigating the Role of Large-Scale Domain Dynamics in Protein-Protein Interactions

Intrinsically disordered linkers provide multi-domain proteins with degrees of conformational freedom that are often essential for function. These highly dynamic assemblies represent a significant fraction of all proteomes, and deciphering the physical basis of their interactions represents a considerable challenge. Here we describe the difficulties associated with mapping the large-scale domain dynamics and describe two recent examples where solution state methods, in particular NMR spectroscopy, are used to investigate conformational exchange on very different timescales.

Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics

Accurate and precise structural ensembles of proteins and macromolecular complexes can be obtained with metainference, a recently proposed Bayesian inference method that integrates experimental information with prior knowledge and deals with all sources of errors in the data as well as with sample heterogeneity. The study of complex macromolecular systems, however, requires an extensive conformational sampling, which represents a separate challenge. To address such challenge and to exhaustively and efficiently generate structural ensembles we combine metainference with metadynamics and illustrate its application to the calculation of the free energy landscape of the alanine dipeptide.

Mapping transiently formed and sparsely populated conformations on a complex energy landscape

Determining the structures, kinetics, thermodynamics and mechanisms that underlie conformational exchange processes in proteins remains extremely difficult. Only in favourable cases is it possible to provide atomic-level descriptions of sparsely populated and transiently formed alternative conformations. Here we benchmark the ability of enhanced-sampling molecular dynamics simulations to determine the free energy landscape of the L99A cavity mutant of T4 lysozyme. We find that the simulations capture key properties previously measured by NMR relaxation dispersion methods including the structure of a minor conformation, the kinetics and thermodynamics of conformational exchange, and the effect of mutations. We discover a new tunnel that involves the transient exposure towards the solvent of an internal cavity, and show it to be relevant for ligand escape. Together, our results provide a comprehensive view of the structural landscape of a protein, and point forward to studies of conformational exchange in systems that are less characterized experimentally.

DOI:http://dx.doi.org/10.7554/eLife.17505.001

Proteins are the workhorses of cells, where they perform a wide range of roles. To do so, they adopt specific three-dimensional structures that enable them to interact with other molecules as necessary. Often a protein needs to be able to shift between different states with distinct structures as it goes about its job. To fully understand how a protein works, it is important to be able to characterize these different structures and how the protein changes between them.

Many of the experimental techniques used to study protein structure rely on isolating the individual structural forms of a protein. Since many structures only exist briefly, this can be very difficult. To complement experimental results, computer simulations allow researchers to model how atoms behave within a molecule. However, a number of factors limit how well these models represent what happens experimentally, such as the accuracy of the physical description used for the modeling.

Wang et al. set out to test and benchmark how well computer simulations could model changes in structure for a protein called T4 lysozyme, which has been studied extensively using experimental techniques. T4 lysozyme exists in two different states that have distinct structures. By comparing existing detailed experimental measurements with the results of their simulations, Wang et al. found that the simulations could capture key aspects of how T4 lysozyme changes its shape.

The simulations described the structure of the protein in both states and accurately determined the relative proportion of molecules that are found in each state. They could also determine how long it takes for a molecule to change its shape from one state to the other. The findings allowed Wang et al. to describe in fine detail – down to the level of individual atoms – how the protein changes its shape and how mutations in the protein affect its ability to do so. A key question for future studies is whether these insights can be extended to other proteins that are less well characterized experimentally than T4 lysozyme.

DOI:http://dx.doi.org/10.7554/eLife.17505.002

Finding Our Way in the Dark Proteome

The traditional structure-function paradigm has provided significant insights for well-folded proteins in which structures can be easily and rapidly revealed by X-ray crystallography beamlines. However, approximately one-third of the human proteome is comprised of intrinsically disordered proteins and regions (IDPs/IDRs) that do not adopt a dominant well-folded structure, and therefore remain "unseen" by traditional structural biology methods. This Perspective considers the challenges raised by the "Dark Proteome", in which determining the diverse conformational substates of IDPs in their free states, in encounter complexes of bound states, and in complexes retaining significant disorder requires an unprecedented level of integration of multiple and complementary solution-based experiments that are analyzed with state-of-the art molecular simulation, Bayesian probabilistic models, and high-throughput computation. We envision how these diverse experimental and computational tools can work together through formation of a "computational beamline" that will allow key functional features to be identified in IDP structural ensembles.

The Dynamic Basis for Signal Propagation in Human Pin1-WW

Allostery is the structural manifestation of information transduction in biomolecules. Its hallmark is conformational change induced by perturbations at a distal site. An increasing body of evidence demonstrates the presence of allostery in very flexible and even disordered proteins, encouraging a thermodynamic description of this phenomenon. Still, resolving such processes at atomic resolution is difficult. Here we establish a protocol to determine atomistic thermodynamic models of such systems using high-resolution solution state nuclear magnetic resonance data and extensive molecular simulations. Using this methodology, we study information transduction in the WW domain of a key cell-cycle regulator Pin1. Pin1 binds promiscuously to phospho-Ser/Thr-Pro motifs, however, disparate structural and dynamic responses have been reported upon binding different ligands. Our model consists of two topologically distinct states whose relative population may be specifically skewed by an incoming ligand. This model provides a canonical basis for the understanding of multi-functionality in Pin1.

Towards a structural biology of the hydrophobic effect in protein folding

The hydrophobic effect is a major driving force in protein folding. A complete understanding of this effect requires the description of the conformational states of water and protein molecules at different temperatures. Towards this goal, we characterise the cold and hot denatured states of a protein by modelling NMR chemical shifts using restrained molecular dynamics simulations. A detailed analysis of the resulting structures reveals that water molecules in the bulk and at the protein interface form on average the same number of hydrogen bonds. Thus, even if proteins are 'large' particles (in terms of the hydrophobic effect, i.e. larger than 1 nm), because of the presence of complex surface patterns of polar and non-polar residues their behaviour can be compared to that of 'small' particles (i.e. smaller than 1 nm). We thus find that the hot denatured state is more compact and richer in secondary structure than the cold denatured state, since water at lower temperatures can form more hydrogen bonds than at high temperatures. Then, using Φ-value analysis we show that the structural differences between the hot and cold denatured states result in two alternative folding mechanisms. These findings thus illustrate how the analysis of water-protein hydrogen bonds can reveal the molecular origins of protein behaviours associated with the hydrophobic effect.

Structure of a low-population binding intermediate in protein-RNA recognition

The interaction of the HIV-1 protein transactivator of transcription (Tat) and its cognate transactivation response element (TAR) RNA transactivates viral transcription and represents a paradigm for the widespread occurrence of conformational rearrangements in protein-RNA recognition. Although the structures of free and bound forms of TAR are well characterized, the conformations of the intermediates in the binding process are still unknown. By determining the free energy landscape of the complex using NMR residual dipolar couplings in replica-averaged metadynamics simulations, we observe two low-population intermediates. We then rationally design two mutants, one in the protein and another in the RNA, that weaken specific nonnative interactions that stabilize one of the intermediates. By using surface plasmon resonance, we show that these mutations lower the release rate of Tat, as predicted. These results identify the structure of an intermediate for RNA-protein binding and illustrate a general strategy to achieve this goal with high resolution.

Experimental Inferential Structure Determination of Ensembles for Intrinsically Disordered Proteins

We develop a Bayesian approach to determine the most probable structural ensemble model from candidate structures for intrinsically disordered proteins (IDPs) that takes full advantage of NMR chemical shifts and J-coupling data, their known errors and variances, and the quality of the theoretical back-calculation from structure to experimental observables. Our approach differs from previous formulations in the optimization of experimental and back-calculation nuisance parameters that are treated as random variables with known distributions, as opposed to structural or ensemble weight optimization or use of a reference ensemble. The resulting experimental inferential structure determination (EISD) method is size extensive with O(N) scaling, with N = number of structures, that allows for the rapid ranking of large ensemble data comprising tens of thousands of conformations. We apply the EISD approach on singular folded proteins and a corresponding set of ∼25 000 misfolded states to illustrate the problems that can arise using Boltzmann weighted priors. We then apply the EISD method to rank IDP ensembles most consistent with the NMR data and show that the primary error for ranking or creating good IDP ensembles resides in the poor back-calculation from structure to simulated experimental observable. We show that a reduction by a factor of 3 in the uncertainty of the back-calculation error can improve the discrimination among qualitatively different IDP ensembles for the amyloid-beta peptide.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Ensemble-Biased Metadynamics: A Molecular Simulation Method to Sample Experimental Distributions

We introduce an enhanced-sampling method for molecular dynamics (MD) simulations referred to as ensemble-biased metadynamics (EBMetaD). The method biases a conventional MD simulation to sample a molecular ensemble that is consistent with one or more probability distributions known a priori, e.g., experimental intramolecular distance distributions obtained by double electron-electron resonance or other spectroscopic techniques. To this end, EBMetaD adds an adaptive biasing potential throughout the simulation that discourages sampling of configurations inconsistent with the target probability distributions. The bias introduced is the minimum necessary to fulfill the target distributions, i.e., EBMetaD satisfies the maximum-entropy principle. Unlike other methods, EBMetaD does not require multiple simulation replicas or the introduction of Lagrange multipliers, and is therefore computationally efficient and straightforward in practice. We demonstrate the performance and accuracy of the method for a model system as well as for spin-labeled T4 lysozyme in explicit water, and show how EBMetaD reproduces three double electron-electron resonance distance distributions concurrently within a few tens of nanoseconds of simulation time. EBMetaD is integrated in the open-source PLUMED plug-in (www.plumed-code.org), and can be therefore readily used with multiple MD engines.

Accurate Determination of Conformational Transitions in Oligomeric Membrane Proteins

The structural dynamics governing collective motions in oligomeric membrane proteins play key roles in vital biomolecular processes at cellular membranes. In this study, we present a structural refinement approach that combines solid-state NMR experiments and molecular simulations to accurately describe concerted conformational transitions identifying the overall structural, dynamical, and topological states of oligomeric membrane proteins. The accuracy of the structural ensembles generated with this method is shown to reach the statistical error limit, and is further demonstrated by correctly reproducing orthogonal NMR data. We demonstrate the accuracy of this approach by characterising the pentameric state of phospholamban, a key player in the regulation of calcium uptake in the sarcoplasmic reticulum, and by probing its dynamical activation upon phosphorylation. Our results underline the importance of using an ensemble approach to characterise the conformational transitions that are often responsible for the biological function of oligomeric membrane protein states.

Metainference: A Bayesian inference method for heterogeneous systems

Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called "metainference," that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors.

Structural Insights into the Calcium-Mediated Allosteric Transition in the C-Terminal Domain of Calmodulin from Nuclear Magnetic Resonance Measurements

Calmodulin is a two-domain signaling protein that becomes activated upon binding cooperatively two pairs of calcium ions, leading to large-scale conformational changes that expose its binding site. Despite significant advances in understanding the structural biology of calmodulin functions, the mechanistic details of the conformational transition between closed and open states have remained unclear. To investigate this transition, we used a combination of molecular dynamics simulations and nuclear magnetic resonance (NMR) experiments on the Ca(2+)-saturated E140Q C-terminal domain variant. Using chemical shift restraints in replica-averaged metadynamics simulations, we obtained a high-resolution structural ensemble consisting of two conformational states and validated such an ensemble against three independent experimental data sets, namely, interproton nuclear Overhauser enhancements, (15)N order parameters, and chemical shift differences between the exchanging states. Through a detailed analysis of this structural ensemble and of the corresponding statistical weights, we characterized a calcium-mediated conformational transition whereby the coordination of Ca(2+) by just one oxygen of the bidentate ligand E140 triggers a concerted movement of the two EF-hands that exposes the target binding site. This analysis provides atomistic insights into a possible Ca(2+)-mediated activation mechanism of calmodulin that cannot be achieved from static structures alone or from ensemble NMR measurements of the transition between conformations.

Investigating protein conformational energy landscapes and atomic resolution dynamics from NMR dipolar couplings: a review

Nuclear magnetic resonance spectroscopy is exquisitely sensitive to protein dynamics. In particular inter-nuclear dipolar couplings, that become measurable in solution when the protein is dissolved in a dilute liquid crystalline solution, report on all conformations sampled up to millisecond timescales. As such they provide the opportunity to describe the Boltzmann distribution present in solution at atomic resolution, and thereby to map the conformational energy landscape in unprecedented detail. The development of analytical methods and approaches based on numerical simulation and their application to numerous biologically important systems is presented.

ENCORE: Software for Quantitative Ensemble Comparison

There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or ensemble refinement approaches have provided conformational ensembles that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural ensembles in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational ensembles generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for ensemble comparison, that each can be used to quantify the similarity between conformational ensembles by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing ensembles generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural ensembles refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the ensembles. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads ensemble data in many common formats, and can work with large trajectory files.

Molecular Dynamics of Biomolecules through Direct Analysis of Dipolar Couplings

Residual dipolar couplings (RDCs) are important probes in structural biology, but their analysis is often complicated by the determination of an alignment tensor or its associated assumptions. We here apply the maximum entropy principle to derive a tensor-free formalism which allows for direct, dynamic analysis of RDCs and holds the classic tensor formalism as a special case. Specifically, the framework enables us to robustly analyze data regardless of whether a clear separation of internal and overall dynamics is possible. Such a separation is often difficult in the core subjects of current structural biology, which include multidomain and intrinsically disordered proteins as well as nucleic acids. We demonstrate the method is tractable and self-consistent and generalizes to data sets comprised of observations from multiple different alignment conditions.

Topology, structures, and energy landscapes of human chromosomes

Chromosome conformation capture experiments provide a rich set of data concerning the spatial organization of the genome. We use these data along with a maximum entropy approach to derive a least-biased effective energy landscape for the chromosome. Simulations of the ensemble of chromosome conformations based on the resulting information theoretic landscape not only accurately reproduce experimental contact probabilities, but also provide a picture of chromosome dynamics and topology. The topology of the simulated chromosomes is probed by computing the distribution of their knot invariants. The simulated chromosome structures are largely free of knots. Topologically associating domains are shown to be crucial for establishing these knotless structures. The simulated chromosome conformations exhibit a tendency to form fibril-like structures like those observed via light microscopy. The topologically associating domains of the interphase chromosome exhibit multistability with varying liquid crystalline ordering that may allow discrete unfolding events and the landscape is locally funneled toward "ideal" chromosome structures that represent hierarchical fibrils of fibrils.

Structure and dynamics of the integrin LFA-1 I-domain in the inactive state underlie its inside-out/outside-in signaling and allosteric mechanisms

Lymphocyte function-associated antigen 1 (LFA-1) is an integrin that transmits information in two directions across the plasma membrane of leukocytes, in so-called outside-in and inside-out signaling mechanisms. To investigate the structural basis of these mechanisms, we studied the conformational space of the apo I-domain using replica-averaged metadynamics simulations in combination with nuclear magnetic resonance chemical shifts. We thus obtained a free energy landscape that reveals the existence of three conformational substates of this domain. The three substates include conformations similar to existing crystallographic structures of the low-affinity I-domain, the inactive I-domain with an allosteric antagonist inhibitor bound underneath α helix 7, and an intermediate affinity state of the I-domain. The multiple substates were validated with residual dipolar coupling measurements. These results suggest that the presence of three substates in the apo I-domain enables the precise regulation of the binding process that is essential for the physiological function of LFA-1.

Simulating the distance distribution between spin-labels attached to proteins

EPR/DEER spectroscopy is playing an increasingly important role in the characterization of the conformational states of proteins. In this study, force field parameters for the bifunctional spin-label (RX) used in EPR/DEER are parametrized and tested with molecular dynamics (MD) simulations. The dihedral angles connecting the C_α atom of the backbone to the nitroxide ring moiety of the RX spin-label attached to i and i + 4 positions in a polyalanine α-helix agree very well with those observed in the X-ray crystallography. Both RX_i,i+4 and RX_i,i+3 are more rigid than the monofunctional spin-label (R1) commonly used in EPR/DEER, while RX_i,i+4 is more rigid and causes less distortion in a protein backbone than RX_i,i+3. Simplified dummy spin-label models with a single effective particle representing the RX_i,i+3 and RX_i,i+4 are also developed and parametrized from the all-atom simulations. MD simulations with dummy spin-labels (MDDS) provide distance distributions that can be directly compared to distance distributions obtained from EPR/DEER to rapidly assess if a hypothetical three-dimensional (3D) structural model is consistent with experiment. The dummy spin-labels can also be used in the restrained-ensemble MD (re-MD) simulations to carry out structural refinement of 3D models. Applications of this methodology to T4 lysozyme, KCNE1, and LeuT are shown to provide important insights about their conformational dynamics.

Structural dynamics and conformational equilibria of SERCA regulatory proteins in membranes by solid-state NMR restrained simulations

Solid-state NMR spectroscopy is emerging as a powerful approach to determine structure, topology, and conformational dynamics of membrane proteins at the atomic level. Conformational dynamics are often inferred and quantified from the motional averaging of the NMR parameters. However, the nature of these motions is difficult to envision based only on spectroscopic data. Here, we utilized restrained molecular dynamics simulations to probe the structural dynamics, topology and conformational transitions of regulatory membrane proteins of the calcium ATPase SERCA, namely sarcolipin and phospholamban, in explicit lipid bilayers. Specifically, we employed oriented solid-state NMR data, such as dipolar couplings and chemical shift anisotropy measured in lipid bicelles, to refine the conformational ensemble of these proteins in lipid membranes. The samplings accurately reproduced the orientations of transmembrane helices and showed a significant degree of convergence with all of the NMR parameters. Unlike the unrestrained simulations, the resulting sarcolipin structures are in agreement with distances and angles for hydrogen bonds in ideal helices. In the case of phospholamban, the restrained ensemble sampled the conformational interconversion between T (helical) and R (unfolded) states for the cytoplasmic region that could not be observed using standard structural refinements with the same experimental data set. This study underscores the importance of implementing NMR data in molecular dynamics protocols to better describe the conformational landscapes of membrane proteins embedded in realistic lipid membranes.

Structure of a low-population intermediate state in the release of an enzyme product

Enzymes can increase the rate of biomolecular reactions by several orders of magnitude. Although the steps of substrate capture and product release are essential in the enzymatic process, complete atomic-level descriptions of these steps are difficult to obtain because of the transient nature of the intermediate conformations, which makes them largely inaccessible to standard structure determination methods. We describe here the determination of the structure of a low-population intermediate in the product release process by human lysozyme through a combination of NMR spectroscopy and molecular dynamics simulations. We validate this structure by rationally designing two mutations, the first engineered to destabilise the intermediate and the second to stabilise it, thus slowing down or speeding up, respectively, product release. These results illustrate how product release by an enzyme can be facilitated by the presence of a metastable intermediate with transient weak interactions between the enzyme and product.

A relationship between the transient structure in the monomeric state and the aggregation propensities of α-synuclein and β-synuclein

α-Synuclein is an intrinsically disordered protein whose aggregation is implicated in Parkinson's disease. A second member of the synuclein family, β-synuclein, shares significant sequence similarity with α-synuclein but is much more resistant to aggregation. β-Synuclein is missing an 11-residue stretch in the central non-β-amyloid component region that forms the core of α-synuclein amyloid fibrils, yet insertion of these residues into β-synuclein to produce the βSHC construct does not markedly increase the aggregation propensity. To investigate the structural basis of these different behaviors, quantitative nuclear magnetic resonance data, in the form of paramagnetic relaxation enhancement-derived interatomic distances, are combined with molecular dynamics simulations to generate ensembles of structures representative of the solution states of α-synuclein, β-synuclein, and βSHC. Comparison of these ensembles reveals that the differing aggregation propensities of α-synuclein and β-synuclein are associated with differences in the degree of residual structure in the C-terminus coupled to the shorter separation between the N- and C-termini in β-synuclein and βSHC, making protective intramolecular contacts more likely.

Time-averaged order parameter restraints in molecular dynamics simulations

A method is described that allows experimental S(2) order parameters to be enforced as a time-averaged quantity in molecular dynamics simulations. The two parameters that characterize time-averaged restraining, the memory relaxation time and the weight of the restraining potential energy term in the potential energy function used in the simulation, are systematically investigated based on two model systems, a vector with one end restrained in space and a pentapeptide. For the latter it is shown that the backbone N-H order parameter of individual residues can be enforced such that the spatial fluctuations of quantities depending on atomic coordinates are not significantly perturbed. The applicability to realistic systems is illustrated for the B3 domain of protein G in aqueous solution.

Molecular dynamics simulation of configurational ensembles compatible with experimental FRET efficiency data through a restraint on instantaneous FRET efficiencies

Förster resonance energy transfer (FRET) measurements are widely used to investigate (bio)molecular interactions or/and association. FRET efficiencies, the primary data obtained from this method, give, in combination with the common assumption of isotropic chromophore orientation, detailed insight into the lengthscale of molecular phenomena. This study illustrates the application of a FRET efficiency restraint during classical atomistic molecular dynamics simulations of a mutant mastoparan X peptide in either water or 7 M aqueous urea. The restraint forces acting on the donor and acceptor chromophores ensure that the sampled peptide configurational ensemble satisfies the experimental primary data by modifying interchromophore separation and chromophore transition dipole moment orientations. By means of a conformational cluster analysis, it is seen that indeed different configurational ensembles may be sampled without and with application of the restraint. In particular, while the FRET efficiency and interchromophore distances monitored in an unrestrained simulation may differ from the experimentally-determined values, they can be brought in agreement with experimental data through usage of the FRET efficiency restraining potential. Furthermore, the present results suggest that the assumption of isotropic chromophore orientation is not always justified. The FRET efficiency restraint allows the generation of configurational ensembles that may not be accessible with unrestrained simulations, and thereby supports a meaningful interpretation of experimental FRET results in terms of the underlying molecular degrees of freedom. Thus, it offers an additional tool to connect the realms of computer and wet-lab experimentation.

A simple and fast approach for predicting (1)H and (13)C chemical shifts: toward chemical shift-guided simulations of RNA

We introduce a simple and fast approach for predicting RNA chemical shifts from interatomic distances that performs with an accuracy similar to existing predictors and enables the first chemical shift-restrained simulations of RNA to be carried out. Our analysis demonstrates that the applied restraints can effectively guide conformational sampling toward regions of space that are more consistent with chemical shifts than the initial coordinates used for the simulations. As such, our approach should be widely applicable in mapping the conformational landscape of RNAs via chemical shift-guided molecular dynamics simulations. The simplicity and demonstrated sensitivity to three-dimensional structure should also allow our method to be used in chemical shift-based RNA structure prediction, validation, and refinement.

Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts

Methods of protein structure determination based on NMR chemical shifts are becoming increasingly common. The most widely used approaches adopt the molecular fragment replacement strategy, in which structural fragments are repeatedly reassembled into different complete conformations in molecular simulations. Although these approaches are effective in generating individual structures consistent with the chemical shift data, they do not enable the sampling of the conformational space of proteins with correct statistical weights. Here, we present a method of molecular fragment replacement that makes it possible to perform equilibrium simulations of proteins, and hence to determine their free energy landscapes. This strategy is based on the encoding of the chemical shift information in a probabilistic model in Markov chain Monte Carlo simulations. First, we demonstrate that with this approach it is possible to fold proteins to their native states starting from extended structures. Second, we show that the method satisfies the detailed balance condition and hence it can be used to carry out an equilibrium sampling from the Boltzmann distribution corresponding to the force field used in the simulations. Third, by comparing the results of simulations carried out with and without chemical shift restraints we describe quantitatively the effects that these restraints have on the free energy landscapes of proteins. Taken together, these results demonstrate that the molecular fragment replacement strategy can be used in combination with chemical shift information to characterize not only the native structures of proteins but also their conformational fluctuations.

Efficient and Minimal Method to Bias Molecular Simulations with Experimental Data

A primary goal in molecular simulations is to modify the potential energy of a system so that properties of the simulation match experimental data. This is traditionally done through iterative cycles of simulation and reparameterization. An alternative approach is to bias the potential energy so that the system matches experimental data. This can be done while minimally changing the underlying free energy of the molecular simulation. Current minimal biasing methods require replicas, which can lead to unphysical dynamics and introduces new complexity: the choice of replica number and their properties. Here, we describe a new method, called experiment directed simulation that does not require replicas, converges rapidly, can match many data simultaneously, and minimally modifies the potential. The experiment directed simulation method is demonstrated on model systems and a three-component electrolyte simulation. The theory used to derive the method also provides insight into how changing a molecular force-field impacts the expected value of observables in simulation.

Cyclophilin A catalyzes proline isomerization by an electrostatic handle mechanism

Proline isomerization is a ubiquitous process that plays a key role in the folding of proteins and in the regulation of their functions. Different families of enzymes, known as "peptidyl-prolyl isomerases" (PPIases), catalyze this reaction, which involves the interconversion between the cis and trans isomers of the N-terminal amide bond of the amino acid proline. However, complete descriptions of the mechanisms by which these enzymes function have remained elusive. We show here that cyclophilin A, one of the most common PPIases, provides a catalytic environment that acts on the substrate through an electrostatic handle mechanism. In this mechanism, the electrostatic field in the catalytic site turns the electric dipole associated with the carbonyl group of the amino acid preceding the proline in the substrate, thus causing the rotation of the peptide bond between the two residues. We identified this mechanism using a combination of NMR measurements, molecular dynamics simulations, and density functional theory calculations to simultaneously determine the cis-bound and trans-bound conformations of cyclophilin A and its substrate as the enzymatic reaction takes place. We anticipate that this approach will be helpful in elucidating whether the electrostatic handle mechanism that we describe here is common to other PPIases and, more generally, in characterizing other enzymatic processes.

Statistical mechanics of the denatured state of a protein using replica-averaged metadynamics

The characterization of denatured states of proteins is challenging because the lack of permanent structure in these states makes it difficult to apply to them standard methods of structural biology. In this work we use all-atom replica-averaged metadynamics (RAM) simulations with NMR chemical shift restraints to determine an ensemble of structures representing an acid-denatured state of the 86-residue protein ACBP. This approach has enabled us to reach convergence in the free energy landscape calculations, obtaining an ensemble of structures in relatively accurate agreement with independent experimental data used for validation. By observing at atomistic resolution the transient formation of native and non-native structures in this acid-denatured state of ACBP, we rationalize the effects of single-point mutations on the folding rate, stability, and transition-state structures of this protein, thus characterizing the role of the unfolded state in determining the folding process.

A tensor-free method for the structural and dynamical refinement of proteins using residual dipolar couplings

Residual dipolar couplings (RDCs) are parameters measured in nuclear magnetic resonance spectroscopy that can provide exquisitely detailed information about the structure and dynamics of biological macromolecules. We describe here a method of using RDCs for the structural and dynamical refinement of proteins that is based on the observation that the RDC between two atomic nuclei depends directly on the angle ϑ between the internuclear vector and the external magnetic field. For every pair of nuclei for which an RDC is available experimentally, we introduce a structural restraint to minimize the deviation from the value of the angle ϑ derived from the measured RDC and that calculated in the refinement protocol. As each restraint involves only the calculation of the angle ϑ of the corresponding internuclear vector, the method does not require the definition of an overall alignment tensor to describe the preferred orientation of the protein with respect to the alignment medium. Application to the case of ubiquitin demonstrates that this method enables an accurate refinement of the structure and dynamics of this protein to be obtained.

Bayesian approach to inverse statistical mechanics

Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.

Transient electrostatic interactions dominate the conformational equilibrium sampled by multidomain splicing factor U2AF65: a combined NMR and SAXS study

Multidomain proteins containing intrinsically disordered linkers exhibit large-scale dynamic modes that play key roles in a multitude of molecular recognition and signaling processes. Here, we determine the conformational space sampled by the multidomain splicing factor U2AF65 using complementary nuclear magnetic resonance spectroscopy and small-angle scattering data. Available degrees of conformational freedom are initially stochastically sampled and experimental data then used to delineate the potential energy landscape in terms of statistical probability. The spatial distribution of U2AF65 conformations is found to be highly anisotropic, comprising significantly populated interdomain contacts that appear to be electrostatic in origin. This hypothesis is supported by the reduction of signature PREs reporting on expected interfaces with increasing salt concentration. The described spatial distribution reveals the complete spectrum of the unbound forms of U2AF65 that coexist with the small percentage of a preformed RNA-bound domain arrangement required for polypyrimidine-tract recognition by conformational selection. More generally, the proposed approach to describing conformational equilibria of multidomain proteins can be further combined with other experimental data that are sensitive to domain dynamics.

New opportunities for tensor-free calculations of residual dipolar couplings for the study of protein dynamics

Residual dipolar couplings (RDCs) can provide exquisitely detailed information about the structure and dynamics of proteins. It is challenging, however, to extract such information from RDC measurements in conformationally heterogeneous states of proteins because of the complex relationship between RDCs and protein structures. To obtain new insights into this problem, we discuss methods of calculating the RDCs that do not require the definition of an alignment tensor. These methods can help in particular in the search of effective ways to use RDCs to characterise disordered or partially disordered states of proteins.

Combining experiments and simulations using the maximum entropy principle

A key component of computational biology is to compare the results of computer modelling with experimental measurements. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons sometimes reveal that the computations are not in quantitative agreement with experimental data. The principle of maximum entropy is a general procedure for constructing probability distributions in the light of new data, making it a natural tool in cases when an initial model provides results that are at odds with experiments. The number of maximum entropy applications in our field has grown steadily in recent years, in areas as diverse as sequence analysis, structural modelling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general procedure is explained in the context of a simple example, after which we proceed with a real-world application in the field of molecular simulations, where the maximum entropy procedure has recently provided new insight. Given the limited accuracy of force fields, macromolecular simulations sometimes produce results that are at not in complete and quantitative accordance with experiments. A common solution to this problem is to explicitly ensure agreement between the two by perturbing the potential energy function towards the experimental data. So far, a general consensus for how such perturbations should be implemented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical insights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges.

Determination of the individual roles of the linker residues in the interdomain motions of calmodulin using NMR chemical shifts

Many protein molecules are formed by two or more domains whose structures and dynamics are closely related to their biological functions. It is thus important to develop methods to determine the structural properties of these multidomain proteins. Here, we characterize the interdomain motions in the calcium-bound state of calmodulin (Ca(2+)-CaM) using NMR chemical shifts as replica-averaged structural restraints in molecular dynamics simulations. We find that the conformational fluctuations of the interdomain linker, which are largely responsible for the overall interdomain motions of CaM, can be well described by exploiting the information provided by chemical shifts. We thus identify 10 residues in the interdomain linker region that change their conformations upon substrate binding. Five of these residues (Met76, Lys77, Thr79, Asp80 and Ser81) are highly flexible and cover the range of conformations observed in the substrate-bound state, while the remaining five (Arg74, Lys75, Asp78, Glu82 and Glu83) are much more rigid and do not populate conformations typical of the substrate-bound form. The ensemble of conformations representing the Ca(2+)-CaM state obtained in this study is in good agreement with residual dipolar coupling, paramagnetic resonance enhancement, small-angle X-ray scattering and fluorescence resonance energy transfer measurements, which were not used as restraints in the calculations. These results provide initial evidence that chemical shifts can be used to characterize the conformational fluctuations of multidomain proteins.