X-ray solution scattering studies of the structural diversity intrinsic to protein ensembles

It is becoming increasingly clear that characterization of the protein ensemble-the collection of all conformations of which the protein is capable-will be a critical step in developing a full understanding of the linkage between structure, dynamics, and function. X-ray solution scattering in the small angle (SAXS) and wide-angle (WAXS) regimes represents an important new window to exploring the behavior of ensembles. The characteristics of the ensemble express themselves in X-ray solution scattering data in predictable ways. Here we present an overview of the effect that structural diversity intrinsic to protein ensembles has on scattering data. We then demonstrate the observation of these effects in scattering from four molecular systems; myoglobin; ubiquitin; alcohol dehydrogenase; and HIV protease; and demonstrate the modulation of these ensembles by ligand binding, mutation, and environmental factors. The observations are analyzed quantitatively in terms of the average spatial extent of structural fluctuations occurring within these proteins under different experimental conditions. The insights which these analyses support are discussed in terms of the function of the various proteins.     

Computer-based screening of functional conformers of proteins

A long-standing goal in biology is to establish the link between function, structure, and dynamics of proteins. Considering that protein function at the molecular level is understood by the ability of proteins to bind to other molecules, the limited structural data of proteins in association with other bio-molecules represents a major hurdle to understanding protein function at the structural level. Recent reports show that protein function can be linked to protein structure and dynamics through network centrality analysis, suggesting that the structures of proteins bound to natural ligands may be inferred computationally. In the present work, a new method is described to discriminate protein conformations relevant to the specific recognition of a ligand. The method relies on a scoring system that matches critical residues with central residues in different structures of a given protein. Central residues are the most traversed residues with the same frequency in networks derived from protein structures. We tested our method in a set of 24 different proteins and more than 260,000 structures of these in the absence of a ligand or bound to it. To illustrate the usefulness of our method in the study of the structure/dynamics/function relationship of proteins, we analyzed mutants of the yeast TATA-binding protein with impaired DNA binding. Our results indicate that critical residues for an interaction are preferentially found as central residues of protein structures in complex with a ligand. Thus, our scoring system effectively distinguishes protein conformations relevant to the function of interest.     

Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles

Hydrogen-deuterium exchange combined with mass spectrometry (HDX-MS) is a widely applied biophysical technique that probes the structure and dynamics of biomolecules without the need for site-directed modifications or bio-orthogonal labels. The mechanistic interpretation of HDX data, however, is often qualitative and subjective, owing to a lack of quantitative methods to rigorously translate observed deuteration levels into atomistic structural information. To help address this problem, we have developed a methodology to generate structural ensembles that faithfully reproduce HDX-MS measurements. In this approach, an ensemble of protein conformations is first generated, typically using molecular dynamics simulations. A maximum-entropy bias is then applied post hoc to the resulting ensemble such that averaged peptide-deuteration levels, as predicted by an empirical model, agree with target values within a given level of uncertainty. We evaluate this approach, referred to as HDX ensemble reweighting (HDXer), for artificial target data reflecting the two major conformational states of a binding protein. We demonstrate that the information provided by HDX-MS experiments and by the model of exchange are sufficient to recover correctly weighted structural ensembles from simulations, even when the relevant conformations are rarely observed. Degrading the information content of the target data—e.g., by reducing sequence coverage, by averaging exchange levels over longer peptide segments, or by incorporating different sources of uncertainty—reduces the structural accuracy of the reweighted ensemble but still allows for useful insights into the distinctive structural features reflected by the target data. Finally, we describe a quantitative metric to rank candidate structural ensembles according to their correspondence with target data and illustrate the use of HDXer to describe changes in the conformational ensemble of the membrane protein LeuT. In summary, HDXer is designed to facilitate objective structural interpretations of HDX-MS data and to inform experimental approaches and further developments of theoretical exchange models.     

Investigating energy‐based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1

The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure‐based and physics‐based atomistic force field with an efficient sampling strategy is adopted to simulate a model di‐domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape. Subsequently, a conformational ensemble recovered with low‐energy structures and the minimum‐size restraint is identified in good agreement with experimental PRE rates, and the result is also supported by chemical shift perturbations and small‐angle X‐ray scattering data. It is illustrated that the regularizations of energy and ensemble‐size prevent an arbitrary interpretation of protein conformations. Moreover, energy is found to serve as a critical control to refine the structure pool and prevent data overfitting, because the absence of energy regularization exposes ensemble construction to the noise from high‐energy structures and causes a more ambiguous representation of protein conformations. Finally, we perform structure‐ensemble optimizations with a topology‐based structure pool, to enhance the understanding on the ensemble results from different sources of pool candidates.     

Enhancing the quality of protein conformation ensembles with relative populations

The function and dynamics of many proteins are best understood not from a single structure but from an ensemble. A high quality ensemble is necessary for accurately delineating protein dynamics. However, conformations in an ensemble are generally given equal weights. Few attempts were made to assign relative populations to the conformations, mainly due to the lack of right experimental data. Here we propose a method for assigning relative populations to ensembles using experimental residue dipolar couplings (RDC) as constraints, and show that relative populations can significantly enhance an ensemble’s ability in representing the native states and dynamics. The method works by identifying conformation states within an ensemble and assigning appropriate relative populations to them. Each of these conformation states is represented by a sub-ensemble consisting of a subset of the conformations. Application to the ubiquitin X-ray ensemble clearly identifies two key conformation states, with relative populations in excellent agreement with previous work. We then apply the method to a reprotonated ERNST ensemble that is enhanced with a switched conformation, and show that as a result of population reweighting, not only the reproduction of RDCs is significantly improved, but common conformational features (particularly the dihedral angle distributions of ? ₅₃ and ψ ₅₂) also emerge for both the X-ray ensemble and the reprotonated ERNST ensemble.     

Conformational equilibria and free energy profiles for the allosteric transition of the ribose-binding protein

The ribose-binding protein (RBP) is a sugar-binding bacterial periplasmic protein whose function is associated with a large allosteric conformational change from an open to a closed conformation upon binding to ribose. The crystal structures of RBP in open and closed conformations have been solved. It has been hypothesized that the open and closed conformations exist in a dynamic equilibrium in solution, and that sugar binding shifts the population from open conformations to closed conformations. Here, we study by computer simulations the thermodynamic changes that accompany this conformational change, and model the structural changes that accompany the allosteric transition, using umbrella sampling molecular dynamics and the weighted histogram analysis method. The open state is comprised of a diverse ensemble of conformations; the open ribose-free X-ray crystal conformations being representative of this ensemble. The unligated open form of RBP is stabilized by conformational entropy. The simulations predict detectable populations of closed ribose-free conformations in solution. Additional interdomain hydrogen bonds stabilize this state. The predicted shift in equilibrium from the open to the closed state on binding to ribose is in agreement with experiments. This is driven by the energetic stabilization of the closed conformation due to ribose-protein interactions. We also observe a significant population of a hitherto unobserved ribose-bound partially open state. We believe that this state is the one that has been suggested to play a role in the transfer of ribose to the membrane-bound permease complex.     

Dynamics of backbone conformational heterogeneity in Bacillus subtilis ribonuclease P protein

Interconversion of protein conformations is imperative to function, as evidenced by conformational changes associated with enzyme catalytic cycles, ligand binding and post-translational modifications. In this study, we used 15N NMR relaxation experiments to probe the fast (i.e., ps-ns) and slow (i.e., micros-ms) conformational dynamics of Bacillus subtilis ribonuclease P protein (P protein) in its folded state, bound to two sulfate anions. Using the Lipari-Szabo mapping method [Andrec, M., Montelione, G. T., and Levy, R. M. (2000) J. Biomol. NMR 18, 83-100] to interpret the data, we find evidence for P protein dynamics on the mus-ms time scale in the ensemble. The residues that exhibit these slow internal motions are found in regions that have been previously identified as part of the P protein-P RNA interface. These results suggest that structural flexibility within the P protein ensemble may be important for proper RNase P holoenzyme assembly and/or catalysis.     

Refinement of Peptide Conformational Ensembles by 2D IR Spectroscopy: Application to Ala‒Ala‒Ala

Characterizing ensembles of intrinsically disordered proteins is experimentally challenging because of the ill-conditioned nature of ensemble determination with limited data and the intrinsic fast dynamics of the conformational ensemble. Amide I two-dimensional infrared (2D IR) spectroscopy has picosecond time resolution to freeze structural ensembles as needed for probing disordered-protein ensembles and conformational dynamics. Also, developments in amide I computational spectroscopy now allow a quantitative and direct prediction of amide I spectra based on conformational distributions drawn from molecular dynamics simulations, providing a route to ensemble refinement against experimental spectra. We performed a Bayesian ensemble refinement method on Ala–Ala–Ala against isotope-edited Fourier-transform infrared spectroscopy and 2D IR spectroscopy and tested potential factors affecting the quality of ensemble refinements. We found that isotope-edited 2D IR spectroscopy provides a stringent constraint on Ala–Ala–Ala conformations and returns consistent conformational ensembles with the dominant ppII conformer across varying prior distributions from many molecular dynamics force fields and water models. The dominant factor influencing ensemble refinements is the systematic frequency uncertainty from spectroscopic maps. However, the uncertainty of conformer populations can be significantly reduced by incorporating 2D IR spectra in addition to traditional Fourier-transform infrared spectra. Bayesian ensemble refinement against isotope-edited 2D IR spectroscopy thus provides a route to probe equilibrium-complex protein ensembles and potentially nonequilibrium conformational dynamics.     

Similarity Measures for Protein Ensembles

Analyses of similarities and changes in protein conformation can provide important information regarding protein function and evolution. Many scores, including the commonly used root mean square deviation, have therefore been developed to quantify the similarities of different protein conformations. However, instead of examining individual conformations it is in many cases more relevant to analyse ensembles of conformations that have been obtained either through experiments or from methods such as molecular dynamics simulations. We here present three approaches that can be used to compare conformational ensembles in the same way as the root mean square deviation is used to compare individual pairs of structures. The methods are based on the estimation of the probability distributions underlying the ensembles and subsequent comparison of these distributions. We first validate the methods using a synthetic example from molecular dynamics simulations. We then apply the algorithms to revisit the problem of ensemble averaging during structure determination of proteins, and find that an ensemble refinement method is able to recover the correct distribution of conformations better than standard single-molecule refinement.     

The MUMO (minimal under-restraining minimal over-restraining) method for the determination of native state ensembles of proteins

While reliable procedures for determining the conformations of proteins are available, methods for generating ensembles of structures that also reflect their flexibility are much less well established. Here we present a systematic assessment of the ability of ensemble-averaged molecular dynamics simulations with ensemble-averaged NMR restraints to simultaneously reproduce the average structure of proteins and their associated dynamics. We discuss the effects that under-restraining (overfitting) and over-restraining (underfitting) have on the structures generated in ensemble-averaged molecular simulations. We then introduce the MUMO (minimal under-restraining minimal over-restraining) method, a procedure in which different observables are averaged over a different number of molecules. As both over-restraining and under-restraining are significantly reduced in the MUMO method, it is possible to generate ensembles of conformations that accurately characterize both the structure and the dynamics of native states of proteins. The application of the MUMO method to the protein ubiquitin yields a high-resolution structural ensemble with an RDC Q-factor of 0.19.     

Concerted motions in HIV-1 TAR RNA may allow access to bound state conformations: RNA dynamics from NMR residual dipolar couplings.

Ground-state dynamics in RNA is a critical precursor for structural adaptation observed ubiquitously in protein-RNA recognition. A tertiary conformational analysis of the stem-loop structural element in the transactivation response element (TAR) from human immunodeficiency virus type 1 (HIV-I) RNA is presented using recently introduced NMR methods that rely on the measurement of residual dipolar couplings (RDC) in partially oriented systems. Order matrix analysis of RDC data provides evidence for inter-helical motions that are of amplitude 46(+/-4) degrees, of random directional character, and that are executed about an average conformation with an inter-helical angle between 44 degrees and 54 degrees. The generated ensemble of TAR conformations have different organizations of functional groups responsible for interaction with the trans-activator protein Tat, including conformations similar to the previously characterized bound-state conformation. These results demonstrate the utility of RDC-NMR for simultaneously characterizing RNA tertiary dynamics and average conformation, and indicate an avenue for TAR complex formation involving tertiary structure capture.     

Statistical characterization of protein ensembles

When accounting for structural fluctuations or measurement errors, a single rigid structure may not be sufficient to represent a protein. One approach to solve this problem is to represent the possible conformations as a discrete set of observed conformations, an ensemble. In this work, we follow a different richer approach, and introduce a framework for estimating probability density functions in very high dimensions, and then apply it to represent ensembles of folded proteins. This proposed approach combines techniques such as kernel density estimation, maximum likelihood, cross-validation, and bootstrapping. We present the underlying theoretical and computational framework and apply it to artificial data and protein ensembles obtained from molecular dynamics simulations. We compare the results with those obtained experimentally, illustrating the potential and advantages of this representation.     

Fast de novo discovery of low‐energy protein loop conformations

In the prediction of protein structure from amino acid sequence, loops are challenging regions for computational methods. Since loops are often located on the protein surface, they can have significant roles in determining protein functions and binding properties. Loop prediction without the aid of a structural template requires extensive conformational sampling and energy minimization, which are computationally difficult. In this article we present a new de novo loop sampling method, the Parallely filtered Energy Targeted All‐atom Loop Sampler (PETALS) to rapidly locate low energy conformations. PETALS explores both backbone and side‐chain positions of the loop region simultaneously according to the energy function selected by the user, and constructs a nonredundant ensemble of low energy loop conformations using filtering criteria. The method is illustrated with the DFIRE potential and DiSGro energy function for loops, and shown to be highly effective at discovering conformations with near‐native (or better) energy. Using the same energy function as the DiSGro algorithm, PETALS samples conformations with both lower RMSDs and lower energies. PETALS is also useful for assessing the accuracy of different energy functions. PETALS runs rapidly, requiring an average time cost of 10 minutes for a length 12 loop on a single 3.2 GHz processor core, comparable to the fastest existing de novo methods for generating an ensemble of conformations. Proteins 2017; 85:1402–1412. © 2017 Wiley Periodicals, Inc.     

Nature of the Pre-Chemistry Ensemble in Mitogen-Activated Protein Kinases

In spite of the availability of a significant amount of structural detail on docking interactions involving mitogen-activated protein kinases (MAPKs) and their substrates, the mechanism by which the disordered phospho-acceptor on the substrate transiently interacts with the kinase catalytic elements and is phosphorylated, often with high efficiency, remains poorly understood. Here, this dynamic interaction is analyzed in the context of available biophysical and biochemical data for ERK2, an archetypal MAPK. A hypothesis about the nature of the ternary complex involving a MAPK, its substrate, and ATP immediately prior to the chemical step (the pre-chemistry complex) is proposed. It is postulated that the solution ensemble (the pre-chemistry ensemble) representing the pre-chemistry complex comprises several conformations that are linked by dynamics on multiple timescales. These individual conformations possess different intrinsic abilities to proceed through the chemical step. The overall rate of chemistry is therefore related to the microscopic nature of the pre-chemistry ensemble, its constituent conformational microstates, and their intrinsic abilities to yield a phosphorylated product. While characterizing these microstates within the pre-chemistry ensemble in atomic or near-atomic detail is an extremely challenging proposition, recent developments in hybrid methodologies that employ computational approaches driven by experimental data appear to provide the most promising path forward toward achieving this goal.     

Exploring the dynamic information content of a protein NMR structure: Comparison of a molecular dynamics simulation with the NMR and X‐ray structures of Escherichia coli ribonuclease HI

The multiconformer nature of solution nuclear magnetic resonance (NMR) structures of proteins results from the effects of intramolecular dynamics, spin diffusion and an uneven distribution of structural restraints throughout the molecule. A delineation of the former from the latter two contributions is attempted in this work for an ensemble of 15 NMR structures of the protein Escherichia coli ribonuclease HI (RNase HI). Exploration of the dynamic information content of the NMR ensemble is carried out through correlation with data from two crystal structures and a 1.7‐ns molecular dynamics (MD) trajectory of RNase HI in explicit solvent. Assessment of the consistency of the crystal and mean MD structures with nuclear Overhauser effect (NOE) data showed that the NMR ensemble is overall more compatible with the high‐resolution (1.48 Å) crystal structure than with either the lower‐resolution (2.05 Å) crystal structure or the MD simulation. Furthermore, the NMR ensemble is found to span more conformational space than the MD simulation for both the backbone and the sidechains of RNase HI. Nonetheless, the backbone conformational variability of both the NMR ensemble and the simulation is especially consistent with NMR relaxation measurements of two loop regions that are putative sites of substrate recognition. Plausible side‐chain dynamic information is extracted from the NMR ensemble on the basis of (i) rotamericity and syn‐pentane character of variable torsion angles, (ii) comparison of the magnitude of atomic mean‐square fluctuations (msf) with those deduced from crystallographic thermal factors, and (iii) comparison of torsion angle conformational behavior in the NMR ensemble and the simulation. Several heterogeneous torsion angles, while adopting non‐rotameric/syn‐pentane conformations in the NMR ensemble, exist in a unique conformation in the simulation and display low X‐ray thermal factors. These torsions are identified as sites whose variability is likely to be an artifact of the NMR structure determination procedure. A number of other torsions show a close correspondence between the conformations sampled in the NMR and MD ensembles, as well as significant correlations among crystallographic thermal factors and atomic msf calculated from the NMR ensemble and the simulation. These results indicate that a significant amount of dynamic information is contained in the NMR ensemble. The relevance of the present findings for the biological function of RNase HI, protein recognition studies, and previous investigations of the motional content of protein NMR structures are discussed. Proteins 1999;36:87–110. © 1999 Wiley‐Liss, Inc.     

Adaptability in protein structures: structural dynamics and implications in ligand design

The basic framework of understanding the mechanisms of protein functions is achieved from the knowledge of their structures which can model the molecular recognition. Recent advancement in the structural biology has revealed that in spite of the availability of the structural data, it is nontrivial to predict the mechanism of the molecular recognition which progresses via situation-dependent structural adaptation. The mutual selectivity of protein–protein and protein–ligand interactions often depends on the modulations of conformations empowered by their inherent flexibility, which in turn regulates the function. The mechanism of a protein’s function, which used to be explained by the ideas of ‘lock and key’ has evolved today as the concept of ‘induced fit’ as well as the ‘population shift’ models. It is felt that the ‘dynamics’ is an essential feature to take into account for understanding the mechanism of protein’s function. The design principles of therapeutic molecules suffer from the problems of plasticity of the receptors whose binding conformations are accurately not predictable from the prior knowledge of a template structure. On the other hand, flexibility of the receptors provides the opportunity to improve the binding affinity of a ligand by suitable substitution that will maximize the binding by modulating the receptors surface. In this paper, we discuss with example how the protein’s flexibility is correlated with its functions in various systems, revealing the importance of its understanding and for making applications. We also highlight the methodological challenges to investigate it computationally and to account for the flexible nature of the molecules in drug design.     

A molecular dynamics study of the 41-56 beta-hairpin from B1 domain of protein G.

The structural and dynamical behavior of the 41-56 beta-hairpin from the protein G B1 domain (GB1) has been studied at different temperatures using molecular dynamics (MD) simulations in an aqueous environment. The purpose of these simulations is to establish the stability of this hairpin in view of its possible role as a nucleation site for protein folding. The conformation of the peptide in the crystallographic structure of the protein GB1 (native conformation) was lost in all simulations. The new equilibrium conformations are stable for several nanoseconds at 300K (>10 ns), 350 K (>6.5 ns), and even at 450 K (up to 2.5 ns). The new structures have very similar hairpin-like conformations with properties in agreement with available experimental nuclear Overhauser effect (NOE) data. The stability of the structure in the hydrophobic core region during the simulations is consistent with the experimental data and provides further evidence for the role played by hydrophobic interactions in hairpin structures. Essential dynamics analysis shows that the dynamics of the peptide at different temperatures spans basically the same essential subspace. The main equilibrium motions in this subspace involve large fluctuations of the residues in the turn and ends regions. Of the six interchain hydrogen bonds, the inner four remain stable during the simulations. The space spanned by the first two eigenvectors, as sampled at 450 K, includes almost all of the 47 different hairpin structures found in the database. Finally, analysis of the hydration of the 300 K average conformations shows that the hydration sites observed in the native conformation are still well hydrated in the equilibrium MD ensemble.