Protein Conformational Ensembles Research Articles

Determining and visualizing flexibility in protein structures.

How to compare the structures of an ensemble of protein conformations is a fundamental problem in structural biology. As has been previously observed, the widely used RMSD measure due to Kabsch, in which a rigid-body superposition minimizing the least-squares positional deviations is performed, has its drawbacks when comparing and visualizing a set of flexible protein structures. Here, we develop a method, fleximatch, of protein structure comparison that takes flexibility into account. Based on a distance matrix measure of flexibility, a weighted superposition of distance matrices rather than of atomic coordinates is performed. Subsequently, this allows a consistent determination of (a) a superposition of structures for visualization, (b) a partitioning of the protein structure into rigid molecular components (core atoms), and (c) an atomic mobility measure. The method is suitable for highlighting both particularly flexible and rigid parts of a protein from structures derived from NMR, X-ray diffraction or molecular simulation.

A Monte Carlo Method for Generating Side Chain Structural Ensembles

We report a Monte Carlo side chain entropy (MC-SCE) method that uses a physical energy function inclusive of long-range electrostatics and hydrophobic potential of mean force, coupled with both backbone variations and a backbone dependent side chain rotamer library, to describe protein conformational ensembles. Using the MC-SCE method in conjunction with backbone variability, we can reliably determine the side chain rotamer populations derived from both room temperature and cryogenically cooled X-ray crystallographic structures for CypA and H-Ras andNMR J-coupling constants for CypA, Eglin-C, and the DHFR product binary complexes E:THF and E:FOL. Furthermore, we obtain near perfect discrimination between a protein's native state ensemble and ensembles of misfolded structures for 55 different proteins, thereby generating far more competitive side chain packings for all of these proteins and their misfolded states.

Evidence of conformational selection driving the formation of ligand binding sites in protein-protein interfaces.

Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.

Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging.

Accurate calculations of free energies involved in small-molecule binding to a receptor are challenging. Interactions between ligand, receptor, and solvent molecules have to be described precisely, and a large number of conformational microstates has to be sampled, particularly for ligand binding to a flexible protein. Linear interaction energy models are computationally efficient methods that have found considerable success in the prediction of binding free energies. Here, we parametrize a linear interaction model for implicit solvation with coefficients adapted by ligand and binding site relative polarities in order to predict ligand binding free energies. Results obtained for a diverse series of ligands suggest that the model has good predictive power and transferability. We also apply implicit ligand theory and propose approximations to average contributions of multiple ligand-receptor poses built from a protein conformational ensemble and find that exponential averages require proper energy discrimination between plausible binding poses and false-positives (i.e., decoys). The linear interaction model and the averaging procedures presented can be applied independently of each other and of the method used to obtain the receptor structural representation.

SLDMOL: A tool for the structural characterization of thermally disordered membrane proteins

SLDMOL is a program for modeling the 1-D scattering length density (SLD) profile of proteins at the lipid membrane–solution interface or adsorbed to other surfaces. The program reads experimental SLD data from neutron or X-ray reflectivity measurements and compares the results to a trajectory of protein structures, finding the conformation and orientation that best fits the experimental data. SLDMOL is a freely distributed open source program written in python that can be run independently using command lines or a GUI. SLDMOL has also been integrated into the larger SASSIE package extending molecular modeling capabilities. Sample environment conditions can be replicated including H2O/D2O solvent contrasts, specific amino acid deuteration and complex molecular assemblies. Ensembles of protein conformations can be generated independently (e.g. molecular dynamics simulations) or with SASSIE. For each individual structure a best-fit SLD profile is outputted along with a goodness of fit parameter, protein depth penetration and surface coverage. In addition to individual comparisons SLD profiles can be calculated over ensemble averages of protein structures. As a result, SLDMOL provides a detailed molecular interpretation of reflectivity data or conversely can be used to predict experimental outcomes for different protein conformation and specific deuteration schemes prior to measurements. Program summaryProgram title: SLDMOLCatalogue identifier: AETX_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AETX_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public License, version 3No. of lines in distributed program, including test data, etc.: 3302107No. of bytes in distributed program, including test data, etc.: 125715760Distribution format: tar.gzProgramming language: Python.Computer: PC/Mac.Operating system: 32- and 64-bit Linux (Ubuntu 10.04, Centos 5.6) and Mac OS X (10.7–10.9).RAM: 1 GBClassification: 3.External routines: Python 2.6.5, numpy 1.4.0, scipy 0.8.0, Tcl 8.5, Tk 8.5, Mac installation requires Xcode 3 development tools.Subprograms used:CatIdTitleReferenceAEKL_v1_0SASSIECPC 183(2012)382Nature of problem:Reflectivity is a powerful technique to study the conformation of biological molecules at surface and interfaces. Open source software that facilitates computational modeling and interpretation of experimental results in terms of detailed molecular structure is currently lacking.Solution method:SLDMOL takes one or an ensemble of atomistic configurations of proteins and compares them to 1-D scattering length density (SLD) profiles determined from reflectivity experiments. Protein structures can be generated independently (e.g. molecular dynamics simulations) or through the SASSIE software package. SLDMOL performs an optimization to determine which structure best fits the SLD profile. Structures can also be averaged either directly or through a Monte-Carlo weighting algorithm. Finally SLDMOL allows the modeling of reflectivity experiments through the in silico selective deuteration of amino acids and the calculation of SLD profiles in a number of different aqueous solvent contrasts.Additional comments:!!!!Due to the large file size, SLDMOL is not delivered directly when download or Email is requested. Instead an html file giving details of how the program can be obtained is sent.!!!!Running time:Varies depending on application. Typically 10 min to 24 h depending on the number of structures to be evaluated.

Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2.

Molecular recognition is a complex process that involves a large ensemble of structures of the receptor and ligand. Yet, most structure-based virtual screening is carried out on a single structure typically from X-ray crystallography. Explicit-solvent molecular dynamics (MD) simulations offer an opportunity to sample multiple conformational states of a protein. Here we evaluate our recently developed scoring method SVMSP in its ability to enrich chemical libraries docked to MD structures of seven proteins from the Directory of Useful Decoys (DUD). SVMSP is a target-specific rescoring method that combines machine learning with statistical potentials. We find that enrichment power as measured by the area under the ROC curve (ROC-AUC) is not affected by increasing the number of MD structures. Among individual MD snapshots, many exhibited enrichment that was significantly better than the crystal structure, but no correlation between enrichment and structural deviation from crystal structure was found. We followed an innovative approach by training SVMSP scoring models using MD structures (SVMSPMD). The resulting models were applied to two difficult cases (p38 and CDK2) for which enrichment was not better than random. We found remarkable increase in enrichment power, particularly for p38, where the ROC-AUC increased by 0.30 to 0.85. Finally, we explored approaches for a priori identification of MD snapshots with high enrichment power from an MD simulation in the absence of active compounds. We found that the use of randomly selected compounds docked to the target of interest using SVMSP led to notable enrichment for EGFR and Src MD snapshots. SVMSP rescoring of protein–compound MD structures was applied for the search of small-molecule inhibitors of the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2). Rank-ordering of a commercial library of 50 000 compounds docked to MD structures of ALDH2 led to five small-molecule inhibitors. Four compounds had IC50s below 5 μM. These compounds serve as leads for the design and synthesis of more potent and selective ALDH2 inhibitors.

Dynamic protein conformations preferentially drive energy transfer along the active chain of the photosystem II reaction centre.

One longstanding puzzle concerning photosystem II, a core component of photosynthesis, is that only one of the two symmetric branches in its reaction centre is active in electron transfer. To investigate the effect of the photosystem II environment on the preferential selection of the energy transfer pathway (a prerequisite for electron transfer), we have constructed an exciton model via extensive molecular dynamics simulations and quantum mechanics/molecular mechanics calculations based on a recent X-ray structure. Our results suggest that it is essential to take into account an ensemble of protein conformations to accurately compute the site energies. We identify the cofactor CLA606 of active chain as the most probable site for the energy excitation. We further pinpoint a number of charged protein residues that collectively lower the CLA606 site energy. Our work provides insights into the understanding of molecular mechanisms of the core machinery of the green-plant photosynthesis.

Ensemble-based docking using biased molecular dynamics.

Proteins are dynamic molecules, and understanding their movements, especially as they relate to molecular recognition and protein-ligand interactions, poses a significant challenge to structure-based drug discovery. In most instances, protein flexibility is underrepresented in computer-aided drug design due to uncertainties on how it should be accurately modeled as well as the computational cost associated with attempting to incorporate flexibility in the calculations. One approach that aims to address these issues is ensemble-based docking. With this technique, ligands are docked to an ensemble of rigid protein conformations. Molecular dynamics (MD) simulations can be used to generate the ensemble of protein conformations for the subsequent docking. Here we present a novel approach that uses biased-MD simulations to generate the docking ensemble. The MD simulations are biased toward an initial protein-ligand X-ray complex structure. The biasing maintains some of the original crystallographic pocket-ligand information and thereby enhances sampling of the more relevant conformational space of the protein. Resulting trajectories are clustered to select a representative set of protein conformations, and ligands are docked to that reduced set of conformations. Cross-docking to this ensemble and then selecting the lowest scoring pose enables reliable identification of the correct binding mode. Various levels of biasing are investigated, and the method is validated for cyclin-dependent kinase 2 and factor Xa.

WExplore: Hierarchical Exploration of High-Dimensional Spaces Using the Weighted Ensemble Algorithm

As most relevant motions in biomolecular systems are inaccessible to conventional molecular dynamics simulations, algorithms that enhance sampling of rare events are indispensable. Increasing interest in intrinsically disordered systems and the desire to target ensembles of protein conformations (rather than single structures) in drug development motivate the need for enhanced sampling algorithms that are not limited to "two-basin" problems, and can efficiently determine structural ensembles. For systems that are not well-studied, this must often be done with little or no information about the dynamics of interest. Here we present a novel strategy to determine structural ensembles that uses dynamically defined sampling regions that are organized in a hierarchical framework. It is based on the weighted ensemble algorithm, where an ensemble of copies of the system ("replicas") is directed to new regions of configuration space through merging and cloning operations. The sampling hierarchy allows for a large number of regions to be defined, while using only a small number of replicas that can be balanced over multiple length scales. We demonstrate this algorithm on two model systems that are analytically solvable and examine the 10-residue peptide chignolin in explicit solvent. The latter system is analyzed using a configuration space network, and novel hydrogen bonds are found that facilitate folding.

A Computational Method Including Protein Flexibility to Estimate Affinities with Small Ligands

Computational methods are used to generate protein-ligand complex structures and predict their binding affinities. Usually protein flexibility is not fully considered, and the energy functions used to estimate binding affinities are poor. This work investigated how implicit ligand and solvation theories as well as the linear response approximation may be combined to better describe the effect of protein flexibility on ligand binding affinities.T4 lysozyme mutants, HIV-1 reverse transcriptase (HIVRT) and human FK506 binding protein 12 (FKBP) were chosen as model systems. An adaptive energy function based on the linear interaction energy approximation was parametrized and used to estimate partial affinities. Parameters were adapted according to ligand and protein surface polarities. Proteins were represented as an approximate conformational ensemble derived from molecular dynamics simulations. Interaction energies were obtained using the OPLS-AA force field with modified partial charges for ligands. A generalized Born model was used for implicit solvation.The parametrized energy function resulted in average deviations between experimental and calculated affinities of 1.0 kcal/mol and a correlation coefficient R2=0.8 for a test set of complexes with known binding sites. Discrimination of false-positive poses was also substantial. Then, approximations to the implicit ligand theory were proposed in order to obtain total binding affinities by combining interaction energies calculated for ligand complexes with the protein conformational ensembles. Several configurations contribute with the same weight for the FKBP protein. But, for lysozyme and HIVRT proteins, total affinities are dominated by one configuration. These results suggest that a faithful representation of protein conformational flexibility and an adequate statistical treatment based on implicit theories may be used to rapidly estimate reliable binding affinities.

Evaluating DEER Distance Profiles in Terms of Protein Conformational Ensembles

Distance profiles obtained from DEER spectroscopy provide a wealth of information regarding distances between spin labels in macromolecular systems. We have been evaluating the utility of DEER distance profiles to provide information regarding changes in conformational sampling ensembles in HIV-1 protease. In this workshop we will present our approaches and developments related to assessing errors in the data and how the data are related back to protein conformational sampling states. We will also show that sample freezing methods do not alter HIV distance ensembles within errors of the measurements.

Exploring Early Stages of the Chemical Unfolding of Proteins at the Proteome Scale

After decades of using urea as denaturant, the kinetic role of this molecule in the unfolding process is still undefined: does urea actively induce protein unfolding or passively stabilize the unfolded state? By analyzing a set of 30 proteins (representative of all native folds) through extensive molecular dynamics simulations in denaturant (using a range of force-fields), we derived robust rules for urea unfolding that are valid at the proteome level. Irrespective of the protein fold, presence or absence of disulphide bridges, and secondary structure composition, urea concentrates in the first solvation shell of quasi-native proteins, but with a density lower than that of the fully unfolded state. The presence of urea does not alter the spontaneous vibration pattern of proteins. In fact, it reduces the magnitude of such vibrations, leading to a counterintuitive slow down of the atomic-motions that opposes unfolding. Urea stickiness and slow diffusion is, however, crucial for unfolding. Long residence urea molecules placed around the hydrophobic core are crucial to stabilize partially open structures generated by thermal fluctuations. Our simulations indicate that although urea does not favor the formation of partially open microstates, it is not a mere spectator of unfolding that simply displaces to the right of the folded←→unfolded equilibrium. On the contrary, urea actively favors unfolding: it selects and stabilizes partially unfolded microstates, slowly driving the protein conformational ensemble far from the native one and also from the conformations sampled during thermal unfolding.

The Impact of Molecular Dynamics Sampling on the Performance of Virtual Screening against GPCRs

The formation of ligand-protein complexes requires simultaneous adaptation of the binding partners. In structure based virtual screening, high throughput docking approaches typically consider the ligand flexibility, but the conformational freedom of the protein is usually taken into account in a limited way. The goal of this study is to elaborate a methodology for incorporating protein flexibility to improve the virtual screening enrichments on GPCRs. Explicit-solvated molecular dynamics simulations (MD) were carried out in lipid bilayers to generate an ensemble of protein conformations for the X-ray structures and homology models of both aminergic and peptidergic GPCRs including the chemokine CXCR4, dopamine D3, histamine H4, and serotonin 5HT6 holo receptor complexes. The quality of the receptor models was assessed by enrichment studies to compare X-ray structures, homology models, and snapshots from the MD trajectory. According to our results, selected frames from the MD trajectory can outperform X-ray structures and homology models in terms of enrichment factor and AUC values. Significant changes were observed considering EF1% values: comparing the original CXCR4, D3, and H4 targets and the additional 5HT6 initial models to that of the best MD frame resulted in 0 to 6.7, 0.32 to 3.5 (10×), 13.3 to 26.7 (2×), and 0 to 14.1 improvements, respectively. It is worth noting that rank-average based ensemble evaluation calculated for different ensemble sizes could not improve the results further. We propose here that MD simulation can capture protein conformations representing the key interacting points of the receptor but less biased toward one specific chemotype. These conformations are useful for the identification of a "consensus" binding site with improved performance in virtual screening.

Self Organizing Maps to efficiently cluster and functionally interpret protein conformational ensembles

An approach that combines Self-Organizing maps, hierarchical clustering and network components is presented, aimed at comparing protein conformational ensembles obtained from multiple Molecular Dynamic simulations. As a first result the original ensembles can be summarized by using only the representative conformations of the clusters obtained. In addition the network components analysis allows to discover and interpret the dynamic behavior of the conformations won by each neuron. The results showed the ability of this approach to efficiently derive a functional interpretation of the protein dynamics described by the original conformational ensemble, highlighting its potential as a support for protein engineering.

TRAPP: A Tool for Analysis ofTransient BindingPockets inProteins

We present TRAPP (TRAnsient Pockets in Proteins), a new automated software platform for tracking, analysis, and visualization of binding pocket variations along a protein motion trajectory or within an ensemble of protein structures that may encompass conformational changes ranging from local side chain fluctuations to global backbone motions. TRAPP performs accurate grid-based calculations of the shape and physicochemical characteristics of a binding pocket for each structure and detects the conserved and transient regions of the pocket in an ensemble of protein conformations. It also provides tools for tracing the opening of a particular subpocket and residues that contribute to the binding site. TRAPP thus enables an assessment of the druggability of a disease-related target protein taking its flexibility into account.

Artificial Neural Networks for Efficient Clustering of Conformational Ensembles and their Potential for Medicinal Chemistry

The biological function of proteins is strictly related to their molecular flexibility and dynamics: enzymatic activity, protein-protein interactions, ligand binding and allosteric regulation are important mechanisms involving protein motions. Computational approaches, such as Molecular Dynamics (MD) simulations, are now routinely used to study the intrinsic dynamics of target proteins as well as to complement molecular docking approaches. These methods have also successfully supported the process of rational design and discovery of new drugs. Identification of functionally relevant conformations is a key step in these studies. This is generally done by cluster analysis of the ensemble of structures in the MD trajectory. Recently Artificial Neural Network (ANN) approaches, in particular methods based on Self-Organising Maps (SOMs), have been reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data-mining problems. In the specific case of conformational analysis, SOMs have been successfully used to compare multiple ensembles of protein conformations demonstrating a potential in efficiently detecting the dynamic signatures central to biological function. Moreover, examples of the use of SOMs to address problems relevant to other stages of the drug-design process, including clustering of docking poses, have been reported. In this contribution we review recent applications of ANN algorithms in analysing conformational and structural ensembles and we discuss their potential in computer-based approaches for medicinal chemistry.

Collapse of Unfolded Proteins in a Mixture of Denaturants

Both urea and guanidinium chloride (GdmCl) are frequently used as protein denaturants. Given that proteins generally adopt extended or unfolded conformations in either aqueous urea or GdmCl, one might expect that the unfolded protein chains will remain or become further extended due to the addition of another denaturant. However, a collapse of denatured proteins is revealed using atomistic molecular dynamics simulations when a mixture of denaturants is used. Both hen egg-white lysozyme and protein L are found to undergo collapse in the denaturant mixture. The collapse of the protein conformational ensembles is accompanied by a decreased solubility and increased non-native self-interactions of hydrophobic residues in the urea/GdmCl mixture. The increase of non-native interactions rather than the native contacts indicates that the proteins experience a simple collapse transition from the fully denatured states. During the protein collapse, the relatively stronger denaturant GdmCl displays a higher tendency to be absorbed onto the protein surface due to their stronger electrostatic interactions with proteins. At the same time, urea molecules also accumulate near the protein surface, resulting in an enhanced "local crowding" for the protein near its first solvation shell. This rearrangement of denaturants near the protein surface and crowded local environment induce the protein collapse, mainly by burying their hydrophobic residues. These findings from molecular simulations are then further explained by a simple analytical model based on statistical mechanics.

CAVER 3.0: A Tool for the Analysis of Transport Pathways in Dynamic Protein Structures

Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at http://www.caver.cz.

Integrating Ligand-Based and Protein-Centric Virtual Screening of Kinase Inhibitors Using Ensembles of Multiple Protein Kinase Genes and Conformations

The rapidly growing wealth of structural and functional information about kinase genes and kinase inhibitors that is fueled by a significant therapeutic role of this protein family provides a significant impetus for development of targeted computational screening approaches. In this work, we explore an ensemble-based, protein-centric approach that allows for simultaneous virtual ligand screening against multiple kinase genes and multiple kinase receptor conformations. We systematically analyze and compare the results of ligand-based and protein-centric screening approaches using both single-receptor and ensemble-based docking protocols. A panel of protein kinase targets that includes ABL, EGFR, P38, CDK2, TK, and VEGFR2 kinases is used in this comparative analysis. By applying various performance metrics we have shown that ligand-centric shape matching can provide an effective enrichment of active compounds outperforming single-receptor docking screening. However, ligand-based approaches can be highly sensitive to the choice of inhibitor queries. Employment of multiple inhibitor queries combined with parallel selection ranking criteria can improve the performance and efficiency of ligand-based virtual screening. We also demonstrated that replica-exchange Monte Carlo docking with kinome-based ensembles of multiple crystal structures can provide a superior early enrichment on the kinase targets. The central finding of this study is that incorporation of the template-based structural information about kinase inhibitors and protein kinase structures in diverse functional states can significantly enhance the overall performance and robustness of both ligand and protein-centric screening strategies. The results of this study may be useful in virtual screening of kinase inhibitors potentially offering a beneficial spectrum of therapeutic activities across multiple disease states.

PPM: a side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles

The combination of the wide availability of protein backbone and side-chain NMR chemical shifts with advances in understanding of their relationship to protein structure makes these parameters useful for the assessment of structural-dynamic protein models. A new chemical shift predictor (PPM) is introduced, which is solely based on physical-chemical contributions to the chemical shifts for both the protein backbone and methyl-bearing amino-acid side chains. To explicitly account for the effects of protein dynamics on chemical shifts, PPM was directly refined against 100 ns long molecular dynamics (MD) simulations of 35 proteins with known experimental NMR chemical shifts. It is found that the prediction of methyl-proton chemical shifts by PPM from MD ensembles is improved over other methods, while backbone Cα, Cβ, C', N, and H(N) chemical shifts are predicted at an accuracy comparable to the latest generation of chemical shift prediction programs. PPM is particularly suitable for the rapid evaluation of large protein conformational ensembles on their consistency with experimental NMR data and the possible improvement of protein force fields from chemical shifts.