Large-scale Conformational Changes Of Proteins Research Articles

Sampling of the conformational landscape of small proteins with Monte Carlo methods

Computer simulation provides an increasingly realistic picture of large-scale conformational change of proteins, but investigations remain fundamentally constrained by the femtosecond timestep of molecular dynamics simulations. For this reason, many biologically interesting questions cannot be addressed using accessible state-of-the-art computational resources. Here, we report the development of an all-atom Monte Carlo approach that permits the modelling of the large-scale conformational change of proteins using standard off-the-shelf computational hardware and standard all-atom force fields. We demonstrate extensive thermodynamic characterization of the folding process of the α-helical Trp-cage, the Villin headpiece and the β-sheet WW-domain. We fully characterize the free energy landscape, transition states, energy barriers between different states, and the per-residue stability of individual amino acids over a wide temperature range. We demonstrate that a state-of-the-art intramolecular force field can be combined with an implicit solvent model to obtain a high quality of the folded structures and also discuss limitations that still remain.

Quantifying the Intrinsic Conformation Energy Landscape Topography of Proteins with Large-Scale Open-Closed Transition.

Large-scale conformational changes of proteins, including the open–closed transitions, are crucial for a variety of protein functions. These open–closed transitions are often associated with ligand binding. However, the understandings of the underlying mechanisms of the conformational changes within proteins during the open–closed transitions are still challenging at present. In this study, we quantified the intrinsic underlying conformational energy landscapes of five different proteins with large-scale open–closed transitions. This is realized by exploring the underlying density of states and the intrinsic conformational energy landscape topography measure Λ. Λ is a dimensionless ratio of conformational energy gap δE versus conformational energy roughness δE and configurational entropy S or size of the intrinsic conformational energy landscape. By quantifying the Λ of intrinsic open–closed conformational (Λoc) and intrinsic global folding (Λglobal) energy landscapes, we show that both intrinsic open–closed conformation energy and entropy landscapes are funneled toward the closed state. Furthermore, our results indicate the strong correlations between Λ and thermodynamics (conformational state transition temperature against trapping temperature) as well as between Λ and kinetics (open–closed kinetic time) of these proteins. This shows that the intrinsic conformational landscape topography determines both the conformational thermodynamic stability and kinetic speed of the conformational dynamics. Our investigations provide important insights for understanding the fundamental mechanisms of the protein conformational dynamics in a physical and global way.

Protein plasticity driven by disorder and collapse governs the heterogeneous binding of CytR to DNA.

The amplitude of thermodynamic fluctuations in biological macromolecules determines their conformational behavior, dimensions, nature of phase transitions and effectively their specificity and affinity, thus contributing to fine-tuned molecular recognition. Unique among large-scale conformational changes in proteins are temperature-induced collapse transitions in intrinsically disordered proteins (IDPs). Here, we show that CytR DNA-binding domain, an IDP that folds on binding DNA, undergoes a coil-to-globule transition with temperature in the absence of DNA while exhibiting energetically decoupled local and global structural rearrangements, and maximal thermodynamic fluctuations at the optimal bacterial growth temperature. The collapse is shown to be a continuous transition through a combination of statistical-mechanical modeling and all-atom implicit solvent simulations. Surprisingly, CytR binds single-site cognate DNA with negative cooperativity, described by Hill coefficients less than one, resulting in a graded binding response. We show that heterogeneity arising from varying binding-competent CytR conformations or orientations at the single-molecular level contributes to negative binding cooperativity at the level of bulk measurements due to the conflicting requirements of collapse transition, large fluctuations and folding-upon-binding. Our work reports strong evidence for functionally driven thermodynamic fluctuations in determining the extent of collapse and disorder with implications in protein search efficiency of target DNA sites and regulation.

A probabilistic model for detecting rigid domains in protein structures.

Large-scale conformational changes in proteins are implicated in many important biological functions. These structural transitions can often be rationalized in terms of relative movements of rigid domains. There is a need for objective and automated methods that identify rigid domains in sets of protein structures showing alternative conformational states. We present a probabilistic model for detecting rigid-body movements in protein structures. Our model aims to approximate alternative conformational states by a few structural parts that are rigidly transformed under the action of a rotation and a translation. By using Bayesian inference and Markov chain Monte Carlo sampling, we estimate all parameters of the model, including a segmentation of the protein into rigid domains, the structures of the domains themselves, and the rigid transformations that generate the observed structures. We find that our Gibbs sampling algorithm can also estimate the optimal number of rigid domains with high efficiency and accuracy. We assess the power of our method on several thousand entries of the DynDom database and discuss applications to various complex biomolecular systems. The Python source code for protein ensemble analysis is available at: https://github.com/thachnguyen/motion_detection : mhabeck@gwdg.de.

A Riemannian Framework for Tackling Large-Scale Conformational Changes of Proteins using All-ATOM Molecular Dynamics Simulations

Simulation studies of biomolecular processes often suffer from the curse of dimensionality both in sampling and in analysis. Dimensionality reduction, either using intuitive reaction coordinates or automated algorithms, is thus an inevitable part of advanced biomolecular simulation studies. Most free energy calculation methods and path-finding algorithms, implicitly or explicitly, rely on an effective diffusion model in a low-dimensional space as their underlying theoretical framework. Dimensionality reduction, however, comes with a price and the effective dynamics of the reduced system could be nontrivial. For instance, assuming the diffusivity of the effective dynamics, the diffusion tensor is likely to be position-dependent and anisotropic.We have developed a Riemannian framework for free energy calculation methods and path-finding algorithms in molecular dynamics simulations. We show that a Riemannian diffusion model may describe the dynamics of the reduced system such that a Riemannian metric replaces the position-dependent diffusion tensor. In addition, the Riemannian formulation provides a suitable mathematical framework for generalizing one-dimensional free energy relations to multi-dimensional spaces in a rigorous manner such that the model is invariant under any smooth coordinate transformation.Within the proposed mathematical framework, a novel combination of free energy calculation methods and path-finding algorithms is developed which can be used for accurate calculation of free energies along protein conformational transition pathways using all-atom molecular dynamics simulations. The method is particularly useful for describing large-scale and complex conformational changes of proteins. We note that the simplified Euclidean version of the method was recently employed successfully in reconstructing the transport cycle of a membrane transporter at an atomic level (see M. Moradi, et al, “Atomic-level characterization of transport cycle thermodynamics in the glycerol-3-phosphate:phosphate antiporter.”, Nat. Commun., 6:8393, 2015).

Enhanced conformational sampling technique provides an energy landscape view of large-scale protein conformational transitions.

Large-scale conformational changes in proteins are important for their functions. Tracking the conformational change in real time at the level of a single protein molecule, however, remains a great challenge. In this article, we present a novel in silico approach with the combination of normal mode analysis and integrated-tempering-sampling molecular simulation (NMA-ITS) to give quantitative data for exploring the conformational transition pathway in multi-dimensional energy landscapes starting only from the knowledge of the two endpoint structures of the protein. The open-to-closed transitions of three proteins, including nCaM, AdK, and HIV-1 PR, were investigated using NMA-ITS simulations. The three proteins have varied structural flexibilities and domain communications in their respective conformational changes. The transition state structure in the conformational change of nCaM and the associated free-energy barrier are in agreement with those measured in a standard explicit-solvent REMD simulation. The experimentally measured transition intermediate structures of the intrinsically flexible AdK are captured by the conformational transition pathway measured here. The dominant transition pathways between the closed and fully open states of HIV-1 PR are very similar to those observed in recent REMD simulations. Finally, the evaluated relaxation times of the conformational transitions of three proteins are roughly at the same level as reported experimental data. Therefore, the NMA-ITS method is applicable for a variety of cases, providing both qualitative and quantitative insights into the conformational changes associated with the real functions of proteins.

CHARMM Force-Fields with Modified Polyphosphate Parameters Allow Stable Simulation of the ATP-Bound Structure of Ca(2+)-ATPase.

Adenosine triphosphate (ATP) is an indispensable energy source in cells. In a wide variety of biological phenomena like glycolysis, muscle contraction/relaxation, and active ion transport, chemical energy released from ATP hydrolysis is converted to mechanical forces to bring about large-scale conformational changes in proteins. Investigation of structure-function relationships in these proteins by molecular dynamics (MD) simulations requires modeling of ATP in solution and ATP bound to proteins with accurate force-field parameters. In this study, we derived new force-field parameters for the triphosphate moiety of ATP based on the high-precision quantum calculations of methyl triphosphate. We tested our new parameters on membrane-embedded sarcoplasmic reticulum Ca(2+)-ATPase and four soluble proteins. The ATP-bound structure of Ca(2+)-ATPase remains stable during MD simulations, contrary to the outcome in shorter simulations using original parameters. Similar results were obtained with the four ATP-bound soluble proteins. The new force-field parameters were also tested by investigating the range of conformations sampled during replica-exchange MD simulations of ATP in explicit water. Modified parameters allowed a much wider range of conformational sampling compared with the bias toward extended forms with original parameters. A diverse range of structures agrees with the broad distribution of ATP conformations in proteins deposited in the Protein Data Bank. These simulations suggest that the modified parameters will be useful in studies of ATP in solution and of the many ATP-utilizing proteins.

Simulating Large-Scale Conformational Changes of Proteins by Accelerating Collective Motions Obtained from Principal Component Analysis

Enhanced sampling methods remain of continuing interest over the past decades because they are able to explore conformational space of proteins much more extensively than conventional molecular dynamics (MD) simulations. In this paper, we report a new sampling method that utilizes a few collective modes obtained from principal component analysis (PCA) to guide the MD simulations. Two multidomain proteins, bacteriophage T4 lysozyme and human vinculin, are studied to test the method. By updating the PCA modes with a proper frequency, our method can sample large-amplitude conformational changes of the proteins much more efficiently than standard MD. Since those PCA modes are calculated from structural ensembles generated by all-atom simulations, the method may overcome an inherent limitation called "tip effect" that would possibly appear in those sampling techniques based on coarse-grained elastic network models. The algorithm proposed here is potentially very useful in developing tools for flexible fitting of protein structures integrating cryo-electron microscope or small-angle X-ray scattering data.

Thermodynamic Characterization of Protein Folding using Monte Carlo Methods

The study of protein folding has been a difficult challenge in molecular biology and simulation science.Recent publications showed the reproducible folding of small protein using molecular dynamics simulations. This large conformational changes can be only achieved by using specialized supercomputers [1]. In contrast to molecular dynamics simulations, Monte Carlo based simulations are not constrained solving Newton's equation of motion and therefore may be used conventional computer architectures to sample the protein folding process. Here we show fast reproducible all-atom folding transitions of the villin headpiece (Figure 1) simulated using SIMONA[2], a Monte Carlo based simulation package for nanoscale simulations including a variant of the Amber99SB∗-ILDN[3]. The results demonstrate the applicability of Monte Carlo simulation techniques to the investigation of large-scale conformational changes of proteins on conventional computer architectures. The computing time for observing folding/refolding events can be significantly reduced in comparison with molecular dynamics simulations. The thermodynamic characterization of simulated proteins can be compared with the experimental results.[1] Shaw, D. E. et al. Science 330, 341-346 (2010).[2] Wolf, M., Strunk, T. et al. J Comput Chem (2012).[3] Lindorff-Larsen et al. Science 334, 517-520 (2011).View Large Image | View Hi-Res Image | Download PowerPoint Slide

Exploring Transition Pathway and Free-Energy Profile of Large-Scale Protein Conformational Change by Combining Normal Mode Analysis and Umbrella Sampling Molecular Dynamics

Large-scale conformational changes of proteins are usually associated with the binding of ligands. Because the conformational changes are often related to the biological functions of proteins, understanding the molecular mechanisms of these motions and the effects of ligand binding becomes very necessary. In the present study, we use the combination of normal-mode analysis and umbrella sampling molecular dynamics simulation to delineate the atomically detailed conformational transition pathways and the associated free-energy landscapes for three well-known protein systems, viz., adenylate kinase (AdK), calmodulin (CaM), and p38α kinase in the absence and presence of respective ligands. For each protein under study, the transient conformations along the conformational transition pathway and thermodynamic observables are in agreement with experimentally and computationally determined ones. The calculated free-energy profiles reveal that AdK and CaM are intrinsically flexible in structures without obvious energy barrier, and their ligand binding shifts the equilibrium from the ligand-free to ligand-bound conformation (population shift mechanism). In contrast, the ligand binding to p38α leads to a large change in free-energy barrier (ΔΔG ≈ 7 kcal/mol), promoting the transition from DFG-in to DFG-out conformation (induced fit mechanism). Moreover, the effect of the protonation of D168 on the conformational change of p38α is also studied, which reduces the free-energy difference between the two functional states of p38α and thus further facilitates the conformational interconversion. Therefore, the present study suggests that the detailed mechanism of ligand binding and the associated conformational transition is not uniform for all kinds of proteins but correlated to their respective biological functions.

Thermodynamic Characterization of Protein Folding Equilibriums at the All Atom Level

Recent progress has permitted observation of reproducible folding of small proteins in molecular dynamics simulations(1, 2). Despite the availability of transferable forcefields, these methods remain constrained by the short time-step and availability of specialized hardware. using a new Monte-Carlo based biomolecular simulation code, SIMONA(3), with a modified AMBER forcefield we observe rapid reproducible folding transitions in single-PC simulations of several small proteins, including the villin headpiece and the WW-domain, which can be used to completely characterize the protein folding equilibrium at the all-atom level. These results demonstrate that Monte-Carlo methods can significantly accelerate sampling of large-scale conformational changes of proteins on widely accessible computational resources.1. Lindorff-Larsen, K., et. al. Science 334, 517-520. (2012)2. Shaw, D. E., et.al. Science 330, 341-346 (2010).3. Strunk, T., et. al. SIMONA 1.0: An efficient and versatile framework for stochastic simulations of molecular and nanoscale systems, J. Comp. Chem, DOI: 10.1002/jcc.23089. (2012)View Large Image | View Hi-Res Image | Download PowerPoint Slide

Minimum Free Energy Path of Ligand-Induced Transition in Adenylate Kinase

Large-scale conformational changes in proteins involve barrier-crossing transitions on the complex free energy surfaces of high-dimensional space. Such rare events cannot be efficiently captured by conventional molecular dynamics simulations. Here we show that, by combining the on-the-fly string method and the multi-state Bennett acceptance ratio (MBAR) method, the free energy profile of a conformational transition pathway in Escherichia coli adenylate kinase can be characterized in a high-dimensional space. The minimum free energy paths of the conformational transitions in adenylate kinase were explored by the on-the-fly string method in 20-dimensional space spanned by the 20 largest-amplitude principal modes, and the free energy and various kinds of average physical quantities along the pathways were successfully evaluated by the MBAR method. The influence of ligand binding on the pathways was characterized in terms of rigid-body motions of the lid-shaped ATP-binding domain (LID) and the AMP-binding (AMPbd) domains. It was found that the LID domain was able to partially close without the ligand, while the closure of the AMPbd domain required the ligand binding. The transition state ensemble of the ligand bound form was identified as those structures characterized by highly specific binding of the ligand to the AMPbd domain, and was validated by unrestrained MD simulations. It was also found that complete closure of the LID domain required the dehydration of solvents around the P-loop. These findings suggest that the interplay of the two different types of domain motion is an essential feature in the conformational transition of the enzyme.

Conformational Transition Pathways of Adenylate Kinase Explored by the String Method

Large-scale conformational changes in proteins involve barrier-crossing transitions on the complex free energy surfaces of high-dimensional space. Here we show that, by combining the on-the-fly string method and the multi-state Bennett acceptance ratio (MBAR) method, the free energy profile of a conformational transition pathway in Escherichia coli adenylate kinase can be accurately determined. The minimum free energy paths (MFEPs) of adenylate kinase were searched by the on-the-fly string method in 20-dimensional space spanned by the 20 largest-amplitude principal modes, and the free energy and various kinds of average physical quantities along the MFEPs were successfully evaluated by the MBAR method. The influence of ligand binding on the MFEPs was characterized in terms of rigid-body motions of the LID and AMPbd domains. It was found that the LID domain was able to partially close without the ligand, while the closure of the AMPbd domain required the ligand binding. The transition state ensemble of the ligand bound form was identified as those structures characterized by highly specific binding of the ligand to the AMPbd domain, and was validated by unrestrained MD simulations. It was also found that complete closure of the LID domain required the dehydration of solvents around the P-loop. These findings suggest that the interplay of the two different types of domain motion is an feature in the conformational transition of the enzyme.

Predicting large-scale conformational changes in proteins using energy-weighted normal modes

We report the development of a method to improve the sampling of protein conformational space in molecular simulations. It is shown that a principal component analysis of energy-weighted normal modes in Cartesian coordinates can be used to extract vectors suitable for describing the dynamics of protein substructures. The method can operate with either atomistic or user-defined coarse-grained models of protein structure. An implicit reverse coarse-graining allows the dynamics of all-atoms to be recovered when a coarse-grained model is used. For an external test set of four proteins, it is shown that the new method is more successful than normal mode analysis in describing the large-scale conformational changes observed on ligand binding. The method has potential applications in protein-ligand and protein-protein docking and in biasing molecular dynamics simulations.

Predicting the reaction coordinates of millisecond light-induced conformational changes in photoactive yellow protein.

Understanding the dynamics of large-scale conformational changes in proteins still poses a challenge for molecular simulations. We employ transition path sampling of explicit solvent molecular dynamics trajectories to obtain atomistic insight in the reaction network of the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor photoactive yellow protein. Likelihood maximization analysis predicts the best model for the reaction coordinates of each substep as well as tentative transition states, without further simulation. We find that the unfolding of the alpha-helical region 43-51 is followed by sequential solvent exposure of both Glu46 and the chromophore. Which of these two residues is exposed first is correlated with the presence of a salt bridge that is part of the N-terminal domain. Additional molecular dynamics simulations indicate that the exposure of the chromophore does not result in a productive pathway. We discuss several possibilities for experimental validation of these predictions. Our results open the way for studying millisecond conformational changes in other medium-sized (signaling) proteins.

Predicting the Reaction Coordinates of Millisecond Light-Induced Conformational Changes in Photoactive Yellow Protein

Understanding the dynamics of large-scale conformational changes in proteins still poses a challenge for molecular simulation, as such processes occur on long time scales. The transition path sampling method aims to sample reactive paths connecting two stable states. We used transition path sampling to investigate the mechanisms underlying the millisecond timescale partial unfolding transition in the photocycle of the bacterial sensor Photoactive Yellow Protein. This reaction is characterized by loss of alpha-helical structure and solvent exposure of the chromophore binding pocket. Advanced analysis methods predict the best model for the reaction coordinates of each step in the unfolding reactions as well as tentative transition states. We find that the unfolding of the alpha-helical region 43-51 is followed by sequential solvent exposure of Glu46 and the chromophore. Solvent exposure of the chromophore can also occur first, but is a dead-end route. Which of these two residues is exposed first, is correlated with the presence of a salt bridge that is part of the N-terminal domain.

End-Point Targeted Molecular Dynamics: Large-Scale Conformational Changes in Potassium Channels

Large-scale conformational changes in proteins that happen often on biological time scales may be relatively rare events on the molecular dynamics time scale. We have implemented an approach to targeted molecular dynamics called end-point targeted molecular dynamics that transforms proteins between two specified conformational states through the use of nonharmonic “soft” restraints. A key feature of the method is that the protein is free to discover its own conformational pathway through the plethora of possible intermediate states. The method is applied to the Shaker K v1.2 potassium channel in implicit solvent. The rate of cycling between the open and closed states was varied to explore how slow the cycling rate needed to be to ensure that microscopic reversibility along the transition pathways was well approximated. Results specific to the K + channel include: 1), a variation in backbone torsion angles of residues near the Pro-Val-Pro motif in the inner helix during both opening and closing; 2), the identification of possible occlusion sites in the closed channel located among Pro-Val-Pro residues and downstream; 3), a difference in the opening and closing pathways of the channel; and 4), evidence of a transient intermediate structural substate. The results also show that likely intermediate conformations during the opening-closing process can be generated in computationally tractable simulation times.

Large-scale allosteric conformational transitions of adenylate kinase appear to involve a population-shift mechanism

Large-scale conformational changes in proteins are often associated with the binding of a substrate. Because conformational changes may be related to the function of an enzyme, understanding the kinetics and energetics of these motions is very important. We have delineated the atomically detailed conformational transition pathway of the phosphotransferase enzyme adenylate kinase (AdK) in the absence and presence of an inhibitor. The computed free energy profiles associated with conformational transitions offer detailed mechanistic insights into, as well as kinetic information on, the ligand binding mechanism. Specifically, potential of mean force calculations reveal that in the ligand-free state, there is no significant barrier separating the open and closed conformations of AdK. The enzyme samples near closed conformations, even in the absence of its substrate. The ligand binding event occurs late, toward the closed state, and transforms the free energy landscape. In the ligand-bound state, the closed conformation is energetically most favored with a large barrier to opening. These results emphasize the underlying dynamic nature of the enzyme and indicate that the conformational transitions in AdK are more intricate than a mere two-state jump between the crystal-bound and -unbound states. Based on the existence of the multiple conformations of the enzyme in the open and closed states, a different viewpoint of ligand binding is presented. Our estimated activation energy barrier for the conformational transition is also in reasonable accord with the experimental findings.

Sampling small-scale and large-scale conformational changes in proteins and molecular complexes

Sampling of small-scale and large-scale motions is important in various computational tasks, such as protein-protein docking and ligand binding. Here, we report further development and applications of the activation-relaxation technique for internal coordinate space trajectories (ARTIST). This method generates conformational moves of any complexity and size by identifying and crossing well-defined saddle points connecting energy minima. Simulations on two all-atom proteins and three protein complexes containing between 70 and 300 amino acids indicate that ARTIST opens the door to the full treatment of all degrees of freedom in dense systems such as protein-protein complexes.

Detection of multiple protein conformational ensembles in solution via deconvolution of charge-state distributions in ESI MS.

Monitoring the changes in charge-state distributions of protein ions in electrospray ionization (ESI) mass spectra has become one of the commonly accepted tools to detect large-scale conformational changes of proteins in solution. However, these experiments produce only qualitative, low-resolution information. Our goal is to develop a procedure that would produce quantitative data on protein conformational isomers coexisting in solution at equilibrium. To that end, we have examined the evolution of positive ion charge-state distributions in the