Comment on: "Energy landscape of a small peptide revealed by dihedral angle principal component analysis"

The dihedral angle principal component analysis method published recently by Mu, Nguyen, and Stock, is shown to produce distortions of the free energy landscape due to the neglect of constraints in the coordinates. It is further shown that these distortions can create artificial minima and energy barriers. The rugged energy landscape that the authors find for a small peptide chain might thus be an artifact of their method.     

Stretching the immunoglobulin 27 domain of the titin protein: the dynamic energy landscape

Molecular simulations are carried out on the Immunoglobulin 27 domain of the titin protein. The energy landscape is mapped out using an implicit solvent model, and molecular dynamics simulations are run with the solvent explicitly modeled. Stretching a protein is shown to produce a dynamic energy landscape in which the energy minima move in configuration space, change in depth, and are created and destroyed. The connections of these landscape changes to the mechanical unfolding of the Immunoglobulin 27 domain are addressed. Hydrogen bonds break upon stretching by either intrabasin processes associated with the movement of energy minima, or interbasin processes associated with transitions between energy minima. Intrabasin changes are reversible and dominate for flexible interactions, whereas interbasin changes are irreversible and dominate for stiff interactions. The most flexible interactions are Glu-Lys salt bridges, which can act like tethers to bind strands even after all backbone interactions between the strands have been broken. As the protein is stretched, different types of structures become the lowest energy structures, including structures that incorporate nonnative hydrogen bonds. Structures that have flat energy versus elongation profiles become the lowest energy structures at elongations of several Angstroms, and are associated with the unfolding intermediate state observed experimentally.     

Energy landscape of a small peptide revealed by dihedral angle principal component analysis

A 100 ns molecular dynamics simulation of penta-alanine in explicit water is performed to study the reversible folding and unfolding of the peptide. Employing a standard principal component analysis (PCA) using Cartesian coordinates, the resulting free-energy landscape is found to have a single minimum, thus suggesting a simple, relatively smooth free-energy landscape. Introducing a novel PCA based on a transformation of the peptide dihedral angles, it is found, however, that there are numerous free energy minima of comparable energy (less than or approximately 1 kcal/mol), which correspond to well-defined structures with characteristic hydrogen-bonding patterns. That is, the true free-energy landscape is actually quite rugged and its smooth appearance in the Cartesian PCA represents an artifact of the mixing of internal and overall motion. Well-separated minima corresponding to specific conformational structures are also found in the unfolded part of the free energy landscape, revealing that the unfolded state of penta-alanine is structured rather than random. Performing a connectivity analysis, it is shown that neighboring states are connected by low barriers of similar height and that each state typically makes transitions to three or four neighbor states. Several principal pathways for helix nucleation are identified and discussed in some detail.     

Evolutionary computer programming of protein folding and structure predictions

In order to understand the mechanism of protein folding and to assist the rational de-novo design of fast-folding, non-aggregating and stable artificial enzymes it is very helpful to be able to simulate protein folding reactions and to predict the structures of proteins and other biomacromolecules. Here, we use a method of computer programming called "evolutionary computer programming" in which a program evolves depending on the evolutionary pressure exerted on the program. In the case of the presented application of this method on a computer program for folding simulations, the evolutionary pressure exerted was towards faster finding deep minima in the energy landscape of protein folding. Already after 20 evolution steps, the evolved program was able to find deep minima in the energy landscape more than 10 times faster than the original program prior to the evolution process.     

Diffusion of glycerol through Escherichia coli aquaglyceroporin GlpF

The glycerol uptake facilitator, GlpF, a major intrinsic protein found in Escherichia coli, selectively conducts water and glycerol across the inner membrane. The free energy landscape characterizing the assisted transport of glycerol by this homotetrameric aquaglyceroporin has been explored by means of equilibrium molecular dynamics over a timescale spanning 0.12 μs. To overcome the free energy barriers of the conduction pathway, an adaptive biasing force is applied to the glycerol molecule confined in each of the four channels. The results illuminate the critical role played by intramolecular relaxation on the diffusion properties of the permeant. These free energy calculations reveal that glycerol tumbles and isomerizes on a timescale comparable to that spanned by its adaptive-biasing-force-assisted conduction in GlpF. As a result, reorientation and conformational equilibrium of glycerol in GlpF constitute a bottleneck in the molecular simulations of the permeation event. A profile characterizing the position-dependent diffusion of the permeant has been determined, allowing reaction rate theory to be applied for investigating conduction kinetics based on the measured free energy landscape.     

A knowledge-based move set for protein folding

The free energy landscape of protein folding is rugged, occasionally characterized by compact, intermediate states of low free energy. In computational folding, this landscape leads to trapped, compact states with incorrect secondary structure. We devised a residue-specific, protein backbone move set for efficient sampling of protein-like conformations in computational folding simulations. The move set is based on the selection of a small set of backbone dihedral angles, derived from clustering dihedral angles sampled from experimental structures. We show in both simulated annealing and replica exchange Monte Carlo (REMC) simulations that the knowledge-based move set, when compared with a conventional move set, shows statistically significant improved ability at overcoming kinetic barriers, reaching deeper energy minima, and achieving correspondingly lower RMSDs to native structures. The new move set is also more efficient, being able to reach low energy states considerably faster. Use of this move set in determining the energy minimum state and for calculating thermodynamic quantities is discussed.     

The Energy Landscape,Folding Pathways and the Kinetics of a Knotted Protein

The folding pathway and rate coefficients of the folding of a knotted protein are calculated for a potential energy function with minimal energetic frustration. A kinetic transition network is constructed using the discrete path sampling approach, and the resulting potential energy surface is visualized by constructing disconnectivity graphs. Owing to topological constraints, the low-lying portion of the landscape consists of three distinct regions, corresponding to the native knotted state and to configurations where either the N or C terminus is not yet folded into the knot. The fastest folding pathways from denatured states exhibit early formation of the N terminus portion of the knot and a rate-determining step where the C terminus is incorporated. The low-lying minima with the N terminus knotted and the C terminus free therefore constitute an off-pathway intermediate for this model. The insertion of both the N and C termini into the knot occurs late in the folding process, creating large energy barriers that are the rate limiting steps in the folding process. When compared to other protein folding proteins of a similar length, this system folds over six orders of magnitude more slowly.     

Hierarchic finite level energy landscape model: to describe the refolding kinetics of phosphoglycerate kinase

One of the most intriguing predictions of energy landscape models is the existence of non-exponential protein folding kinetics caused by hierarchical structures in the landscapes. Here we provide the strongest evidence so far of such hierarchy and determine the time constants and weights of the kinetic components of the suggested hierarchic energy landscape. To our knowledge, the idea of hierarchical folding energy barriers has never been tested over such a broad timescale. Refolding of yeast phosphoglycerate kinase was initiated from the guanidine-unfolded state by stopped-flow or manual mixing and monitored by tryptophan fluorescence from 1 ms to 15 min. The strategy to build a model that describes folding of yeast phosphoglycerate kinase was to start from the simplest paradigm and modify it stepwise to the necessary minimal extent after repeated comparisons with the experiments. We made no a priori assumptions about the folding landscape. The result was a hierarchic finite level landscape model that quantitatively describes the refolding of yeast phosphoglycerate kinase from 1 ms to 15 min. The early steps of the folding process happen in the upper region of the landscape, where the surface has a hierarchic structure. This leads to stretched kinetics in the early phase of the folding. The lower region of the energy landscape is dominated by a trap that reflects the accumulation of molten globule intermediate state. From this intermediate, the protein can reach the global energy minimum corresponding to the native state through a cross-barrier folding step.     

Protein Collective Motions Coupled to Ligand Migration in Myoglobin

Ligand migration processes inside myoglobin and protein dynamics coupled to the migration were theoretically investigated with molecular dynamics simulations. Based on a linear response theory, we identified protein motions coupled to the transient migration of ligand, carbon monoxide (CO), through channels. The result indicates that the coupled protein motions involve collective motions extended over the entire protein correlated with local gating motions at the channels. Protein motions, coupled to opening of a channel from the distal pocket to a neighboring xenon site, were found to share the collective motion with experimentally observed protein motions coupled to a doming motion of the heme Fe atom upon photodissociation of the ligand. Analysis based on generalized Langevin dynamics elucidated slow and diffusive features of the protein response motions. Remarkably small transmission coefficients for rates of the CO migrations through myoglobin were found, suggesting that the CO migration dynamics are characterized as motions governed by the protein dynamics involving the collective motions, rather than as thermally activated transitions across energy barriers of well-structured channels.     

Ruggedness in the Free Energy Landscape Dictates Misfolding of the Prion Protein

Experimental determination of the key features of the free energy landscapes of proteins, which dictate their adeptness to fold correctly, or propensity to misfold and aggregate and which are modulated upon a change from physiological to aggregation-prone conditions, is a difficult challenge. In this study, sub-millisecond kinetic measurements of the folding and unfolding of the mouse prion protein reveal how the free energy landscape becomes more complex upon a shift from physiological (pH 7) to aggregation-prone (pH 4) conditions. Folding and unfolding utilize the same single pathway at pH 7, but at pH 4, folding occurs on a pathway distinct from the unfolding pathway. Moreover, the kinetics of both folding and unfolding at pH 4 depend not only on the final conditions but also on the conditions under which the processes are initiated. Unfolding can be made to switch to occur on the folding pathway by varying the initial conditions. Folding and unfolding pathways appear to occupy different regions of the free energy landscape, which are separated by large free energy barriers that change with a change in the initial conditions. These barriers direct unfolding of the native protein to proceed via an aggregation-prone intermediate previously identified to initiate the misfolding of the mouse prion protein at low pH, thus identifying a plausible mechanism by which the ruggedness of the free energy landscape of a protein may modulate its aggregation propensity.     

Fluctuations and correlations in crystalline protein dynamics: a simulation analysis of staphylococcal nuclease

Understanding collective motions in protein crystals is likely to furnish insight into functional protein dynamics and will improve models for refinement against diffraction data. Here, four 10 ns molecular dynamics simulations of crystalline Staphylococcal nuclease are reported and analyzed in terms of fluctuations and correlations in atomic motion. The simulation-derived fluctuations strongly correlate with, but are slightly higher than, the values derived from the experimental B-factors. Approximately 70% of the atomic fluctuations are due to internal protein motion. For 65% of the protein atoms the internal fluctuations converge on the nanosecond timescale. Convergence is much slower for the elements of the interatomic displacement correlation matrix--of these, >80% converge within 1 ns for interatomic distances less, approximately <6 A, but only 10% for separations approximately =12 A. Those collective motions that converged on the nanosecond timescale involve mostly correlations within the beta-barrel or between alpha-helices of the protein. The R-factor with the experimental x-ray diffuse scattering for the crystal, which is determined by the displacement variance-covariance matrix, decreases to 8% after 10 ns simulation. Both the number of converged correlation matrix elements and the R-factor depend logarithmically on time, consistent with a model in which the number of energy minima sampled depends exponentially on the maximum energy barrier crossed. The logarithmic dependence is also extrapolated to predict a convergence time for the whole variance-covariance matrix of approximately 1 micros.     

Stretch activation and nonlinear elasticity of muscle cross-bridges.

When active insect fibrillar flight muscle is stretched, its ATPase rate increases and it develops "negative viscosity," which allows it to perform oscillatory work. We use a six-state model for the cross-bridge cycle to show that such "stretch activation" may arise naturally as a nonlinear property of a cross-bridge interacting with a single attachment site on a thin filament. Attachment is treated as a thermally activated process in which elastic energy must be supplied to stretch or compress the cross-bridge spring. We find that stretch activation occurs at filament displacements where, before the power stroke, the spring is initially in compression rather than in tension. In that case, pulling the filaments relieves the initial compression and reduces the elastic energy required for attachment. The result is that the attachment rate is enhanced by stretching. The model also displays the "delayed tension" effect observed in length-step experiments. When the muscle is stretched suddenly, the power stroke responds very quickly, but there is a time lag before dissociation at the end of the cycle catches up with the increased attachment rate. This lag is responsible for the delayed tension and hence also for the negative viscosity.     

Structure optimization of the two-dimensional off-lattice hydrophobic–hydrophilic model

A two-dimensional off-lattice protein model with two species of monomers, hydrophobic and hydrophilic, was studied. Low-energy configurations in the model were optimized using the improved energy landscape paving (ELP+) method. In ELP+, the energy landscape paving (ELP) was first applied to search for the low-energy states. After the ELP led to the basins of the local energy minima, the additional degree-of-freedom of bond length was introduced, and the gradient descent method was then used to search for lower energy states near the local minima. Numerical results show that the proposed methods are quite effective for finding the ground states of proteins. A comparison between ELP+ and other methods is made.     

A novel approach to decoy set generation: designing a physical energy function having local minima with native structure characteristics

We suggest a new approach to the generation of candidate structures (decoys) for ab initio prediction of protein structures. Our method is based on random sampling of conformation space and subsequent local energy minimization. At the core of this approach lies the design of a novel type of energy function. This energy function has local minima with native structure characteristics and wide basins of attraction. The current work presents our motivation for deriving such an energy function and also tests the derived energy function.Our approach is novel in that it takes advantage of the inherently rough energy landscape of proteins, which is generally considered a major obstacle for protein structure prediction. When local minima have wide basins of attraction, the protein's conformation space can be greatly reduced by the convergence of large regions of the space into single points, namely the local minima corresponding to these funnels. We have implemented this concept by an iterative process. The potential is first used to generate decoy sets and then we study these sets of decoys to guide further development of the potential. A key feature of our potential is the use of cooperative multi-body interactions that mimic the role of the entropic and solvent contributions to the free energy.The validity and value of our approach is demonstrated by applying it to 14 diverse, small proteins. We show that, for these proteins, the size of conformation space is considerably reduced by the new energy function. In fact, the reduction is so substantial as to allow efficient conformational sampling. As a result we are able to find a significant number of near-native conformations in random searches performed with limited computational resources.     

A small tripeptide AFA undergoes two state cooperative conformational transitions: implications for conformational biases in unfolded states

It is important to understand the conformational biases that are present in unfolded states to understand protein folding. In this context, it is surprising that even a short tripeptide like AFA samples folded/ordered conformation as demonstrated recently by NMR experiments of the peptide in aqueous solution at 280 K. In this paper, we present molecular dynamics simulation of the peptide in explicit water using OPLS-AA/L all-atom force field. The results are in overall agreement with NMR results and provide some further insights. The peptide samples turn and extended conformational forms corresponding to minima in free energy landscape. Frequent transitions between the minima are observed due to modest free energy barriers. The turn conformation seems to be stabilized by hydrophobic interactions and possibly by bridging water molecules between backbone donors and acceptors. Thus the peptide does not sample conformations randomly, but samples well defined conformations. The peptide served as a model for folding-unfolding equilibrium in the context of peptide folding. Further, implications for drug design are also discussed.     

Conformational heterogeneity of an equilibrium folding intermediate quantified and mapped by scanning mutagenesis

It is challenging to experimentally define an energy landscape for protein folding that comprises multiple partially unfolded states. Experimental results are often ambiguous as to whether a non-native state is conformationally homogeneous. Here, we tested an approach combining systematic mutagenesis and a Br?nsted-like analysis to reveal and quantify conformational heterogeneity of folding intermediate states. Using this method, we resolved an otherwise apparently homogeneous equilibrium folding intermediate of Borrelia burgdorferi OspA into two conformationally distinct species and determined their relative populations. Furthermore, we mapped the structural differences between these intermediate species, which are consistent with the non-native species that we previously proposed based on native-state hydrogen exchange studies. When treated as a single state, the intermediate ensemble exhibited fractional Phi-values for mutations and Hammond-type behaviors that are often observed for folding transition states. We found that a change in relative population of the two species within the intermediate ensemble explains these properties well, suggesting that fractional Phi-values and Hammond-type behaviors exhibited by folding intermediates and transition states may arise more often from conformational heterogeneity than from a single partial structure. Our results are consistent with the presence of multiple minima in a rugged energy landscape predicted from theoretical studies. The method described here provides a promising means to probe a complex folding energy landscape.     

Leucine-rich hydrophobic clusters promote folding of the N-terminus of the intrinsically disordered transactivation domain of p53

Molecular dynamics simulations have been performed on the intrinsically disordered 39-residue N-terminal transactivation domain of p53 (p53_1-39). Simulations not only revealed that p53_1-39 is natively compact, but also possesses a folded structure. Furthermore, leucine-rich hydrophobic clusters were found to play a crucial role in the formation and stabilization of the folded structure of p53_1-39. Collapsing in the sub-microsecond timescale might allow for rapid conformational turnovers of p53_1-39, necessary for its efficient transactivation activity and modulation. Fast collapsing might be the result of unique conformational landscapes, featuring several energy minima separated by small energy barriers. It is suggested that IDPs with highly specialized functions in the cell, such as transactivation, possibly display more ordered patterns than their less specialized counterparts.     

Inversion of the balance between hydrophobic and hydrogen bonding interactions in protein folding and aggregation

Identifying the forces that drive proteins to misfold and aggregate, rather than to fold into their functional states, is fundamental to our understanding of living systems and to our ability to combat protein deposition disorders such as Alzheimer's disease and the spongiform encephalopathies. We report here the finding that the balance between hydrophobic and hydrogen bonding interactions is different for proteins in the processes of folding to their native states and misfolding to the alternative amyloid structures. We find that the minima of the protein free energy landscape for folding and misfolding tend to be respectively dominated by hydrophobic and by hydrogen bonding interactions. These results characterise the nature of the interactions that determine the competition between folding and misfolding of proteins by revealing that the stability of native proteins is primarily determined by hydrophobic interactions between side-chains, while the stability of amyloid fibrils depends more on backbone intermolecular hydrogen bonding interactions.