Transition State Contact Orders Correlate with Protein Folding Rates

Solvation enhances folding cooperativity and the topology dependence of folding rates in a lattice protein model

The aqueous solvent profoundly influences protein folding, yet its effects are relatively poorly understood. In this study, we investigate the impact of solvation on the folding of lattice proteins by using Monte Carlo simulations. The proteins are modeled as self-avoiding 27-mer chains on a cubic lattice, with compact native states and structure-based Gō potentials. Each residue that makes no contact with other residues in a given protein conformation is assigned a solvation energy ɛs, representing its full exposure to the solvent. We find that a negative ɛs, indicating a favorable solvation, increases the cooperativity of the folding transition by lowering the free energy of the unfolded state, increasing the folding free energy barrier, and narrowing the folding routes. This favorable solvation also significantly improves the correlation between folding rates and the native topology, measured by the relative contact order. Our results suggest that the Gō model may overestimate the importance of native interactions, and a solvation potential countering the native bias can play a significant role. The solvation energy in our model can be related to the polar interaction between water and peptide groups in the protein backbone. It is, therefore, suggested that the solvation of peptide groups may significantly contribute to the exceptional folding cooperativity and the pronounced topology-dependence of folding rates observed in two-state proteins.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

The mechanism of amyloid fibril growth from Φ-value analysis

Amyloid fibrils are highly stable misfolded protein assemblies that play an important role in several neurodegenerative and systemic diseases. Although structural information of the amyloid state is now abundant, mechanistic details about the misfolding process remain elusive. Inspired by the Φ-value analysis of protein folding, we combined experiments and molecular simulations to resolve amino-acid contacts and determine the structure of the transition-state ensemble-the rate-limiting step-for fibril elongation of PI3K-SH3 amyloid fibrils. The ensemble was validated experimentally by Tanford β analysis and computationally by free energy calculations. Although protein folding proceeds on funnel-shaped landscapes, here we find that the energy landscape for the misfolding reaction consists of a large 'golf course' region, defined by a single energy barrier and transition state, accessing a sharply funnelled region. Thus, misfolding occurs by rare, successful monomer-fibril end collisions interspersed by numerous unsuccessful binding attempts. Taken together, these insights provide a quantitative and highly resolved description of a protein misfolding reaction.

Energy Landscape of Ubiquitin Is Weakly Multidimensional

Single molecule pulling experiments report time-dependent changes in the extension (X) of a biomolecule as a function of the applied force (f). By fitting the data to one-dimensional analytical models of the energy landscape, we can extract the hopping rates between the folded and unfolded states in two-state folders as well as the height and the location of the transition state (TS). Although this approach is remarkably insightful, there are cases for which the energy landscape is multidimensional (catch bonds being the most prominent). To assess if the unfolding energy landscape in small single domain proteins could be one-dimensional, we simulated force-induced unfolding of ubiquitin (Ub) using the coarse-grained self-organized polymer-side chain (SOP-SC) model. Brownian dynamics simulations using the SOP-SC model reveal that the Ub energy landscape is weakly multidimensional (WMD), governed predominantly by a single barrier. The unfolding pathway is confined to a narrow reaction pathway that could be described as diffusion in a quasi-1D X-dependent free energy profile. However, a granular analysis using the P_fold analysis, which does not assume any form for the reaction coordinate, shows that X alone does not account for the height and, more importantly, the location of the TS. The f-dependent TS location moves toward the folded state as f increases, in accord with the Hammond postulate. Our study shows that, in addition to analyzing the f-dependent hopping rates, the transition state ensemble must also be determined without resorting to X as a reaction coordinate to describe the unfolding energy landscapes of single domain proteins, especially if they are only WMD.

Finding the needle in the haystack: towards solving the protein-folding problem computationally

Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.

Validation of Molecular Simulation: An Overview of Issues

Computer simulation of molecular systems enables structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic, or supra-molecular level. To interpret results of such simulations appropriately, the quality of the calculated properties must be evaluated. This depends on the way the simulations are performed and on the way they are validated by comparison to values Q^exp of experimentally observable quantities Q. One must consider 1) the accuracy of Q^exp , 2) the accuracy of the function Q(r^N ) used to calculate a Q-value based on a molecular configuration r^N of N particles, 3) the sensitivity of the function Q(r^N ) to the configuration r^N , 4) the relative time scales of the simulation and experiment, 5) the degree to which the calculated and experimental properties are equivalent, and 6) the degree to which the system simulated matches the experimental conditions. Experimental data is limited in scope and generally corresponds to averages over both time and space. A critical analysis of the various factors influencing the apparent degree of (dis)agreement between simulations and experiment is presented and illustrated using examples from the literature. What can be done to enhance the validation of molecular simulation is also discussed.

How determinant is N-terminal to C-terminal coupling for protein folding?

The existence of native interactions between the protein termini is a major determinant of the free energy barrier in a two-state folding transition being therefore a critical modulator of protein folding cooperativity.

Protein unfolding rates correlate as strongly as folding rates with native structure

Although the folding rates of proteins have been studied extensively, both experimentally and theoretically, and many native state topological parameters have been proposed to correlate with or predict these rates, unfolding rates have received much less attention. Moreover, unfolding rates have generally been thought either to not relate to native topology in the same manner as folding rates, perhaps depending on different topological parameters, or to be more difficult to predict. Using a dataset of 108 proteins including two-state and multistate folders, we find that both unfolding and folding rates correlate strongly, and comparably well, with well-established measures of native topology, the absolute contact order and the long range order, with correlation coefficient values of 0.75 or higher. In addition, compared to folding rates, the absolute values of unfolding rates vary more strongly with native topology, have a larger range of values, and correlate better with thermodynamic stability. Similar trends are observed for subsets of different protein structural classes. Taken together, these results suggest that choosing a scaffold for protein engineering may require a compromise between a simple topology that will fold sufficiently quickly but also unfold quickly, and a complex topology that will unfold slowly and hence have kinetic stability, but fold slowly. These observations, together with the established role of kinetic stability in determining resistance to thermal and chemical denaturation as well as proteases, have important implications for understanding fundamental aspects of protein unfolding and folding and for protein engineering and design.

Capturing protein folding-relevant topology via absolute contact order variants

Absolute contact order is one of the simplest parameters used to predict protein folding rates. Many variants of contact order (CO) have been applied to highlight different aspects of contact neighborhoods and their relationship to folding. However, a systematic study of the influence of CO variants on correlation with folding rate has not been performed for a large combined set of multi- and two-state proteins. We explore different contact neighborhoods and resulting CO by varying the distance thresholds and weighting of sequence separation for heavy atom and residue-based counting methods for a set of 136 proteins diverse across folding and structural classes. We examine the changes in contact neighborhoods and compare correlations with our CO variants and the protein folding rates across our data set as well as by folding type and structural class. Different CO variants lead to the strongest correlations within each protein structural class. Our results demonstrate that backbone topology at a distance beyond where energetic interactions dominate is able to capture folding determinants, and suggest that more sensitive methods of characterizing contact relationships may improve ln kf prediction for diverse protein sets.

Computational and theoretical methods for protein folding

A computational approach is essential whenever the complexity of the process under study is such that direct theoretical or experimental approaches are not viable. This is the case for protein folding, for which a significant amount of data are being collected. This paper reports on the essential role of in silico methods and the unprecedented interplay of computational and theoretical approaches, which is a defining point of the interdisciplinary investigations of the protein folding process. Besides giving an overview of the available computational methods and tools, we argue that computation plays not merely an ancillary role but has a more constructive function in that computational work may precede theory and experiments. More precisely, computation can provide the primary conceptual clues to inspire subsequent theoretical and experimental work even in a case where no preexisting evidence or theoretical frameworks are available. This is cogently manifested in the application of machine learning methods to come to grips with the folding dynamics. These close relationships suggested complementing the review of computational methods within the appropriate theoretical context to provide a self-contained outlook of the basic concepts that have converged into a unified description of folding and have grown in a synergic relationship with their computational counterpart. Finally, the advantages and limitations of current computational methodologies are discussed to show how the smart analysis of large amounts of data and the development of more effective algorithms can improve our understanding of protein folding.

Assessing the effect of dynamics on the closed-loop protein-folding hypothesis

The closed-loop (loop-n-lock) hypothesis of protein folding suggests that loops of about 25 residues, closed through interactions between the loop ends (locks), play an important role in protein structure. Coarse-grain elastic network simulations, and examination of loop lengths in a diverse set of proteins, each supports a bias towards loops of close to 25 residues in length between residues of high stability. Previous studies have established a correlation between total contact distance (TCD), a metric of sequence distances between contacting residues (cf. contact order), and the log-folding rate of a protein. In a set of 43 proteins, we identify an improved correlation (r² = 0.76), when the metric is restricted to residues contacting the locks, compared to the equivalent result when all residues are considered (r² = 0.65). This provides qualified support for the hypothesis, albeit with an increased emphasis upon the importance of a much larger set of residues surrounding the locks. Evidence of a similar-sized protein core/extended nucleus (with significant overlap) was obtained from TCD calculations in which residues were successively eliminated according to their hydrophobicity and connectivity, and from molecular dynamics simulations. Our results suggest that while folding is determined by a subset of residues that can be predicted by application of the closed-loop hypothesis, the original hypothesis is too simplistic; efficient protein folding is dependent on a considerably larger subset of residues than those involved in lock formation.

Atomic-level description of ubiquitin folding

Equilibrium molecular dynamics simulations, in which proteins spontaneously and repeatedly fold and unfold, have recently been used to help elucidate the mechanistic principles that underlie the folding of fast-folding proteins. The extent to which the conclusions drawn from the analysis of such proteins, which fold on the microsecond timescale, apply to the millisecond or slower folding of naturally occurring proteins is, however, unclear. As a first attempt to address this outstanding issue, we examine here the folding of ubiquitin, a 76-residue-long protein found in all eukaryotes that is known experimentally to fold on a millisecond timescale. Ubiquitin folding has been the subject of many experimental studies, but its slow folding rate has made it difficult to observe and characterize the folding process through all-atom molecular dynamics simulations. Here we determine the mechanism, thermodynamics, and kinetics of ubiquitin folding through equilibrium atomistic simulations. The picture emerging from the simulations is in agreement with a view of ubiquitin folding suggested from previous experiments. Our findings related to the folding of ubiquitin are also consistent, for the most part, with the folding principles derived from the simulation of fast-folding proteins, suggesting that these principles may be applicable to a wider range of proteins.

Take home lessons from studies of related proteins

The 'Fold Approach' involves a detailed analysis of the folding of several topologically, structurally and/or evolutionarily related proteins. Such studies can reveal determinants of the folding mechanism beyond the gross topology, and can dissect the residues required for folding from those required for stability or function. While this approach has not yet matured to the point where we can predict the native conformation of any polypeptide chain in silico, it has been able to highlight, amongst others, the specific residues that are responsible for nucleation, pathway malleability, kinetic intermediates, chain knotting, internal friction and Paracelsus switches. Some of the most interesting discoveries have resulted from the attempt to explain differences between homologues.

The folding transition state of protein L is extensive with nonnative interactions (and not small and polarized)

Progress in understanding protein folding relies heavily upon an interplay between experiment and theory. In particular, readily interpretable experimental data that can be meaningfully compared to simulations are required. According to standard mutational ϕ analysis, the transition state for Protein L contains only a single hairpin. However, we demonstrate here using ψ analysis with engineered metal ion binding sites that the transition state is extensive, containing the entire four-stranded β sheet. Underreporting of the structural content of the transition state by ϕ analysis also occurs for acyl phosphatase [Pandit, A. D., Jha, A., Freed, K. F. & Sosnick, T. R., (2006). Small proteins fold through transition states with native-like topologies. J. Mol. Biol.361, 755-770], ubiquitin [Sosnick, T. R., Dothager, R. S. & Krantz, B. A., (2004). Differences in the folding transition state of ubiquitin indicated by ϕ and ψ analyses. Proc. Natl Acad. Sci. USA 101, 17377-17382] and BdpA [Baxa, M., Freed, K. F. & Sosnick, T. R., (2008). Quantifying the structural requirements of the folding transition state of protein A and other systems. J. Mol. Biol.381, 1362-1381]. The carboxy-terminal hairpin in the transition state of Protein L is found to be nonnative, a significant result that agrees with our Protein Data Bank-based backbone sampling and all-atom simulations. The nonnative character partially explains the failure of accepted experimental and native-centric computational approaches to adequately describe the transition state. Hence, caution is required even when an apparent agreement exists between experiment and theory, thus highlighting the importance of having alternative methods for characterizing transition states.

The redundancy of NMR restraints can be used to accelerate the unfolding behavior of an SH3 domain during molecular dynamics simulations

1 Abstract

Performance assessment of different constraining potentials in computational structure prediction for disulfide-bridged proteins

The presence of disulfide bonds in proteins has very important implications on the three-dimensional structure and folding of proteins. An adequate treatment of disulfide bonds in de-novo protein simulations is therefore very important. Here we present a computational study of a set of small disulfide-bridged proteins using an all-atom stochastic search approach and including various constraining potentials to describe the disulfide bonds. The proposed potentials can easily be implemented in any code based on all-atom force fields and employed in simulations to achieve an improved prediction of protein structure. Exploring different potential parameters and comparing the structures to those from unconstrained simulations and to experimental structures by means of a scoring function we demonstrate that the inclusion of constraining potentials improves the quality of final structures significantly. For some proteins (1KVG and 1PG1) the native conformation is visited only in simulations in presence of constraints. Overall, we found that the Morse potential has optimal performance, in particular for the β-sheet proteins.

Constrained proper sampling of conformations of transition state ensemble of protein folding

Characterizing the conformations of protein in the transition state ensemble (TSE) is important for studying protein folding. A promising approach pioneered by Vendruscolo et al. [Nature (London) 409, 641 (2001)] to study TSE is to generate conformations that satisfy all constraints imposed by the experimentally measured φ values that provide information about the native likeness of the transition states. Faísca et al. [J. Chem. Phys. 129, 095108 (2008)] generated conformations of TSE based on the criterion that, starting from a TS conformation, the probabilities of folding and unfolding are about equal through Markov Chain Monte Carlo (MCMC) simulations. In this study, we use the technique of constrained sequential Monte Carlo method [Lin et al., J. Chem. Phys. 129, 094101 (2008); Zhang et al. Proteins 66, 61 (2007)] to generate TSE conformations of acylphosphatase of 98 residues that satisfy the φ-value constraints, as well as the criterion that each conformation has a folding probability of 0.5 by Monte Carlo simulations. We adopt a two stage process and first generate 5000 contact maps satisfying the φ-value constraints. Each contact map is then used to generate 1000 properly weighted conformations. After clustering similar conformations, we obtain a set of properly weighted samples of 4185 candidate clusters. Representative conformation of each of these cluster is then selected and 50 runs of Markov chain Monte Carlo (MCMC) simulation are carried using a regrowth move set. We then select a subset of 1501 conformations that have equal probabilities to fold and to unfold as the set of TSE. These 1501 samples characterize well the distribution of transition state ensemble conformations of acylphosphatase. Compared with previous studies, our approach can access much wider conformational space and can objectively generate conformations that satisfy the φ-value constraints and the criterion of 0.5 folding probability without bias. In contrast to previous studies, our results show that transition state conformations are very diverse and are far from nativelike when measured in cartesian root-mean-square deviation (cRMSD): the average cRMSD between TSE conformations and the native structure is 9.4 Å for this short protein, instead of 6 Å reported in previous studies. In addition, we found that the average fraction of native contacts in the TSE is 0.37, with enrichment in native-like β-sheets and a shortage of long range contacts, suggesting such contacts form at a later stage of folding. We further calculate the first passage time of folding of TSE conformations through calculation of physical time associated with the regrowth moves in MCMC simulation through mapping such moves to a Markovian state model, whose transition time was obtained by Langevin dynamics simulations. Our results indicate that despite the large structural diversity of the TSE, they are characterized by similar folding time. Our approach is general and can be used to study TSE in other macromolecules.

Structural analysis of an equilibrium folding intermediate in the apoflavodoxin native ensemble by small-angle X-ray scattering

Intermediate conformations are crucial to our understanding of how proteins fold into their native structures and become functional. Conventional spectroscopic measurements of thermal denaturation transitions allow the detection of equilibrium intermediates but often provide little structural detail; thus, application of more informative techniques is required. Here we used small-angle X-ray scattering (SAXS) to study the thermal denaturation of four variants of Anabaena PCC 7119 flavodoxin, including the wild-type apo and holo forms, and two mutants, E20K/E72K and F98N. Denaturation was monitored from changes in SAXS descriptors. Although the starting and final points of the denaturation were similar for the flavodoxin variants tested, substantial differences in the unfolding pathway were apparent between them. In agreement with calorimetric data, analysis of the SAXS data sets indicated a three-state unfolding equilibrium for wild-type apoflavodoxin, a two-state equilibrium for the F98N mutant, and increased thermostability of the E20K/E72K mutant and holoflavodoxin. Although the apoflavodoxin intermediate consistently appeared mixed with significant amounts of either native or unfolded conformations, its SAXS profile was derived from the deconvolution of the temperature-dependent SAXS data set. The apoflavodoxin thermal intermediate was structurally close to the native state but less compact, thereby indicating incipient unfolding. The residues that foster denaturation were explored by an ensemble of equilibrium ϕ-value restrained molecular dynamics. These simulations pointed to residues located in the cofactor and partner-protein recognition regions as the initial sites of denaturation and suggest a conformational adaptation as the mechanism of action in apoflavodoxin.

The folding of single domain proteins--have we reached a consensus?

Rather than stressing the most recent advances in the field, this review highlights the fundamental topics where disagreement remains and where adequate experimental data are lacking. These topics include properties of the denatured state and the role of residual structure, the nature of the fundamental steps and barriers, the extent of pathway heterogeneity and non-native interactions, recent comparisons between theory and experiment, and finally, dynamical properties of the folding reaction.

The nucleation mechanism of protein folding: a survey of computer simulation studies

The nucleation mechanism of protein folding, originally proposed by Baldwin in the early 1970s, was firstly observed by Shakhnovich and co-workers two decades later in the context of Monte Carlo simulations of a simple lattice model. At about the same time the extensive use of φ-value analysis provided the first experimental evidence that the folding of Chymotrypsin-inhibitor 2, a small single-domain protein, which folds with two-state kinetics, is also driven by a nucleation mechanism. Since then, the nucleation mechanism is generally considered the most common form of folding mechanism amongst two-state proteins. However, recent experimental data has put forward the idea that this may not necessarily be so, since the accuracy of the experimentally determined φ values, which are used to identify the critical (i.e. nucleating) residues, is typically poor. Here, we provide a survey of in silico results on the nucleation mechanism, ranging from simple lattice Monte Carlo to more sophisticated off-lattice molecular dynamics simulations, and discuss them in light of experimental data.

Molecular dynamics-based approaches for enhanced sampling of long-time, large-scale conformational changes in biomolecules

The rugged energy landscape of biomolecules together with shortcomings of traditional molecular dynamics (MD) simulations require specialized methods for capturing large-scale, long-time configurational changes along with chemical dynamics behavior. In this report, MD-based methods for biomolecules are surveyed, involving modification of the potential, simulation protocol, or algorithm as well as global reformulations. While many of these methods are successful at probing the thermally accessible configuration space at the expense of altered kinetics, more sophisticated approaches like transition path sampling or Markov chain models are required to obtain mechanistic information, reaction pathways, and/or reaction rates. Divide-and-conquer methods for sampling and for piecing together reaction rate information are especially suitable for readily available computer cluster networks. Successful applications to biomolecules remain a challenge.

Analysis of protein contacts into Protein Units

Three-dimensional structures of proteins are the support of their biological functions. Their folds are maintained by inter-residue interactions which are one of the main focuses to understand the mechanisms of protein folding and stability. Furthermore, protein structures can be composed of single or multiple functional domains that can fold and function independently. Hence, dividing a protein into domains is useful for obtaining an accurate structure and function determination. In previous studies, we enlightened protein contact properties according to different definitions and developed a novel methodology named Protein Peeling. Within protein structures, Protein Peeling characterizes small successive compact units along the sequence called protein units (PUs). The cutting done by Protein Peeling maximizes the number of contacts within the PUs and minimizes the number of contacts between them. This method is so a relevant tool in the context of the protein folding research and particularly regarding the hierarchical model proposed by George Rose. Here, we accurately analyze the PUs at different levels of cutting, using a non-redundant protein databank. Distribution of PU sizes, number of PUs or their accessibility are screened to determine their common and different features. Moreover, we highlight the preferential amino acid interactions inside and between PUs. Our results show that PUs are clearly an intermediate level between secondary structures and protein structural domains.

What have we learned from the studies of two-state folders, and what are the unanswered questions about two-state protein folding?

Small proteins with globular structures often fold by simple all-or-none mechanisms, both in an equilibrium and a kinetic sense, despite the very large number of partly folded conformations available. This type of 'two-state' folding will be discussed in terms of experimental tests, underlying molecular mechanisms, and limits to two-state behavior. Factors that appear to be important for two-state folding include topology (sequence distance of contacts in the native structure), molecular cooperativity and local energy distribution. Because their local stability distributions and cooperativities can be dissected and analyzed separately from topological features, recent studies of the folding of symmetric proteins will be discussed as a means to better understand the origins of two-state folding.

Generating properly weighted ensemble of conformations of proteins from sparse or indirect distance constraints

Inferring three-dimensional structural information of biomacromolecules such as proteins from limited experimental data is an important and challenging task. Nuclear Overhauser effect measurements based on nucleic magnetic resonance, disulfide linking, and electron paramagnetic resonance labeling studies can all provide useful partial distance constraint characteristic of the conformations of proteins. In this study, we describe a general approach for reconstructing conformations of biomolecules that are consistent with given distance constraints. Such constraints can be in the form of upper bounds and lower bounds of distances between residue pairs, contact maps based on specific contact distance cutoff values, or indirect distance constraints such as experimental phi-value measurement. Our approach is based on the framework of sequential Monte Carlo method, a chain growth-based method. We have developed a novel growth potential function to guide the generation of conformations that satisfy given distance constraints. This potential function incorporates not only the distance information of current residue during growth but also the distance information of future residues by introducing global distance upper bounds between residue pairs and the placement of reference points. To obtain protein conformations from indirect distance constraints in the form of experimental phi-values, we first generate properly weighted contact maps satisfying phi-value constraints, we then generate conformations from these contact maps. We show that our approach can faithfully generate conformations that satisfy the given constraints, which approach the native structures when distance constraints for all residue pairs are given.

Folding of polyglutamine chains

Long polyglutamine chains have been associated with a number of neurodegenerative diseases. These include Huntington's disease, where expanded polyglutamine (PolyQ) sequences longer than 36 residues are correlated with the onset of symptoms. In this paper we study the folding pathway of a 54-residue PolyQ chain into a beta-helical structure. Transition path sampling Monte Carlo simulations are used to generate unbiased reactive pathways between unfolded configurations and the folded beta-helical structure of the polyglutamine chain. The folding process is examined in both explicit water and an implicit solvent. Both models reveal that the formation of a few critical contacts is necessary and sufficient for the molecule to fold. Once the primary contacts are formed, the fate of the protein is sealed and it is largely committed to fold. We find that, consistent with emerging hypotheses about PolyQ aggregation, a stable beta-helical structure could serve as the nucleus for subsequent polymerization of amyloid fibrils. Our results indicate that PolyQ sequences shorter than 36 residues cannot form that nucleus, and it is also shown that specific mutations inferred from an analysis of the simulated folding pathway exacerbate its stability.

Quantifying the structural requirements of the folding transition state of protein A and other systems

The B-domain of protein A is a small three-helix bundle that has been the subject of considerable experimental and theoretical investigation. Nevertheless, a unified view of the structure of the transition-state ensemble (TSE) is still lacking. To characterize the TSE of this surprisingly challenging protein, we apply a combination of psi analysis (which probes the role of specific side-chain to side-chain contacts) and kinetic H/D amide isotope effects (which measures hydrogen-bond content), building upon previous studies using mutational phi analysis (which probes the energetic influence of side-chain substitutions). The second helix is folded in the TSE, while helix formation appears just at the carboxy and amino termini of the first and third helices, respectively. The experimental data suggest a homogenous yet plastic TS with a native-like topology. This study generalizes our earlier conclusion, based on two larger alpha/beta proteins, that the TSEs of most small proteins achieve approximately 70% of their native state's relative contact order. This high percentage limits the degree of possible TS heterogeneity and requires a reevaluation of the structural content of the TSE of other proteins, especially when they are characterized as small or polarized.

Kinetic barriers and the role of topology in protein and RNA folding

This review compares the folding behavior of proteins and RNAs. Topics covered include the role of topology in the determination of folding rates, major folding events including collapse, properties of denatured states, pathway heterogeneity, and the influence of the mode of initiation on the folding pathway.

Molecular simulation as an aid to experimentalists

Computer-based molecular simulation techniques are increasingly used to interpret experimental data on biomolecular systems at an atomic level. Direct comparison between experiment and simulation is, however, seldom straightforward. The available experimental data are limited in scope and generally correspond to averages over both time and space. A critical analysis of the various factors that may influence the apparent degree of agreement between the results of simulations and experimentally measured quantities is presented and illustrated using examples from recent literature.

Combining experiment and simulation in protein folding: closing the gap for small model systems

All-atom molecular dynamics (MD) simulations on increasingly powerful computers have been combined with experiments to characterize protein folding in detail over wider time ranges. The folding of small ultrafast folding proteins is being simulated on micros timescales, leading to improved structural predictions and folding rates. To what extent is 'closing the gap' between simulation and experiment for such systems providing insights into general mechanisms of protein folding?

Molecular dynamics simulations from putative transition states of alpha-spectrin SH3 domain

A series of molecular dynamics simulations in explicit solvent were started from nine structural models of the transition state of the SH3 domain of alpha-spectrin, which were generated by Lindorff-Larsen et al. (Nat Struct Mol Biol 2004;11:443-449) using molecular dynamics simulations in which experimental Phi - values were incorporated as restraints. Two of the nine models were simulated 10 times for 200 ns and the remaining models simulated two times for 200 ns. Complete folding was observed in one case, while in the other simulations partial folding or unfolding events were observed, which were characterized by a regularization of elements of secondary structure. These results are consistent with recent experimental evidence that the folding of SH3 domains involves low populated intermediate states.

Multiple tryptophan probes reveal that ubiquitin folds via a late misfolded intermediate

Much of our understanding of protein folding mechanisms is derived from experiments using intrinsic fluorescence of natural or genetically inserted tryptophan (Trp) residues to monitor protein refolding and site-directed mutagenesis to determine the energetic role of amino acids in the native (N), intermediate (I) or transition (T) states. However, this strategy has limited use to study complex folding reactions because a single fluorescence probe may not detect all low-energy folding intermediates. To overcome this limitation, we suggest that protein refolding should be monitored with different solvent-exposed Trp probes. Here, we demonstrate the utility of this approach by investigating the controversial folding mechanism of ubiquitin (Ub) using Trp probes located at residue positions 1, 28, 45, 57, and 66. We first show that these Trp are structurally sensitive and minimally perturbing fluorescent probes for monitoring folding/unfolding of the protein. Using a conventional stopped-flow instrument, we show that ANS and Trp fluorescence detect two distinct transitions during the refolding of all five Trp mutants at low concentrations of denaturant: T(1), a denaturant-dependent transition and T(2), a slower transition, largely denaturant-independent. Surprisingly, some Trp mutants (Ub(M1W), Ub(S57W)) display Trp fluorescence changes during T(1) that are distinct from the expected U-->N transition suggesting that the denaturant-dependent refolding transition of Ub is not a U-->N transition but represents the formation of a structurally distinct I-state (U-->I). Alternatively, this U-->I transition could be also clearly distinguished by using a combination of two Trp mutations Ub(F45W-T66W) for which the two Trp probes that display fluorescence changes of opposite sign during T(1) and T(2) (Ub(F45W-T66W)). Global fitting of the folding/unfolding kinetic parameters and additional folding-unfolding double-jump experiments performed on Ub(M1W), a mutant with enhanced fluorescence in the I-state, demonstrate that the I-state is stable, compact, misfolded, and on-pathway. These results illustrate how transient low-energy I-states can be characterized efficiently in complex refolding reactions using multiple Trp probes.