Structural Characterization of λ-Repressor Folding from All-Atom Molecular Dynamics Simulations

Peptidomimetics for CVD screened via TRADD-TRAF2 complex interface assessments

The main aim of this study is to screen and develop Peptidomimetics to treat atherosclerosis (AS) which is a Cardio Vascular Disease (CVD). Peptidomimetics were obtained from the protein-protein interaction interface of TRADD (Tumor necrosis factor receptor type 1-associated DEATH domain protein) and TRAF2 (TNF receptor-associated factor 2) complex. TRADD-TRAF2 interaction is critical in AS pathogenesis since it assists a series of signal transducers that activate NF-κB. Conceptually, the triggered NF-κB makes an extensive amount of nitric oxide (NO) synthesized by inducible nitric oxide synthase (iNOS), which boons the progress of AS. The examined TRADD-TRAF2 complex (PDB ID: 1F3V) and its interaction details revealed that the sequence range W11-G165 of TRADD highly interacts with TRAF2. The sequence range W11-G165 was selected for the design and preparation of the inhibitory peptide in silico. The selected sequence was mutated with the alanine scanning method to have a range of inhibitory peptides. With the help of different in silico tools, the top three, namely MIP11-25 L, MIP131-143 h, and MIP149-164 m peptides showed the best interaction with the critical residues of TRAF2. Thus, these three peptides were used for generating peptidomimetics using pepMMsMIMIC, a peptidomimetics virtual screening tool. Around 600 peptidomimetics were identified & and retrieved for further screening by employing molecular docking tools and MD (Molecular Dynamics) simulations. Density Functional Theory (DFT) and ADMET predictions were applied to validate the screened peptidomimetics druggability. In the results, peptidomimic compounds MMs03918858 and MMs03927281 with binding energy values of -9.6 kcal/mol and - 9.1 kcal/mol respectively were screened as the best and are proposed for further pre-clinical studies.

Microtubules as a potential platform for energy transfer in biological systems: a target for implementing individualized, dynamic variability patterns to improve organ function

Variability characterizes the complexity of biological systems and is essential for their function. Microtubules (MTs) play a role in structural integrity, cell motility, material transport, and force generation during mitosis, and dynamic instability exemplifies the variability in the proper function of MTs. MTs are a platform for energy transfer in cells. The dynamic instability of MTs manifests itself by the coexistence of growth and shortening, or polymerization and depolymerization. It results from a balance between attractive and repulsive forces between tubulin dimers. The paper reviews the current data on MTs and their potential roles as energy-transfer cellular structures and presents how variability can improve the function of biological systems in an individualized manner. The paper presents the option for targeting MTs to trigger dynamic improvement in cell plasticity, regulate energy transfer, and possibly control quantum effects in biological systems. The described system quantifies MT-dependent variability patterns combined with additional personalized signatures to improve organ function in a subject-tailored manner. The platform can regulate the use of MT-targeting drugs to improve the response to chronic therapies. Ongoing trials test the effects of this platform on various disorders.

The Role of Protein Arginine Methyltransferases in DNA Damage Response

In response to DNA damage, cells have developed a sophisticated signaling pathway, consisting of DNA damage sensors, transducers, and effectors, to ensure efficient and proper repair of damaged DNA. During this process, posttranslational modifications (PTMs) are central events that modulate the recruitment, dissociation, and activation of DNA repair proteins at damage sites. Emerging evidence reveals that protein arginine methylation is one of the common PTMs and plays critical roles in DNA damage response. Protein arginine methyltransferases (PRMTs) either directly methylate DNA repair proteins or deposit methylation marks on histones to regulate their transcription, RNA splicing, protein stability, interaction with partners, enzymatic activities, and localization. In this review, we summarize the substrates and roles of each PRMTs in DNA damage response and discuss the synergistic anticancer effects of PRMTs and DNA damage pathway inhibitors, providing insight into the significance of arginine methylation in the maintenance of genome integrity and cancer therapies.

Natural Food Colorants and Preservatives: A Review, a Demand, and a Challenge

The looming urgency of feeding the growing world population along with the increasing consumers' awareness and expectations have driven the evolution of food production systems and the processes and products applied in the food industry. Although substantial progress has been made on food additives, the controversy in which some of them are still shrouded has encouraged research on safer and healthier next generations. These additives can come from natural sources and confer numerous benefits for health, beyond serving the purpose of coloring or preserving, among others. As limiting factors, these additives are often related to stability, sustainability, and cost-effectiveness issues, which justify the need for innovative solutions. In this context, and with the advances witnessed in computers and computational methodologies for in silico experimental aid, the development of new safer and more efficient natural additives with dual functionality (colorant and preservative), for instance by the copigmentation phenomena, may be achieved more efficiently, circumventing the current difficulties.

Granger Causality Analysis of Chignolin Folding

Constantly advancing computer simulations of biomolecules provide huge amounts of data that are difficult to interpret. In particular, obtaining insights into functional aspects of macromolecular dynamics, often related to cascades of transient events, calls for methodologies that depart from the well-grounded framework of equilibrium statistical physics. One of the approaches toward the analysis of complex temporal data which has found applications in the fields of neuroscience and econometrics is Granger causality analysis. It allows determining which components of multidimensional time series are most influential for the evolution of the entire system, thus providing insights into causal relations within the dynamic structure of interest. In this work, we apply Granger analysis to a long molecular dynamics trajectory depicting repetitive folding and unfolding of a mini β-hairpin protein, CLN025. We find objective, quantitative evidence indicating that rearrangements within the hairpin turn region are determinant for protein folding and unfolding. On the contrary, interactions between hairpin arms score low on the causality scale. Taken together, these findings clearly favor the concept of zipperlike folding, which is one of two postulated β-hairpin folding mechanisms. More importantly, the results demonstrate the possibility of a conclusive application of Granger causality analysis to a biomolecular system.

Effect of Lithium Drug on Binding Affinities of Glycogen Synthase Kinase-3 β to Its Network Partners: A New Computational Approach

Finding new methods to study the effect of small molecules on protein interaction networks provides us with invaluable tools in the fields of pharmacodynamics and drug design. Lithium is an antimanic drug that has been used for the treatment of bipolar disorder for more than 60 years. Here, we utilized a new approach to study the effect of lithium as a drug on the protein interaction network of GSK-3β as a hub protein and computed the affinities of GSK-3β to its partners in the presence of lithium or sodium ions. For this purpose, ensembles of GSK-3β protein structures were created in the presence of either lithium or sodium ions using adaptive tempering molecular dynamics simulations. The protein binding patches of GSK-3β for its partners were determined, and finally, the affinity of each binding patch to the related partner was computed for structures of ensembles using a monomer-based approach. Besides, by comparing structural dynamics of GSK-3β during MD simulations in the presence of LiCl and NaCl, we suggested a new mechanism for the inhibitory effect of lithium on GSK-3β.

Dramatic Shape Changes Occur as Cytochrome c Folds

Extensive experimental studies on the folding of cytochrome c (Cyt c) make this small protein an ideal target for atomic detailed simulations for the purposes of quantitatively characterizing the structural transitions and the associated time scales for folding to the native state from an ensemble of unfolded states. We use previously generated atomically detailed folding trajectories by the stochastic difference equation in length to calculate the time-dependent changes in the small-angle X-ray scattering (SAXS) profiles. Excellent agreement is obtained between experiments and simulations for the time-dependent SAXS spectra, allowing us to identify the structures of the folding intermediates, which shows that Cyt c reaches the native state by a sequential folding mechanism. Using the ensembles of structures along the folding pathways, we show that compaction and the sphericity of Cyt c change dramatically from the prolate ellipsoid shape in the unfolded state to the spherical native state. Our data, which are in unprecedented quantitative agreement with all aspects of time-resolved SAXS experiments, show that hydrophobic collapse and amide group protection coincide on the 100 microseconds time scale, which is in accordance with ultrafast hydrogen/deuterium exchange studies. Based on these results, we propose that compaction of polypeptide chains, accompanied by dramatic shape changes, is a universal characteristic of globular proteins, regardless of the underlying folding mechanism.

A Review on Applications of Computational Methods in Drug Screening and Design

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

Choice of Adaptive Sampling Strategy Impacts State Discovery, Transition Probabilities, and the Apparent Mechanism of Conformational Changes

Interest in atomically detailed simulations has grown significantly with recent advances in computational hardware and Markov state modeling (MSM) methods, yet outstanding questions remain that hinder their widespread adoption. Namely, how do alternative sampling strategies explore conformational space and how might this influence predictions generated from the data? Here, we seek to answer these questions for four commonly used sampling methods: (1) a single long simulation, (2) many short simulations run in parallel, (3) adaptive sampling, and (4) our recently developed goal-oriented sampling algorithm, FAST. We first develop a theoretical framework for analytically calculating the probability of discovering select states on simple landscapes, where we uncover the drastic effects of varying the number and length of simulations. We then use kinetic Monte Carlo simulations on a variety of physically inspired landscapes to characterize the probability of discovering particular states and transition pathways for each of the four methods. Consistently, we find that FAST simulations discover each target state with the highest probability, while traversing realistic pathways. Furthermore, we uncover the potential pathology that short parallel simulations sometimes predict an incorrect transition pathway by crossing large energy barriers that long simulations would typically circumnavigate. We refer to this pathology as "pathway tunneling". To protect against this phenomenon when using adaptive-sampling and FAST simulations, we introduce the FAST-string method. This method enhances sampling along the highest-flux transition paths to refine an MSMs transition probabilities and discriminate between competing pathways. Additionally, we compare the performance of a variety of MSM estimators in describing accurate thermodynamics and kinetics. For adaptive sampling, we recommend simply normalizing the transition counts out of each state after adding small pseudocounts to avoid creating sources or sinks. Lastly, we evaluate whether our insights from simple landscapes hold for all-atom molecular dynamics simulations of the folding of the λ-repressor protein. Remarkably, we find that FAST-contacts predicts the same folding pathway as a set of long simulations but with orders of magnitude less simulation time.

The effects of implicit modeling of nonpolar solvation on protein folding simulations

Implicit solvent models, in which the polar and nonpolar solvation free-energies of solute molecules are treated separately, have been widely adopted for molecular dynamics simulation of protein folding. While the development of the implicit models is mainly focused on the methodological improvement and key parameter optimization for polar solvation, nonpolar solvation has been either ignored or described by a simplistic surface area (SA) model. In this work, we performed the folding simulations of multiple β-hairpin and α-helical proteins with varied surface tension coefficients embedded in the SA model to clearly demonstrate the effects of nonpolar solvation treated by a popular SA model on protein folding. The results indicate that the change in the surface tension coefficient does not alter the ability of implicit solvent simulations to reproduce a protein native structure but indeed controls the components of the equilibrium conformational ensemble and modifies the energetic characterization of the folding transition pathway. The suitably set surface tension coefficient can yield explicit solvent simulations and/or experimentally suggested folding mechanism of protein. In addition, the implicit treatment of both polar and nonpolar components of solvation free-energy contributes to the overestimation of the secondary structure in implicit solvent simulations.

Co-translational folding of α-helical proteins: structural studies of intermediate-length variants of the λ repressor

Nascent polypeptide chains fold cotranslationally, but the atomic‐level details of this process remain unknown. Here, we report crystallographic, de novo modeling, and spectroscopic studies of intermediate‐length variants of the λ repressor N‐terminal domain. Although the ranges of helical regions of the half‐length variant were almost identical to those of the full‐length protein, the relative orientations of these helices in the intermediate‐length variants differed. Our results suggest that cotranslational folding of the λ repressor initially forms a helical structure with a transient conformation, as in the case of a molten globule state. This conformation subsequently matures during the course of protein synthesis.

Database

Structural data are available in the PDB under the accession numbers http://www.rcsb.org/pdb/search/structidSearch.do?structureId=5ZCA and http://www.rcsb.org/pdb/search/structidSearch.do?structureId=3WOA.

The Surface of Protein λ6-85 Can Act as a Template for Recurring Poly(ethylene glycol) Structure

PEGylated proteins play an increasingly important role in pharmaceutical drug delivery. We recently showed that short poly(ethylene glycol) (PEG) chains can affect protein structure, even when they are not making extensive contact with the protein surface. In contrast, PEG is generally thought to form a relatively unstructured coil, and its compactness depends on solvent conditions. Here we test whether a host protein could allow PEG to form recurrent structural motifs while the PEG chain is in contact with the protein surface. We link a PEG oligomer (n = 45) to one of two nearly opposite locations on the small α-helical protein λ_6-85 to investigate this question. We first demonstrate experimentally that in these particular positions, PEG does not significantly affect the thermodynamic stability or folding kinetics of λ_6-85. We then use several all-atom molecular dynamics (MD) simulations 1 μs in duration to show how PEG equilibrates between states extending into the solvent and states packed onto the protein surface. The packing reveals recurring structures, including persistent hydrogen bond and hydrophobic contact patterns that appear multiple times. Some interactions of PEG with surface lysines are best described as an "intermittent slithering" motion of the PEG around the side chain, as seen in short MD movies. Thus, PEG achieves a variety of metastable organized structures on the protein surface, somewhere between a random globule and true folding. We also investigated the PEG-protein interaction in the unfolded state of the protein. We find that PEG has a propensity to stabilize certain helices of λ_6-85, no matter which of the two positions it was attached to. Thus, sufficiently long PEG chains are organized by the protein surface and in turn interact with certain elements of protein structure more than others, even when PEG is attached to very different sites.

Protein Folding and Structure Prediction from the Ground Up II: AAWSEM for α/β Proteins

The atomistic associative memory, water mediated, structure and energy model (AAWSEM) is an efficient coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using structural information derived from all-atom simulations of long segments of the protein. For α helical proteins, the accuracy of structure prediction using AAWSEM has been established previously. In this article, we examine the capability of AAWSEM to predict the structure of α/β proteins. We also elaborate on an iterative approach that uses the structures from a first round of AAWSEM simulation as fragment memories. This iterative scheme improves the quality of the structure prediction and makes the free energy profile more funneled toward native configurations. We explore the use of clustering analyses as a way of evaluating the confidence in various structure prediction models. Clustering using a local relative order parameter (mutual Q) of the predicted structural ensemble turns out to be optimal. The tightest cluster according to mutual Q generally has the most correctly folded structure. Since there is no bioinformatic input, AAWSEM amounts to an ab initio protein structure prediction method that combines the efficiency of coarse-grained simulations with the local structural accuracy that can be achieved from all-atom simulations.

Cooperative folding near the downhill limit determined with amino acid resolution by hydrogen exchange

The relationship between folding cooperativity and downhill, or barrier-free, folding of proteins under highly stabilizing conditions remains an unresolved topic, especially for proteins such as λ-repressor that fold on the microsecond timescale. Under aqueous conditions where downhill folding is most likely to occur, we measure the stability of multiple H bonds, using hydrogen exchange (HX) in a λYA variant that is suggested to be an incipient downhill folder having an extrapolated folding rate constant of 2 × 10(5) s(-1) and a stability of 7.4 kcal·mol(-1) at 298 K. At least one H bond on each of the three largest helices (α1, α3, and α4) breaks during a common unfolding event that reflects global denaturation. The use of HX enables us to both examine folding under highly stabilizing, native-like conditions and probe the pretransition state region for stable species without the need to initiate the folding reaction. The equivalence of the stability determined at zero and high denaturant indicates that any residual denatured state structure minimally affects the stability even under native conditions. Using our ψ analysis method along with mutational ϕ analysis, we find that the three aforementioned helices are all present in the folding transition state. Hence, the free energy surface has a sufficiently high barrier separating the denatured and native states that folding appears cooperative even under extremely stable and fast folding conditions.

Extracting conformational information from single molecule photon statistics

In this paper, we describe the approach of resonant trajectories of photon emission (Traj〈N〉 and TrajQ) in the conformational coordinate X and external field frequency ωL space to extract the conformational information of single molecule. The Smoluchowski equation is employed to describe the conformational dynamics of the single molecule in complex environments. This approach is applied to single Thioflavin T (ThT) molecule, and our results are in excellent agreement with the results of ab initio simulations.

Parallel continuous simulated tempering and its applications in large-scale molecular simulations

In this paper, we introduce a parallel continuous simulated tempering (PCST) method for enhanced sampling in studying large complex systems. It mainly inherits the continuous simulated tempering (CST) method in our previous studies [C. Zhang and J. Ma, J. Chem. Phys. 130, 194112 (2009); C. Zhang and J. Ma, J. Chem. Phys. 132, 244101 (2010)], while adopts the spirit of parallel tempering (PT), or replica exchange method, by employing multiple copies with different temperature distributions. Differing from conventional PT methods, despite the large stride of total temperature range, the PCST method requires very few copies of simulations, typically 2-3 copies, yet it is still capable of maintaining a high rate of exchange between neighboring copies. Furthermore, in PCST method, the size of the system does not dramatically affect the number of copy needed because the exchange rate is independent of total potential energy, thus providing an enormous advantage over conventional PT methods in studying very large systems. The sampling efficiency of PCST was tested in two-dimensional Ising model, Lennard-Jones liquid and all-atom folding simulation of a small globular protein trp-cage in explicit solvent. The results demonstrate that the PCST method significantly improves sampling efficiency compared with other methods and it is particularly effective in simulating systems with long relaxation time or correlation time. We expect the PCST method to be a good alternative to parallel tempering methods in simulating large systems such as phase transition and dynamics of macromolecules in explicit solvent.

Mapping fast protein folding with multiple-site fluorescent probes

Fast protein folding involves complex dynamics in many degrees of freedom, yet microsecond folding experiments provide only low-resolution structural information. We enhance the structural resolution of the five-helix bundle protein λ6-85 by engineering into it three fluorescent tryptophan-tyrosine contact probes. The probes report on distances between three different helix pairs: 1-2, 1-3, and 3-2. Temperature jump relaxation experiments on these three mutants reveal two different kinetic timescales: a slower timescale for 1-3 and a faster one for the two contacts involving helix 2. We hypothesize that these differences arise from a single folding mechanism that forms contacts on different timescales, and not from changes of mechanism due to adding the probes. To test this hypothesis, we analyzed the corresponding three distances in one published single-trajectory all-atom molecular-dynamics simulation of a similar mutant. Autocorrelation analysis of the trajectory reveals the same "slow" and "fast" distance change as does experiment, but on a faster timescale; smoothing the trajectory in time shows that this ordering is robust and persists into the microsecond folding timescale. Structural investigation of the all-atom computational data suggests that helix 2 misfolds to produce a short-lived off-pathway trap, in agreement with the experimental finding that the 1-2 and 3-2 distances involving helix 2 contacts form a kinetic grouping distinct from 1 to 3. Our work demonstrates that comparison between experiment and simulation can be extended to several order parameters, providing a stronger mechanistic test.

Temperature Accelerated Molecular Dynamics with Soft-Ratcheting Criterion Orients Enhanced Sampling by Low-Resolution Information

Many proteins exhibit an equilibrium between multiple conformations, some of them being characterized only by low-resolution information. Visiting all conformations is a demanding task for computational techniques performing enhanced but unfocused exploration of collective variable (CV) space. Otherwise, pulling a structure toward a target condition biases the exploration in a way difficult to assess. To address this problem, we introduce here the soft-ratcheting temperature-accelerated molecular dynamics (sr-TAMD), where the exploration of CV space by TAMD is coupled to a soft-ratcheting algorithm that filters the evolving CV values according to a predefined criterion. Any low resolution or even qualitative information can be used to orient the exploration. We validate this technique by exploring the conformational space of the inactive state of the catalytic domain of the adenyl cyclase AC from Bordetella pertussis. The domain AC gets activated by association with calmodulin (CaM), and the available crystal structure shows that in the complex the protein has an elongated shape. High-resolution data are not available for the inactive, CaM-free protein state, but hydrodynamic measurements have shown that the inactive AC displays a more globular conformation. Here, using as CVs several geometric centers, we use sr-TAMD to enhance CV space sampling while filtering for CV values that correspond to centers moving close to each other, and we thus rapidly visit regions of conformational space that correspond to globular structures. The set of conformations sampled using sr-TAMD provides the most extensive description of the inactive state of AC up to now, consistent with available experimental information.

Benchmarking all-atom simulations using hydrogen exchange

Long-time molecular dynamics (MD) simulations are now able to fold small proteins reversibly to their native structures [Lindorff-Larsen K, Piana S, Dror RO, Shaw DE (2011) Science 334(6055):517-520]. These results indicate that modern force fields can reproduce the energy surface near the native structure. To test how well the force fields recapitulate the other regions of the energy surface, MD trajectories for a variant of protein G are compared with data from site-resolved hydrogen exchange (HX) and other biophysical measurements. Because HX monitors the breaking of individual H-bonds, this experimental technique identifies the stability and H-bond content of excited states, thus enabling quantitative comparison with the simulations. Contrary to experimental findings of a cooperative, all-or-none unfolding process, the simulated denatured state ensemble, on average, is highly collapsed with some transient or persistent native 2° structure. The MD trajectories of this protein G variant and other small proteins exhibit excessive intramolecular H-bonding even for the most expanded conformations, suggesting that the force fields require improvements in describing H-bonding and backbone hydration. Moreover, these comparisons provide a general protocol for validating the ability of simulations to accurately capture rare structural fluctuations.

Enhanced sampling techniques in molecular dynamics simulations of biological systems

BACKGROUND

Molecular dynamics has emerged as an important research methodology covering systems to the level of millions of atoms. However, insufficient sampling often limits its application. The limitation is due to rough energy landscapes, with many local minima separated by high-energy barriers, which govern the biomolecular motion.

SCOPE OF REVIEW

In the past few decades methods have been developed that address the sampling problem, such as replica-exchange molecular dynamics, metadynamics and simulated annealing. Here we present an overview over theses sampling methods in an attempt to shed light on which should be selected depending on the type of system property studied.

MAJOR CONCLUSIONS

Enhanced sampling methods have been employed for a broad range of biological systems and the choice of a suitable method is connected to biological and physical characteristics of the system, in particular system size. While metadynamics and replica-exchange molecular dynamics are the most adopted sampling methods to study biomolecular dynamics, simulated annealing is well suited to characterize very flexible systems. The use of annealing methods for a long time was restricted to simulation of small proteins; however, a variant of the method, generalized simulated annealing, can be employed at a relatively low computational cost to large macromolecular complexes.

GENERAL SIGNIFICANCE

Molecular dynamics trajectories frequently do not reach all relevant conformational substates, for example those connected with biological function, a problem that can be addressed by employing enhanced sampling algorithms. This article is part of a Special Issue entitled Recent developments of molecular dynamics.

The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems

The OPEP coarse-grained protein model has been applied to a wide range of applications since its first release 15 years ago. The model, which combines energetic and structural accuracy and chemical specificity, allows the study of single protein properties, DNA-RNA complexes, amyloid fibril formation and protein suspensions in a crowded environment. Here we first review the current state of the model and the most exciting applications using advanced conformational sampling methods. We then present the current limitations and a perspective on the ongoing developments.

Theoretical volume profiles as a tool for probing transition states: folding kinetics

The mechanism by which conformational changes, particularly folding and unfolding, occur in proteins and other biopolymers has been widely discussed in the literature. Molecular dynamics (MD) simulations of protein folding present a formidable challenge since these conformational changes occur on a time scale much longer than what can be afforded at the current level of computational technology. Transition state (TS) theory offers a more economic description of kinetic properties of a reaction system by relating them to the properties of the TS, or for flexible systems, the TS ensemble (TSE). The application of TS theory to protein folding is limited by ambiguity in the definition of the TSE for this process. We propose to identify the TSE for conformational changes in flexible systems by comparison of its experimentally determined volumetric property, known as the volume of activation, to the structure-specific volume profile of the process calculated using MD. We illustrate this approach by its successful application to unfolding of a model chain system.

Observation of complete pressure-jump protein refolding in molecular dynamics simulation and experiment

Density is an easily adjusted variable in molecular dynamics (MD) simulations. Thus, pressure-jump (P-jump)-induced protein refolding, if it could be made fast enough, would be ideally suited for comparison with MD. Although pressure denaturation perturbs secondary structure less than temperature denaturation, protein refolding after a fast P-jump is not necessarily faster than that after a temperature jump. Recent P-jump refolding experiments on the helix bundle λ-repressor have shown evidence of a <3 μs burst phase, but also of a ~1.5 ms "slow" phase of refolding, attributed to non-native helical structure frustrating microsecond refolding. Here we show that a λ-repressor mutant is nonetheless capable of refolding in a single explicit solvent MD trajectory in about 19 μs, indicating that the burst phase observed in experiments on the same mutant could produce native protein. The simulation reveals that after about 18.5 μs of conformational sampling, the productive structural rearrangement to the native state does not occur in a single swift step but is spread out over a brief series of helix and loop rearrangements that take about 0.9 μs. Our results support the molecular time scale inferred for λ-repressor from near-downhill folding experiments, where transition-state population can be seen experimentally, and also agrees with the transition-state transit time observed in slower folding proteins by single-molecule spectroscopy.

Microsecond molecular dynamics simulation of guanidinium chloride induced unfolding of ubiquitin

All atom molecular dynamics simulations have been used to explore the atomic detail mechanism of guanidinium induced unfolding of the protein ubiquitin.

Effects of sequence and solvation on the temperature-pressure conformational landscape of proteinlike heteropolymers

We study the role of sequence and solvation in shaping the temperature-pressure (T, P) conformational landscape of model heteropolymers with a coarse-grained model. We design foldable primarily hydrophobic sequences with fixed polar content in water at physiological conditions, which demonstrate (T, P) dependence of conformational stability similar to biological proteins. Inherent helicity emerges as a result of local polar-polar interactions in the sequences that mimic biological α helices. The helical propensity is reduced upon solvation and remains unaltered at cold T and high P, which is driven by the T-P induced changes of the hydration shell. Consequently, at nonphysiological conditions the weakening of hydrophobic interactions facilitates population of non-native, helical, compact conformations stabilized through direct nonlocal interactions between polar residues.

Misplaced helix slows down ultrafast pressure-jump protein folding

Using a newly developed microsecond pressure-jump apparatus, we monitor the refolding kinetics of the helix-stabilized five-helix bundle protein λ*YA, the Y22W/Q33Y/G46,48A mutant of λ-repressor fragment 6-85, from 3 μs to 5 ms after a 1,200-bar P-drop. In addition to a microsecond phase, we observe a slower 1.4-ms phase during refolding to the native state. Unlike temperature denaturation, pressure denaturation produces a highly reversible helix-coil-rich state. This difference highlights the importance of the denatured initial condition in folding experiments and leads us to assign a compact nonnative helical trap as the reason for slower P-jump-induced refolding. To complement the experiments, we performed over 50 μs of all-atom molecular dynamics P-drop refolding simulations with four different force fields. Two of the force fields yield compact nonnative states with misplaced α-helix content within a few microseconds of the P-drop. Our overall conclusion from experiment and simulation is that the pressure-denatured state of λ*YA contains mainly residual helix and little β-sheet; following a fast P-drop, at least some λ*YA forms misplaced helical structure within microseconds. We hypothesize that nonnative helix at helix-turn interfaces traps the protein in compact nonnative conformations. These traps delay the folding of at least some of the population for 1.4 ms en route to the native state. Based on molecular dynamics, we predict specific mutations at the helix-turn interfaces that should speed up refolding from the pressure-denatured state, if this hypothesis is correct.

Native states of fast-folding proteins are kinetic traps

It has been suggested that the native state of a protein acts as a kinetic hub that can facilitate transitions between nonnative states. Using recently developed tools to quantify mediation probabilities ("hub scores"), we quantify hub-like behavior in atomic resolution trajectories for the first time. We use a data set of trajectory ensembles for 12 fast-folding proteins previously published by D. E. Shaw Research (Lindorff-Larsen, K.; et al. How Fast-Folding Proteins Fold. Science2011, 334, 517) with an aggregate simulation time of over 8.2 ms. We visualize the free-energy landscape of each molecule using configuration space networks, and show that dynamic quantities can be qualitatively understood from visual inspection of the networks. Modularity optimization is used to provide a parameter-free means of tessellating the network into a group of communities. Using hub scores, we find that the percentage of trajectories that are mediated by the native state is 31% when averaged over all molecules, and reaches a maximum of 52% for the homeodomain and chignolin. Furthermore, for these mediated transitions, we use Markov models to determine whether the native state acts as a facilitator for the transition, or as a trap (i.e., an off-pathway detour). Although instances of facilitation are found in 4 of the 12 molecules, we conclude that the native state acts primarily as a trap, which is consistent with the idea of a funnel-like landscape.

Microsecond folding experiments and simulations: a match is made

For the past two decades, protein folding experiments have been speeding up from the second or millisecond time scale to the microsecond time scale, and full-atom simulations have been extended from the nanosecond to the microsecond and even millisecond time scale. Where the two meet, it is now possible to compare results directly, allowing force fields to be validated and refined, and allowing experimental data to be interpreted in atomistic detail. In this perspective we compare recent experiments and simulations on the microsecond time scale, pointing out the progress that has been made in determining native structures from physics-based simulations, refining experiments and simulations to provide more quantitative underlying mechanisms, and tackling the problems of multiple reaction coordinates, downhill folding, and complex underlying structure of unfolded or misfolded states.

Protein dynamics via computational microscope

The purpose of this overview is to provide a concise introduction to the methodology and current advances in molecular dynamics (MD) simulations. MD simulations emerged as a powerful and popular tool to study dynamic behavior of proteins and macromolecule complexes at the atomic resolution. This approach can extend static structural data, such as X-ray crystallography, into dynamic domains with realistic timescales (up to millisecond) and high precision, therefore becoming a veritable computational microscope. This perspective covers current advances and methodology in the simulation of protein folding and drug design as illustrated by several important published examples. Overall, recent progress in the simulation field points to the direction that MD will have significant impact on molecular biology and pharmaceutical science.

To milliseconds and beyond: challenges in the simulation of protein folding

Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.

De novo prediction of protein folding pathways and structure using the principle of sequential stabilization

Motivated by the relationship between the folding mechanism and the native structure, we develop a unified approach for predicting folding pathways and tertiary structure using only the primary sequence as input. Simulations begin from a realistic unfolded state devoid of secondary structure and use a chain representation lacking explicit side chains, rendering the simulations many orders of magnitude faster than molecular dynamics simulations. The multiple round nature of the algorithm mimics the authentic folding process and tests the effectiveness of sequential stabilization (SS) as a search strategy wherein 2° structural elements add onto existing structures in a process of progressive learning and stabilization of structure found in prior rounds of folding. Because no a priori knowledge is used, we can identify kinetically significant non-native interactions and intermediates, sometimes generated by only two mutations, while the evolution of contact matrices is often consistent with experiments. Moreover, structure prediction improves substantially by incorporating information from prior rounds. The success of our simple, homology-free approach affirms the validity of our description of the primary determinants of folding pathways and structure, and the effectiveness of SS as a search strategy.