Estimation of protein folding rate from Monte Carlo simulations and entropy capacity

The problem of protein self-organization is one of the most important problems of molecular biology nowadays. Despite the recent success in the understanding of general principles of protein folding, details of this process are yet to be elucidated. Moreover, the prediction of protein folding rates has its own practical value due to the fact that aggregation directly depends on the rate of protein folding. The time of folding has been calculated for 67 proteins with known experimental data at the point of thermodynamic equilibrium between unfolded and native states using a Monte Carlo model where each residue is considered to be either folded as in the native state or completely disordered. The times of folding for 67 proteins which reach the native state within the limit of 10(8) Monte Carlo steps are in a good correlation with the experimentally measured folding rate at the mid-transition point (the correlation coefficient is -0.82). Theoretical consideration of a capillarity model for the process of protein folding demonstrates that the difference in the folding rate for proteins sharing more spherical and less spherical folds is the result of differences in the conformational entropy due to a larger surface of the boundary between folded and unfolded phases in the transition state for proteins with more spherical fold. The capillarity model allows us to predict the folding rate at the same level of correlation as by Monte Carlo simulations. The calculated model entropy capacity (conformational entropy per residue divided by the average contact energy per residue) for 67 proteins correlates by about 78% with the experimentally measured folding rate at the mid-transition point.     

Accelerated molecular dynamics simulations of protein folding

Folding of four fast‐folding proteins, including chignolin, Trp‐cage, villin headpiece and WW domain, was simulated via accelerated molecular dynamics (aMD). In comparison with hundred‐of‐microsecond timescale conventional molecular dynamics (cMD) simulations performed on the Anton supercomputer, aMD captured complete folding of the four proteins in significantly shorter simulation time. The folded protein conformations were found within 0.2–2.1 Å of the native NMR or X‐ray crystal structures. Free energy profiles calculated through improved reweighting of the aMD simulations using cumulant expansion to the second‐order are in good agreement with those obtained from cMD simulations. This allows us to identify distinct conformational states (e.g., unfolded and intermediate) other than the native structure and the protein folding energy barriers. Detailed analysis of protein secondary structures and local key residue interactions provided important insights into the protein folding pathways. Furthermore, the selections of force fields and aMD simulation parameters are discussed in detail. Our work shows usefulness and accuracy of aMD in studying protein folding, providing basic references in using aMD in future protein‐folding studies. © 2015 Wiley Periodicals, Inc.     

Basin hopping simulations for all-atom protein folding

We investigate different protocols of the basin hopping technique for de novo protein folding. Using the protein free-energy force field PFF01 we report the reproducible all-atom folding of the 20-amino-acid tryptophan-cage protein [Protein Data Bank (PDB) code: 112y] and of the recently discovered 26-amino-acid potassium channel blocker (PDB code: 1wqc), which exhibits an unusual fold. We find that simulations with increasing cycle length and random starting temperatures perform best in comparison with other parametrizations. The basin hopping technique emerges as a simple but very efficient and robust workhorse for all-atom protein folding.     

Transition network based on equilibrium sampling: a new method for extracting kinetic information from Monte Carlo simulations of protein folding

We propose a new type of transition network for modeling of protein dynamics. The nodes of the network correspond to the conformations taken from random sampling of equilibrium ensemble available, e.g., by Monte Carlo simulations. Although this approach does not provide absolute values of folding/unfolding rates, it allows identification of reaction pathways, transition state ensemble, and, eventually, intermediates. The new method is verified by a comparison with direct molecular dynamic simulations performed for a coarse-grained Gō-like model of proteins. As an illustrative example, we analyze kinetics of formation of a small β-hairpin (Trp zipper 1) in the all-atom representation.     

Solvent effect on the folding dynamics and structure of E6-associated protein characterized from ab initio protein folding simulations

Solvent effect on protein conformation and folding mechanism of E6-associated protein (E6ap) peptide are investigated using a recently developed charge update scheme termed as adaptive hydrogen bond-specific charge (AHBC). On the basis of the close agreement between the calculated helix contents from AHBC simulations and experimental results, we observed based on the presented simulations that the two ends of the peptide may simultaneously take part in the formation of the helical structure at the early stage of folding and finally merge to form a helix with lowest backbone RMSD of about 0.9 A? in 40% 2,2,2-trifluoroethanol solution. However, in pure water, the folding may start at the center of the peptide sequence instead of at the two opposite ends. The analysis of the free energy landscape indicates that the solvent may determine the folding clusters of E6ap, which subsequently leads to the different final folded structure. The current study demonstrates new insight to the role of solvent in the determination of protein structure and folding dynamics.     

Free energy for protein folding from nonequilibrium simulations using the Jarzynski equality

The equilibrium free energy difference between two long-lived molecular species or "conformational states" of a protein (or any other molecule) can in principle be estimated by measuring the work needed to shuttle the system between them, independent of the irreversibility of the process. This is the meaning of the Jarzynski equality (JE), which we test in this paper by performing simulations that unfold a protein by pulling two atoms apart. Pulling is performed fast relative to the relaxation time of the molecule and is thus far from equilibrium. Choosing a simple protein model for which we can independently compute its equilibrium properties, we show that the free energy can be exactly and effectively estimated from nonequilibrium simulations. To do so, one must carefully and correctly determine the ensemble of states that are pulled, which is more important the farther from equilibrium one performs simulations; this highlights a potential problem in using the JE to extract the free energy from forced unfolding experiments. The results presented here also demonstrate that the free energy difference between the native and denatured states of a protein measured in solution is not always equal to the free energy profile that can be estimated from forced unfolding simulations (or experiments) using the JE.     

Topology-based models and NMR structures in protein folding simulations

Topology-based interaction potentials are simplified models that use the native contacts in the folded structure of a protein to define an energetically unfrustrated folding funnel. They have been widely used to analyze the folding transition and pathways of different proteins through computer simulations. Obviously, they need a reliable, experimentally determined folded structure to define the model interactions. In structures elucidated through NMR spectroscopy, a complex treatment of the raw experimental data usually provides a series of models, a set of different conformations compatible with the available experimental data. Here, we use an efficient coarse-grained simulation technique to independently consider the contact maps from every different NMR model in a protein whose structure has been resolved by the use of NMR spectroscopy. For lambda-Cro repressor, a homodimeric protein, we have analyzed its folding characteristics with a topology-based model. We have focused on the competition between the folding of the individual chains and their binding to form the final quaternary structure. From 20 different NMR models, we find a predominant three-state folding behavior, in agreement with experimental data on the folding pathway for this protein. Individual NMR models, however, show distinct characteristics, which are analyzed both at the level of the interplay between tertiary/quaternary structure formation and also regarding the thermal stability of the tertiary structure of every individual chain.     

All-atom structure prediction and folding simulations of a stable protein

We present results from all-atom, fully unrestrained ab initio folding simulations for a stable protein with nontrivial secondary structure elements and a hydrophobic core. The construct, "trpcage", is a 20-residue sequence optimized by the Andersen group at University of Washington and is currently the smallest protein that displays two-state folding properties. Compared over the well-defined regions of the experimental structure, our prediction has a remarkably low 0.97 A Calpha root-mean-square-deviation (rmsd) and 1.4 A for all heavy atoms. The simulated structure family displays additional features that are suggested by experimental data, yet are not evident in the family of NMR-derived structures.     

Use of a novel Hill-climbing genetic algorithm in protein folding simulations

We have developed a novel Hill-climbing genetic algorithm (GA) for simulation of protein folding. The program (written in C) builds a set of Cartesian points to represent an unfolded polypeptide’s backbone. The dihedral angles determining the chain’s configuration are stored in an array of chromosome structures that is copied and then mutated. The fitness of the mutated chain’s configuration is determined by its radius of gyration.

A four-helix bundle was used to optimise simulation conditions, and the program was compared with other, larger, genetic algorithms on a variety of structures. The program ran 50% faster than other GA programs. Overall, tests on 100 non-redundant structures gave comparable results to other genetic algorithms, with the Hill-climbing program running from between 20 and 50% faster. Examples including crambin, cytochrome c, cytochrome B and hemerythrin gave good secondary structure fits with overall alpha carbon atom rms deviations of between 5 and 5.6 Å with an optimised hydrophobic term in the fitness function.     

Multiple molecule effects on the cooperativity of protein folding transitions in simulations

Though molecular simulation of proteins has made notable contributions to the study of protein folding and kinetics, disagreement between simulation and experiment still exists. One of the criticisms levied against simulation is its failure to reproduce cooperative protein folding transitions. This weakness has been attributed to many factors such as a lack of polarizability and adequate capturing of solvent effects. This work, however, investigates how increasing the number of proteins simulated simultaneously can affect the cooperativity of folding transitions--a topic that has received little attention previously. Two proteins are studied in this work: phage T4 lysozyme (Protein Data Bank (PDB) ID: 7LZM) and phage 434 repressor (PDB ID: 1R69). The results show that increasing the number of proteins molecules simulated simultaneously leads to an increase in the macroscopic cooperativity for transitions that are inherently cooperative on the molecular level but has little effect on the cooperativity of other transitions. Taken as a whole, the results identify one area of consideration to improving simulations of protein folding.     

Experimental resolution of early steps in protein folding: testing molecular dynamics simulations

Time-resolved Tyr fluorescence spectroscopy coupled with a laser-induced temperature-jump (T-jump) was employed to follow the folding relaxation dynamics of the B-domain of Staphylococcal protein A. The single Tyr is located in helix 1 (H1) and is a sensitive probe of the structure of this helix and the overall helical bundle structure. The results from this study were compared to those from a complementary infrared T-jump study on this protein [Vu, D. M.; Myers, J. K.; Oas, T. G.; Dyer, R. B. Biochemistry 2004, 43, 3582]. Both methods detect a microsecond process that follows the cooperative relaxation of the helical bundle core. However, a fast process (10-7 s) that follows the relaxation of the individual helices was observed only with the infrared probe. Thus, fast formation of H1 is not observed, but rather H1 forms in the microsecond phase, concomitantly with the docking to (and stabilization by) the other two helices to form the helical bundle structure. This observation validates the results of several previous molecular dynamics simulations that predict H1 formation only in the final assembly of the helix bundle.     

Simulation studies of protein folding/unfolding equilibrium under polar and nonpolar confinement

We study the equilibrium folding/unfolding thermodynamics of a small globular miniprotein, the Trp cage, that is confined to the interior of a 2 nm radius fullerene ball. The interactions of the fullerene surface are changed from nonpolar to polar to mimic the interior of the GroEL/ES chaperonin that assists proteins to fold in vivo. We find that nonpolar confinement stabilizes the folded state of the protein due to the effects of volume reduction that destabilize the unfolded state and also due to interactions with the fullerene surface. For the Trp cage, polar confinement has a net destabilizing effect that results from the stabilizing confinement and the competitive exclusion effect that keeps the protein away from the surface hydration shell and stronger interactions between charged side chains in the protein and the polar surface that compete against the formation of an ion pair that stabilizes the protein folded state. We show that confinement effects due to volume reduction can be overcome by sequence-specific interactions of the protein side chains with the encapsulating surface. This study shows that there is a complex balance among many competing effects that determine the mechanism of GroEL chaperonin in enhancing the folding rate of polypeptide inside its cavity.     

Simple continuous and discrete models for simulating replica exchange simulations of protein folding

The efficiency of temperature replica exchange (RE) simulations hinge on their ability to enhance conformational sampling at physiological temperatures by taking advantage of more rapid conformational interconversions at higher temperatures. While temperature RE is a parallel simulation technique that is relatively straightforward to implement, kinetics in the RE ensemble is complicated, and there is much to learn about how best to employ RE simulations in computational biophysics. Protein folding rates often slow down above a certain temperature due to entropic bottlenecks. This "anti-Arrhenius" behavior represents a challenge for RE. However, it is far from straightforward to systematically explore the impact of this on RE by brute force molecular simulations, since RE simulations of protein folding are very difficult to converge. To understand some of the basic mechanisms that determine the efficiency of RE, it is useful to study simplified low dimensionality systems that share some of the key characteristics of molecular systems. Results are presented concerning the efficiency of temperature RE on a continuous two-dimensional potential that contains an entropic bottleneck. Optimal efficiency was obtained when the temperatures of the replicas did not exceed the temperature at which the harmonic mean of the folding and unfolding rates is maximized. This confirms a result we previously obtained using a discrete network model of RE. Comparison of the efficiencies obtained using the continuous and discrete models makes it possible to identify non-Markovian effects, which slow down equilibration of the RE ensemble on the more complex continuous potential. In particular, the rate of temperature diffusion and also the efficiency of RE is limited by the time scale of conformational rearrangements within free energy basins.     

Hierarchy of folding and unfolding events of protein G, CI2, and ACBP from explicit-solvent simulations

The study of the mechanism which is at the basis of the phenomenon of protein folding requires the knowledge of multiple folding trajectories under biological conditions. Using a biasing molecular-dynamics algorithm based on the physics of the ratchet-and-pawl system, we carry out all-atom, explicit solvent simulations of the sequence of folding events which proteins G, CI2, and ACBP undergo in evolving from the denatured to the folded state. Starting from highly disordered conformations, the algorithm allows the proteins to reach, at the price of a modest computational effort, nativelike conformations, within a root mean square deviation (RMSD) of approximately 1 A?. A scheme is developed to extract, from the myriad of events, information concerning the sequence of native contact formation and of their eventual correlation. Such an analysis indicates that all the studied proteins fold hierarchically, through pathways which, although not deterministic, are well-defined with respect to the order of contact formation. The algorithm also allows one to study unfolding, a process which looks, to a large extent, like the reverse of the major folding pathway. This is also true in situations in which many pathways contribute to the folding process, like in the case of protein G.     

Application of the accelerated molecular dynamics simulations to the folding of a small protein

In this paper, we further explore the applicability of the accelerated molecular dynamics simulation method using a bias potential. The method is applied to both simple model systems and real multidimensional systems. The method is also compared to replica exchange simulations in folding a small protein, Trp cage, using an all atom potential for the protein and an implicit model for the solvent. We show that the bias potential method allows quick searches of folding pathways. We also show that the choice of the bias potential has significant influence on the efficiency of the bias potential method.     

Dominant folding pathways of a peptide chain from ab initio quantum-mechanical simulations

Using the dominant reaction pathways method, we perform an ab initio quantum-mechanical simulation of a conformational transition of a peptide chain. The method we propose makes it possible to investigate the out-of-equilibrium dynamics of these systems, without resorting to an empirical representation of the molecular force field. It also allows to study rare transitions involving rearrangements in the electronic structure. By comparing the results of the ab initio simulation with those obtained by employing a standard force field, we discuss its capability to describe the nonequilibrium dynamics of conformational transitions.     

Determining equilibrium constants for dimerization reactions from molecular dynamics simulations

With today's available computer power, free energy calculations from equilibrium molecular dynamics simulations "via counting" become feasible for an increasing number of reactions. An example is the dimerization reaction of transmembrane alpha-helices. If an extended simulation of the two helices covers sufficiently many dimerization and dissociation events, their binding free energy is readily derived from the fraction of time during which the two helices are observed in dimeric form. Exactly how the correct value for the free energy is to be calculated, however, is unclear, and indeed several different and contradictory approaches have been used. In particular, results obtained via Boltzmann statistics differ from those determined via the law of mass action. Here, we develop a theory that resolves this discrepancy. We show that for simulation systems containing two molecules, the dimerization free energy is given by a formula of the form ΔG ∝ ln(P(1) /P(0) ). Our theory is also applicable to high concentrations that typically have to be used in molecular dynamics simulations to keep the simulation system small, where the textbook dilute approximations fail. It also covers simulations with an arbitrary number of monomers and dimers and provides rigorous error estimates. Comparison with test simulations of a simple Lennard Jones system with various particle numbers as well as with reference free energy values obtained from radial distribution functions show full agreement for both binding free energies and dimerization statistics.     

Thermal conductivity of molten alkali halides from equilibrium molecular dynamics simulations

The thermal conductivity of molten sodium chloride and potassium chloride has been computed through equilibrium molecular dynamics Green-Kubo simulations in the microcanonical ensemble (N,V,E). In order to access the temperature dependence of the thermal conductivity coefficient of these materials, the simulations were performed at five different state points. The form of the microscopic energy flux for ionic systems whose Coulombic interactions are calculated through the Ewald method is discussed in detail and an efficient formula is used by analogy with the methods used to evaluate the stress tensor in Coulombic systems. The results show that the Born-Mayer-Huggins-Tosi-Fumi potential predicts a weak negative temperature dependence for the thermal conductivity of NaCl and KCl. The simulation results are in agreement with part of the experimental data available in the literature with simulation values generally overpredicting the thermal conductivity by 10%-20%.     

Molecular dynamics simulations of proteins and peptides: from folding to drug design

Computer simulations of proteins, lipids and nucleic acids at equilibrium have become essentially routine. However, the fact remains that complete sampling of conformational space continues to be a bottle-neck in the field. The challenge for the future is to overcome such problems and use computational approaches to understand recognition and spontaneous self-organization in biomolecular systems (folding, aggregation and assembly of complexes), processes that cannot be directly observed experimentally. In this review, examples illustrating the extent to which simulations can be used to understand these phenomena in biomolecular systems will be presented along with examples of methodological developments to increase our physical understanding of the processes. The study cases will cover the problems of peptide-receptor recognition and the use of the information obtained for the design of new non-peptidic ligands; the study of the folding mechanism of small proteins and finally the study of the initial stages of peptide self-aggregation.