Effect of ionized protein residues on the nucleation pathway of protein folding

Using a ternary nucleation formalism, we have recently [Y. S. Djikaev and E. Ruckenstein, J. Chem. Phys. 126, 175103 (2007)] proposed a kinetic model for the nucleation mechanism of protein folding. A protein was considered as a heteropolymer consisting of hydrophobic, hydrophilic, and neutral beads with all the bonds having the same constant length and all the bond angles equal and fixed. In this paper, we further develop that model by taking into account of the ionizability of some of the protein residues. As previously, an overall potential around the cluster wherein a protein residue performs a chaotic motion is considered to be a combination of the average dihedral and average pairwise potentials (the latter now including an electrostatic contribution for ionized residues) assigned to the residue and the confining potential due to the polymer connectivity constraint. The overall potential as a function of the distance from the cluster has a double well shape (even for ionized beads) which allows one to determine the rates of emission and absorption of residues by the cluster by using a first passage time analysis. Assuming the equality of the ratios of the numbers of negatively and positively ionized residues in the cluster and in the entire protein, one can keep the modified model within the framework of the ternary nucleation formalism and evaluate the size and composition of the nucleus and the protein folding time as in the previous model. As an illustration, the model is again applied to the folding of bovine pancreatic ribonuclease consisting of 124 amino acids, whereof 40 are hydrophobic, 81 hydrophilic (of which 10 are negatively and 18 positively ionizable), and 3 neutral. Numerical calculations at pH=6.3, pH=7.3, and pH=8.3 show that for this protein the time of folding via nucleation is significantly affected by electrostatic interactions only for the unusually low pH of 6.3 and that among all pH's considered pH=7.3 provides the lowest folding time.     

Model for the nucleation mechanism of protein folding

A nucleation-like pathway of protein folding involves the formation of a cluster containing native residues that grows by including residues from the unfolded part of the protein. This pathway is examined by using a heteropolymer as a protein model. The model heteropolymer consists of hydrophobic and hydrophilic beads with fixed bond lengths and bond angles. The total energy of the heteropolymer is determined by the pairwise repulsive/attractive interactions between nonlinked beads and by the contribution from the dihedral angles involved. The parameters of these interactions can be rigorously defined, unlike the ill-defined surface tension of a cluster of protein residues that constitutes the basis of a previous nucleation model. The main idea underlying the new model consists of averaging the dihedral potential of a selected residue over all possible configurations of all neighboring residues along the protein chain. The resulting average dihedral potential depends on the distance between the selected residue and the cluster center. Its combination with the average pairwise potential of the selected residue and with a confining potential caused by the bonds between the residues leads to an overall potential around the cluster that has a double-well shape. Residues in the inner (closer to the cluster) well are considered as belonging to the folded cluster, whereas those in the outer well are treated as belonging to the unfolded part of the protein. Transitions of residues from the inner well into the outer one and vice versa are considered as elementary emission and absorption events, respectively. The double-well character of the potential well around the cluster allows one to determine the rates of both emission and absorption of residues by the cluster using a first passage time analysis. Once these rates are found as functions of the cluster size, one can develop a self-consistent kinetic theory for the nucleation mechanism of folding of a protein. The model allows one to evaluate the size of the nucleus and the protein folding time. The latter is evaluated as the sum of the times necessary for the first nucleation event to occur and for the nucleus to grow to the maximum size (of the folded protein). Depending on the diffusion coefficients of the native residues in the range from 10(-6) to 10(-8) cm2/s, numerical calculations for a protein of 2500 residues suggest that the folding time ranges from several seconds to several hundreds of seconds.     

A kinetic model for nonisothermic homogeneous nucleation

A model for isothermal homogeneous nucleation is proposed that improves the classical model. A quasiequilibrium distribution of clusters was calculated on a basis of the Frenkel’-Lothe-Pound theory. The dependence of the free energy of clusters on their size was represented by an interpolation formula relating the free energy of dimers and large clusters to which a notion of macroscopic surface tension is applicable. The nucleation rate and the dependence of the cluster temperature on their size were calculated by balance equations describing the heating of from a cluster due to the condensation of monomers and its cooling due to collisions with an ambient gas. It is shown that the nucleation rate in excess buffer gas is higher than for the pure condensing gas by approximately two orders of magnitude. The model adequately describes the experimental data for the nucleation of methanol supersaturated vapor.     

Temperature effects on the nucleation mechanism of protein folding and on the barrierless thermal denaturation of a native protein

Recently, the authors proposed a kinetic model for the nucleation mechanism of protein folding where a protein was treated as a heteropolymer with all the bonds and bond angles equal and constant. As a crucial idea of the model, an overall potential around a cluster of native residues in which a protein residue performs a chaotic motion is considered to be a combination of three potentials: effective pairwise, average dihedral, and confining. The overall potential as a function of the distance from the cluster center has a double well shape which allows one to determine the rates with which the cluster emits and absorbs residues by using a first passage time analysis. One can then develop a kinetic theory for the nucleation mechanism of protein folding and evaluate the protein folding time. In the present paper we evaluate the optimal temperature at which the protein folding time is the shortest. A method is also proposed to determine the temperature dependence of the folding time without carrying out the time consuming calculations for a series of temperatures. Using Taylor series expansions in the formalism of the first passage time analysis, one can calculate the temperature dependence of the cluster emission and absorption rates in the vicinity of some temperature T(0) if they are known at T(0). Thus one can evaluate the protein folding time t(f) at any other temperature T in the vicinity of T(0) at which the folding time t(f) is known. We also present a model for the thermal denaturation of a protein occurring via the decay of the native structure of the protein. Due to a sufficiently large temperature increase or decrease, the rate with which a cluster of native residues within a protein emits residues becomes larger than the absorption rate in the whole range of cluster sizes up to the size of the whole protein. This leads to the unfolding of the protein in a barrierless way, i.e., as spinodal decomposition. Knowing the cluster emission and absorption rates as functions of temperature and cluster size, one can find the threshold temperatures of cold and hot barrierless denaturation as well as the corresponding unfolding times. Both proposed methods are illustrated by numerical calculations for two model proteins, one consisting of 124 amino acids, the other consisting of 2500 residues. The first one roughly mimicks a bovine pancreatic ribonuclease while the second one is a representative of the largest proteins which are extremely difficult to study by straightforward Monte Carlo or molecular dynamics simulations.     

A high-resolution probe of protein folding

Protein folding is a central problem in the biological sciences. To generate residue-specific information on the equilibrium folding of cytochrome c, we have semisynthesized the protein with specifically deuterated residues. The C-D bonds may be easily visualized in an otherwise transparent region of the IR spectra, even at high protein and denaturant concentrations. Plotted as a function of added guanidine hydrochloride denaturant, the absorption intensities reveal that the protein undergoes a conformational change at the protein-based ligand, Met80, which is then followed by a more global unfolding at 2.3 M denaturant. Deuteration and characterization of other residues in cytochrome c, or other protein of interest, should provide complete views of folding with residue specific detail that is capable of resolving even the most rapidly interconverting intermediates.     

Dynamic control of protein folding pathway with a polymer of tunable hydrophobicity

While the knowledge of protein folding in a dilute solution is now well-advanced, little is known of the influence of surrounding conditions on the folding kinetics, in particular when the protein is in a dynamically responsive environment. Here we report a new procedure to control the pathways of protein folding by using a thermally responsive polymer that varies its hydrophobicity concomitant with the protein structural changes. The advantages of folding in a dynamic environment have been demonstrated first by Langevin dynamics simulations on the basis of coarse-grained models for both the protein and polymer and then by experiments for lysozyme refolding in the presence of poly(N-isopropylacrylamide-co-N-tert-butylacrylamide), a thermal responsive polymer that varies its hydrophobicity in response to temperature. The simulation suggests that decreasing the polymer hydrophobicity during the folding process may result in an optimized free-energy landscape that enhances both the folding yield and kinetics. The experiments affirm that an optimal folding condition can be identified when structural transitions of the protein collaborate with the polymer hydrophobicity tuned by variation of temperature.     

A statistical model for predicting protein folding rates from amino acid sequence with structural class information

Prediction of protein folding rates from amino acid sequences is one of the most important challenges in molecular biology. In this work, I have related the protein folding rates with physical-chemical, energetic and conformational properties of amino acid residues. I found that the classification of proteins into different structural classes shows an excellent correlation between amino acid properties and folding rates of two- and three-state proteins, indicating the importance of native state topology in determining the protein folding rates. I have formulated a simple linear regression model for predicting the protein folding rates from amino acid sequences along with structural class information and obtained an excellent agreement between predicted and experimentally observed folding rates of proteins; the correlation coefficients are 0.99, 0.96 and 0.95, respectively, for all-alpha, all-beta and mixed class proteins. This is the first available method, which is capable of predicting the protein folding rates just from the amino acid sequence with the aid of generic amino acid properties and structural class information.     

A bias-exchange approach to protein folding

By suitably extending a recent approach [Bussi, G.; et al. J. Am. Chem. Soc. 2006, 128, 13435] we introduce a powerful methodology that allows the parallel reconstruction of the free energy of a system in a virtually unlimited number of variables. Multiple metadynamics simulations of the same system at the same temperature are performed, biasing each replica with a time-dependent potential constructed in a different set of collective variables. Exchanges between the bias potentials in the different variables are periodically allowed according to a replica exchange scheme. Due to the efficaciously multidimensional nature of the bias the method allows exploring complex free energy landscapes with high efficiency. The usefulness of the method is demonstrated by performing an atomistic simulation in explicit solvent of the folding of a Triptophane cage miniprotein. It is shown that the folding free energy landscape can be fully characterized starting from an extended conformation with use of only 40 ns of simulation on 8 replicas.     

Neural network pairwise interaction fields for protein model quality assessment and ab initio protein folding

In order to use a predicted protein structure one needs to know how good it is, as the utility of a model depends on its quality. To this aim, many Model Quality Assessment Programs (MQAP) have been developed over the last decade, with MQAP also being assessed at the CASP competition. We present a new knowledge-based MQAP which evaluates single protein structure models. We use a tree representation of the Cα trace to train a novel Neural Network Pairwise Interaction Field (NN-PIF) to predict the global quality of a model. NN-PIF allows fast evaluation of multiple structure models for a single sequence. In our tests on a large set of structures, our networks outperform most other methods based on different and more complex protein structure representations in global model quality prediction. Moreover, given NN-PIF can evaluate protein conformations very fast, we train a separate version of the model to gauge its ability to fold protein structures ab initio. We show that the resulting system, which relies only on basic information about the sequence and the Cα trace of a conformation, generally improves the quality of the structures it is presented with and may yield promising predictions in the absence of structural templates, although more research is required to harness the full potential of the model.     

Towards understanding the nucleation mechanism for multi-component systems: an atomistic simulation of the ternary nucleation of water/n-nonane/1-butanol

Inspired by the previous finding of some unusual vapour/liquid nucleation results on the ternary water/n-nonane/1-butanol system, atomistic simulations were carried out for a detailed investigation of this mixture. These simulations reproduced the experimentally-reported non-ideal nucleation behaviour for this system, including both onset activities and the average compositions of the critical nuclei. Close examination of the nucleation free energy data and the structure of the critical nuclei reveals two types of phase separation. One occurs internally inside the cluster via formation of a multi-layered structure. The other takes place externally, leading to the coexistence of multiple nucleation channels, characterized by critical clusters of different compositions. Such mechanistic and structural heterogeneity is the microscopic origin of the complex nucleation behaviour observed for this ternary mixture.     

Solvent-induced forces in protein folding reflections on the protein folding problem

It is shown that the solvent induced forces on hydrophilic groups are the strongest ones. The relevance of this finding to protein folding is discussed.     

Topology-dependent protein folding rates analyzed by a stereochemical model

It is an experimental fact that gross topological parameters of the native structure of small proteins presenting two-state kinetics, as relative contact order chi, correlate with the logarithm of their respective folding rate constant kappa(f). However, reported results show specific cases for which the (chi,log kappa(f)) dependence does not follow the overall trend of the entire collection of experimental data. Therefore, an interesting point to be clarified is to what extent the native topology alone can explain these exceptional data. In this work, the structural determinants of the folding kinetics are investigated by means of a 27-mer lattice model, in that each native is represented by a compact self-avoiding (CSA) configuration. The hydrophobic effect and steric constraints are taken as basic ingredients of the folding mechanism, and each CSA configuration is characterized according to its composition of specific patterns (resembling basic structural elements such as loops, sheets, and helices). Our results suggest that (i) folding rate constants are largely influenced by topological details of the native structure, as configurational pattern types and their combinations, and (ii) global parameters, as the relative contact order, may not be effective to detect them. Distinct pattern types and their combinations are determinants of what we call here the "content of secondary-type" structure (sigma) of the native: high sigma implies a large kappa(f). The largest part of all CSA configurations presents a mix of distinct structural patterns, which determine the chixlog kappa(f) linear dependence: Those structures not presenting a proper chi-dependent balance of patterns have their folding kinetics affected with respect to the pretense linear correlation between chi and log kappa(f). The basic physical mechanism relating sigma and kappa(f) involves the concept of cooperativity: If the native is composed of patterns producing a spatial order rich in effective short-range contacts, a properly designed sequence undertakes a fast folding process. On the other hand, the presence of some structural patterns, such as long loops, may reduce substantially the folding performance. This fact is illustrated through natives having a very similar topology but presenting a distinct folding rate kappa(f), and by analyzing structures having the same chi but different sigma.     

Development and optimisation of a novel genetic algorithm for studying model protein folding

Determination of the native state of a protein from its amino acid sequence is the goal of protein folding simulations, with potential applications in gene therapy and drug design. Location of the global minimum structure for a given sequence, however, is a difficult optimisation problem. In this paper, we describe the development and application of a genetic algorithm (GA) to find the lowest-energy conformations for the 2D HP lattice bead protein model. Optimisation of the parameters of our standard GA program reveals that the GA is most successful (at finding the lowest-energy conformations) for high rates of mating and mutation and relatively high elitism. We have also introduced a number of new genetic operators: a duplicate predator—which maintains population diversity by eliminating duplicate structures; brood selection—where two parent structures undergo crossover and give rise to a brood of (not just two) offspring; and a Monte Carlo based local search algorithm—to explore the neighbourhood of all members of the population. It is shown that these operators lead to significant improvements in the success and efficiency of the GA, both compared with our standard GA and with previously published GA studies for benchmark HP sequences with up to 50 beads.     

Population-based local search for protein folding simulation in the MJ energy model and cubic lattices

We present experimental results on benchmark problems in 3D cubic lattice structures with the Miyazawa–Jernigan energy function for two local search procedures that utilise the pull-move set: (i) population-based local search (PLS) that traverses the energy landscape with greedy steps towards (potential) local minima followed by upward steps up to a certain level of the objective function; (ii) simulated annealing with a logarithmic cooling schedule (LSA). The parameter settings for PLS are derived from short LSA-runs executed in pre-processing and the procedure utilises tabu lists generated for each member of the population. In terms of the total number of energy function evaluations both methods perform equally well, however, PLS has the potential of being parallelised with an expected speed-up in the region of the population size. Furthermore, both methods require a significant smaller number of function evaluations when compared to Monte Carlo simulations with kink-jump moves.     

A limiting speed for protein folding at low solvent viscosity

Because protein folding dynamics are heavily overdamped, Kramers theory predicts the rate of folding to scale inversely with the reaction friction, which is usually interpreted to mean the solvent viscosity. This does not mean, however, that the speed of folding can increase without limit as solvent viscosity decreases. We show that, in a sufficiently fast-folding protein, the folding speed approaches a finite limit at low solvent viscosity, indicating a reaction controlled by internal friction.