Xavier Daura

Peptide folding: When simulation meets experiment

Mol. dynamics simulation studies on the folding of beta-peptides H-beta3-HVal-beta3-HAla-beta3-HLeu-(S,S)-beta3-HAla(alphaMe)-beta3-HVal-beta3-HAla-beta3-HLeu-OH and H-beta2-HVal-beta3-HAla-beta2-HLeu-beta3-HVal-beta2-HAla-beta3-HLeu-OH were carried out. Despite the small differences in sequence between the two peptides studied, the simulations correctly predict a left-handed 31-helical fold for the beta-heptapeptide and a right-handed helical fold for the beta-hexapeptide.

An Improved GROMOS96 Force Field for Aliphatic Hydrocarbons in the Condensed Phase

Over the past 4 years the GROMOS96 force field has been successfully used in biomolecular simulations, for example in peptide folding studies and detailed protein investigations, but no applications to lipid systems have been published yet. Here we provide a detailed investigation of aliphatic liquid systems. For liquids of larger aliphatic chains, n‐heptane and longer, the standard GROMOS96 parameter sets 43A1 and 43A2 yield a too low pressure at the experimental density. Therefore, a reparametrization of the GROMOS96 force field regarding aliphatic carbons was initiated. The new force field parameter set 45A3 shows considerable improvements for n‐alkanes, cyclo‐, iso‐, and neoalkanes and other branched aliphatics. Liquid densities and heat of vaporization are reproduced for almost all of these molecules. Excellent agreement is found with experiment for the free energy of hydration for alkanes. The GROMOS96 45A3 parameter set should, therefore, be suitable for application to lipid aggregates such as membranes and micelles, for mixed systems of aliphatics with or without water, for polymers, and other apolar systems that may interact with different biomolecules.

AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides

BackgroundProtein aggregation correlates with the development of several debilitating human disorders of growing incidence, such as Alzheimers and Parkinsons diseases. On the biotechnological side, protein production is often hampered by the accumulation of recombinant proteins into aggregates. Thus, the development of methods to anticipate the aggregation properties of polypeptides is receiving increasing attention. AGGRESCAN is a web-based software for the prediction of aggregation-prone segments in protein sequences, the analysis of the effect of mutations on protein aggregation propensities and the comparison of the aggregation properties of different proteins or protein sets.ResultsAGGRESCAN is based on an aggregation-propensity scale for natural amino acids derived from in vivo experiments and on the assumption that short and specific sequence stretches modulate protein aggregation. The algorithm is shown to identify a series of protein fragments involved in the aggregation of disease-related proteins and to predict the effect of genetic mutations on their deposition propensities. It also provides new insights into the differential aggregation properties displayed by globular proteins, natively unfolded polypeptides, amyloidogenic proteins and proteins found in bacterial inclusion bodies.ConclusionBy identifying aggregation-prone segments in proteins, AGGRESCAN http://bioinf.uab.es/aggrescan/ shall facilitate (i) the identification of possible therapeutic targets for anti-depositional strategies in conformational diseases and (ii) the anticipation of aggregation phenomena during storage or recombinant production of bioactive polypeptides or polypeptide sets.Protein aggregation correlates with the development of several debilitating human disorders of growing incidence, such as Alzheimers and Parkinsons diseases. On the biotechnological side, protein production is often hampered by the accumulation of recombinant proteins into aggregates. Thus, the development of methods to anticipate the aggregation properties of polypeptides is receiving increasing attention. AGGRESCAN is a web-based software for the prediction of aggregation-prone segments in protein sequences, the analysis of the effect of mutations on protein aggregation propensities and the comparison of the aggregation properties of different proteins or protein sets. AGGRESCAN is based on an aggregation-propensity scale for natural amino acids derived from in vivo experiments and on the assumption that short and specific sequence stretches modulate protein aggregation. The algorithm is shown to identify a series of protein fragments involved in the aggregation of disease-related proteins and to predict the effect of genetic mutations on their deposition propensities. It also provides new insights into the differential aggregation properties displayed by globular proteins, natively unfolded polypeptides, amyloidogenic proteins and proteins found in bacterial inclusion bodies. By identifying aggregation-prone segments in proteins, AGGRESCAN http://bioinf.uab.es/aggrescan/ shall facilitate (i) the identification of possible therapeutic targets for anti-depositional strategies in conformational diseases and (ii) the anticipation of aggregation phenomena during storage or recombinant production of bioactive polypeptides or polypeptide sets.

Folding-unfolding thermodynamics of a beta-heptapeptide from equilibrium simulations.

The thermodynamics of folding and unfolding of a β‐heptapeptide in methanol solution has been studied at four different temperatures, 298 K, 340 K, 350 K, and 360 K, by molecular dynamics simulation. At each of these temperatures, the 50‐ns simulations were sufficient to generate an equilibrium distribution between a relatively small number of conformations (∼102), showing that, even above the melting temperature (∼340 K), the peptide does not randomly sample conformational space. The free energy of folding and the free energy difference between pairs of conformations have been calculated from their relative populations. The experimentally determined folded conformation at 298 K, a left‐handed 31‐helix, is at each of the four temperatures the predominant conformation, with its probability and average lifetime decreasing with increasing temperature. The most common intermediates of folding and unfolding are also the same at the four temperatures. Paths and rates of interconversion between different conformations have been determined. It has been found that folding can occur through multiple pathways, not necessarily downhill in free energy, although the final step involves a reduced number of intermediates. Proteins 1999;34:269–280.

Parametrization of aliphatic CHn united atoms of GROMOS96 force field

The derivation of the van der Waals parameters for the aliphatic CHn united atoms of the GROMOS96 force field is presented. The parameters have been adjusted to reproduce the experimental enthalpies of vaporization and vapor pressures or densities of a set of nine alkanes in the liquid state at 298 K (or at the boiling point in the case of methane), using a cutoff radius for the van der Waals interactions of 1.6 nm. Force fields to be used in molecular simulations are bound to the conditions chosen for their parametrization, for example, the temperature, the densities of the systems included in the calibration set, or the cutoff radius used for the nonbonded interactions. Van der Waals parameters for the CHn united atoms of earlier GROMOS force fields were developed using a cutoff radius of 0.8 nm for the van der Waals interactions. Because the van der Waals interaction energy between aliphatic groups separated by distances between 0.8 and 1.4 nm is not negligible at liquid densities, the use of these parameters in combination with longer cutoffs leads to an overestimation of the attractive van der Waals interaction energy. The relevance of this excess attraction depends on the size of the groups that are interacting, as well as on their local densities. Free energies of hydration have been calculated for five alkanes. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 535–547, 1998

Derivation of an improved simple point charge model for liquid water: SPC/A and SPC/L

Different approaches to improve the simple point charge model for liquid water (SPC) were investigated. This led to a whole series of new water models with additional van der Waals interaction sites at the hydrogen atoms, modified partial charges and modified geometries. The properties of these models are analyzed and discussed. Particular emphasis has been put on the study of the dependence and sensitivity of water properties on the model parameters. We found that a simultaneous improvement of the dielectric permittivity and the diffusion coefficient is difficult to attain for a rigid, nonpolarizable three interaction site model. Nevertheless, two of the models presented here, SPC/A and SPC/L, show good agreement with experimental data on water and have been characterized in more detail. We conclude that SPC/L represents the overall properties of water better than SPC. Especially, it shows excellent dielectric properties, an improved shear viscosity and a slightly lower diffusion coefficient.

The Key to Solving the Protein-Folding Problem Lies in an Accurate Description of the Denatured State

Accurate simulation at the atomic level of the folding process of a variety of peptides into different native folds can be achieved with a general purpose force field and Newtons equations of motion. The key to understanding this peptide folding lies in the unexpectedly small size of the denatured state and an accurate description thereof.

Assessing equilibration and convergence in biomolecular simulations

If molecular dynamics simulations are used to characterize the folding of peptides or proteins, a wide range of conformational states needs to be sampled. This study reports an analysis of peptide simulations to identify the best methods for assessing equilibration and sampling in these systems where there is significant conformational disorder. Four trajectories of a β peptide in methanol and four trajectories of an α peptide in water, each of 5 ns in length, have been studied. Comparisons have also been made with two 50‐ns trajectories of the β peptide in methanol. The convergence rates of quantities that probe both the extent of conformational sampling and the local dynamical properties have been characterized. These include the numbers of hydrogen bonds populated, clusters identified, and main chain torsion angle transitions in the trajectories. The relative equilibrium rates of different quantities are found to vary significantly between the two systems studied reflecting both the differences in peptide primary structure and the different solvents used. A cluster analysis of the simulation trajectories is identified as a very effective method for judging the convergence of the simulations. This is particularly the case if the analysis includes a comparison of multiple trajectories calculated for the same system from different starting structures. Proteins 2002;48:487–496.

Computation of Free Energy

Many quantities that are standardly used to characterize a chemical system are related to free-energy differences between particular states of the system. By statistical mechanics, free-energy differences may be expressed in terms of averages over ensembles of atomic configurations for the molecular system of interest. Here, we review the most useful formulae to calculate free-energy differences from ensembles generated by molecular simulation, illustrate a number of recent developments, and highlight practical aspects of such calculations with examples selected from the literature.

Comparing geometric and kinetic cluster algorithms for molecular simulation data

The identification of metastable states of a molecule plays an important role in the interpretation of molecular simulation data because the free-energy surface, the relative populations in this landscape, and ultimately also the dynamics of the molecule under study can be described in terms of these states. We compare the results of three different geometric cluster algorithms (neighbor algorithm, K-medoids algorithm, and common-nearest-neighbor algorithm) among each other and to the results of a kinetic cluster algorithm. First, we demonstrate the characteristics of each of the geometric cluster algorithms using five two-dimensional data sets. Second, we analyze the molecular dynamics data of a beta-heptapeptide in methanol--a molecule that exhibits a distinct folded state, a structurally diverse unfolded state, and a fast folding/unfolding equilibrium--using both geometric and kinetic cluster algorithms. We find that geometric clustering strongly depends on the algorithm used and that the density based common-nearest-neighbor algorithm is the most robust of the three geometric cluster algorithms with respect to variations in the input parameters and the distance metric. When comparing the geometric cluster results to the metastable states of the beta-heptapeptide as identified by kinetic clustering, we find that in most cases the folded state is identified correctly but the overlap of geometric clusters with further metastable states is often at best approximate.