ABSTRACT
Ion-water-cluster properties are investigated both through the multistate empirical valence bond potential and a polarizable model. Equilibrium properties of the ion-water clusters H+(H2O)100, Na+(H2O)100, Na+(H2O)20, and Cl-(H2O)17 in the temperature region 100-450 K are explored using a hybrid parallel basin-hopping and tempering algorithm. The effect of the solid-liquid phase transition in both caloric curves and structural distribution functions is investigated. It is found that sodium and chloride ions largely reside on the surface of water clusters below the cluster melting temperature but are solvated into the interior of the cluster above the melting temperature, while the solvated proton was found to have significant propensity to reside on or near the surface in both the liquid- and solid-state clusters.
Subject(s)
Chemistry, Physical/methods , Chlorides/chemistry , Protons , Sodium/chemistry , Water/chemistry , Algorithms , Calorimetry , Chlorine/chemistry , Computer Simulation , Ions , Models, Molecular , Models, Statistical , Models, Theoretical , Monte Carlo Method , TemperatureABSTRACT
The ab initio atom-centered density-matrix propagation approach and the multistate empirical valence bond method have been employed to study the structure, dynamics, and rovibrational spectrum of a hydrated proton in the "magic" 21 water cluster. In addition to the conclusion that the hydrated proton tends to reside on the surface of the cluster, with the lone pair on the protonated oxygen pointing "outwards," it is also found that dynamical effects play an important role in determining the vibrational properties of such clusters. This result is used to analyze and complement recent experimental and theoretical studies.
Subject(s)
Ions , Water/chemistry , Adsorption , Algorithms , Binding Sites , Chemistry, Physical/methods , Fourier Analysis , Hydrogen/chemistry , Models, Theoretical , Molecular Conformation , Protons , Software , Thermodynamics , Time FactorsABSTRACT
The application of all-atom force fields (and explicit or implicit solvent models) to protein homology-modeling tasks such as side-chain and loop prediction remains challenging both because of the expense of the individual energy calculations and because of the difficulty of sampling the rugged all-atom energy surface. Here we address this challenge for the problem of loop prediction through the development of numerous new algorithms, with an emphasis on multiscale and hierarchical techniques. As a first step in evaluating the performance of our loop prediction algorithm, we have applied it to the problem of reconstructing loops in native structures; we also explicitly include crystal packing to provide a fair comparison with crystal structures. In brief, large numbers of loops are generated by using a dihedral angle-based buildup procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures. We evaluate this method by using the largest test set yet used for validation of a loop prediction method, with a total of 833 loops ranging from 4 to 12 residues in length. Average/median backbone root-mean-square deviations (RMSDs) to the native structures (superimposing the body of the protein, not the loop itself) are 0.42/0.24 A for 5 residue loops, 1.00/0.44 A for 8 residue loops, and 2.47/1.83 A for 11 residue loops. Median RMSDs are substantially lower than the averages because of a small number of outliers; the causes of these failures are examined in some detail, and many can be attributed to errors in assignment of protonation states of titratable residues, omission of ligands from the simulation, and, in a few cases, probable errors in the experimentally determined structures. When these obvious problems in the data sets are filtered out, average RMSDs to the native structures improve to 0.43 A for 5 residue loops, 0.84 A for 8 residue loops, and 1.63 A for 11 residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that sampling rarely limits prediction accuracy. The overall results are, to our knowledge, the best reported to date, and we attribute this success to the combination of an accurate all-atom energy function, efficient methods for loop buildup and side-chain optimization, and, especially for the longer loops, the hierarchical refinement protocol.
