Theory for the nonequilibrium dynamics of flexible chain molecules: relaxation to equilibrium of pentadecane from an all-trans conformation

We extend to nonequilibrium processes our recent theory for the long time dynamics of flexible chain molecules. While the previous theory describes the equilibrium motions for any bond or interatomic separation in (bio)polymers by time correlation functions, the present extension of the theory enables the prediction of the nonequilibrium relaxation that occurs in processes, such as T-jump experiments, where there are sudden transitions between, for example, different equilibrium states. As a test of the theory, we consider the ``unfolding'' of pentadecane when it is transported from a constrained all-trans conformation to a random-coil state at thermal equilibrium. The time evolution of the mean-square end-to-end distance after release of the constraint is computed both from the theory and from Brownian dynamics (BD) simulations. The predictions of the theory agree very well with the BD simulations. Furthermore, the theory produces enormous savings in computer time. This work is a starting point for the application of the new method to nonequilibrium processes with biological importance such as the helix-coil transition and protein folding.Comment: 11 pages total, including 2 Postscript figures; submitted to Journal of Chemical Physic

Comparison of self-assembly in lattice and off-lattice model amphiphile solutions

Journal ArticleLattice Monte Carlo and off-lattice molecular dynamics simulations of h1t4 and h4t1 (head/tail) amphiphile solutions have been performed as a function of surfactant concentration and temperature. The lattice and off-lattice systems exhibit quite different self-assembly behavior at equivalent thermodynamic conditions. We found that in the weakly aggregating regime (no preferred-size micelles), all models yield similar micelle size distributions at the same average aggregation number, albeit at different thermodynamic conditions (temperatures)

On Docking, Scoring and Assessing Protein-DNA Complexes in a Rigid-Body Framework

We consider the identification of interacting protein-nucleic acid partners using the rigid body docking method FTdock, which is systematic and exhaustive in the exploration of docking conformations. The accuracy of rigid body docking methods is tested using known protein-DNA complexes for which the docked and undocked structures are both available. Additional tests with large decoy sets probe the efficacy of two published statistically derived scoring functions that contain a huge number of parameters. In contrast, we demonstrate that state-of-the-art machine learning techniques can enormously reduce the number of parameters required, thereby identifying the relevant docking features using a miniscule fraction of the number of parameters in the prior works. The present machine learning study considers a 300 dimensional vector (dependent on only 15 parameters), termed the Chemical Context Profile (CCP), where each dimension reflects a specific type of protein amino acid-nucleic acid base interaction. The CCP is designed to capture the chemical complementarities of the interface and is well suited for machine learning techniques. Our objective function is the Chemical Context Discrepancy (CCD), which is defined as the angle between the native system's CCP vector and the decoy's vector and which serves as a substitute for the more commonly used root mean squared deviation (RMSD). We demonstrate that the CCP provides a useful scoring function when certain dimensions are properly weighted. Finally, we explore how the amino acids on a protein's surface can help guide DNA binding, first through long-range interactions, followed by direct contacts, according to specific preferences for either the major or minor grooves of the DNA

Modeling the Hydration Layer around Proteins: HyPred

AbstractProtein hydration plays an integral role in determining protein function and stability. We develop a simple method with atomic level precision for predicting the solvent density near the surface of a protein. A set of proximal radial distribution functions are defined and calculated for a series of different atom types in proteins using all-atom, explicit solvent molecular dynamic simulations for three globular proteins. A major improvement in predicting the hydration layer is found when the protein is held immobile during the simulations. The distribution functions are used to develop a model for predicting the hydration layer with sub-1-Ångstrom resolution without the need for additional simulations. The model and the distribution functions for a given protein are tested in their ability to reproduce the hydration layer from the simulations for that protein, as well as those for other proteins and for simulations in which the protein atoms are mobile. Predictions for the density of water in the hydration shells are then compared with high occupancy sites observed in crystal structures. The accuracy of both tests demonstrates that the solvation model provides a basis for quantitatively understanding protein solvation and thereby predicting the hydration layer without additional simulations