Reduced point charge models of proteins: Assessment based on molecular dynamics simulations

A reduced point charge distribution is used to model Ubiquitin and two complexes, Vps27 UIM-1-Ubiquitin and Barnase-Barstar. It is designed from local extrema in charge density distributions obtained from the Poisson equation applied to smoothed molecular electrostatic potentials. A variant distribution is built by locating point charges on atoms. Various charge fitting conditions are selected, i.e. from either electrostatic Amber99 (Assisted Model Building with Energy Refinement) Coulomb potential or forces, considering reference grid points located within various distances from the protein atoms, with or without separate treatment of main and side chain charges. The program GROMACS (Groningen Machine for Chemical Simulations) is used to generate Amber99SB molecular dynamics (MD) trajectories of the solvated proteins modelled using the various reduced point charge models (RPCMs) so obtained. Point charges that are not located on atoms are considered as virtual sites. Some RPCMs lead to stable MD trajectories. They, however, involve a partial loss in the protein secondary structure and lead to a less-structured solute solvation shell. The model built by fitting charges on Coulomb forces calculated at grid points ranging between 1.4 and 2.0 times the van der Waals radius of the atoms, with a separate treatment of main chain and side chain charges, appears to best approximate all-atom MD trajectories.</p

Analysis of Three-Dimensional Protein Images

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.Comment: See http://www.jair.org/ for any accompanying file