Frontiers in biomolecular mesh generation and molecular visualization systems.

With the development of biomolecular modeling and simulation, especially implicit solvent modeling, higher requirements are set for the stability, efficiency and mesh quality of molecular mesh generation software. In this review, we summarize the recent works in biomolecular mesh generation and molecular visualization. First, we introduce various definitions of molecular surface and corresponding meshing software. Second, as the mesh quality significantly influences biomolecular simulation, we investigate some remeshing methods in the fields of computer graphics and molecular modeling. Then, we show the application of biomolecular mesh in the boundary element method (BEM) and the finite element method (FEM). Finally, to conveniently visualize the numerical results based on the mesh, we present two types of molecular visualization systems.

Protein molecular dynamics with electrostatic force entirely determined by a single Poisson-Boltzmann calculation.

The electrostatic force including the intramolecular Coulombic interactions and the electrostatic contribution of solvation effect were entirely calculated by using the finite difference Poisson-Boltzmann method (FDPB), which was incorporated into the GROMOS96 force field to complete a new finite difference stochastic dynamics procedure (FDSD). Simulations were performed on an insulin dimer. Different relative dielectric constants were successively assigned to the protein interior; a value of 17 was selected as optimal for our system. The simulation data were analyzed and compared with those obtained from 500-ps molecular dynamics (MD) simulation with explicit water and a 500-ps conventional stochastic dynamics (SD) simulation without the mean solvent force. The results indicate that the FDSD method with GROMOS96 force field is suitable to study the dynamics and structure of proteins in solution if used with the optimal protein dielectric constant.

A new computational approach for real protein folding prediction.

An effective and fast minimization approach is proposed for the prediction of protein folding, in which the 'relative entropy' is used as a minimization function and the off-lattice model is used. In this approach, we only use the information of distances between the consecutive Calpha atoms along the peptide chain and a generalized form of the contact potential for 20 types of amino acids. Tests of the algorithm are performed on the real proteins. The root mean square deviations of the structures of eight folded target proteins versus the native structures are in a reasonable range. In principle, this method is an improvement on the energy minimization approach.