EGO - An efficient molecular dynamics program and its application to protein dynamics simulations

Abstract

Molecular dynamics (MD) simulations of proteins provide descriptions of atomic motions, which allow relating observable properties of proteins to microscopic processes. In particular, the 'mechanics' at the atomic level of these 'nano-machines' can be probed by recently developed efficient MD techniques such as single molecule unbinding simulations or 'conformational flooding'. MD simulations in general are computationally very expensive and, therefore, are limited to time scales of nanoseconds, even if the latest supercomputers are used. In contrast, the elementary steps of protein function are typically in the micro- to millisecond range. Moreover, conventional MD simulations are based on force fields and thus, generally, cannot describe chemical reactions. This means, for example, that enzymatic catalysis is beyond the scope of conventional MD methods. In this contribution we describe a concerted effort to overcome these limitations. A fast multiple time step structure adapted multipole method (FAMUSAMM) has been developed to speed up the evaluation of the computationally most demanding Coulomb interactions in solvated protein models, thereby providing the basis for efficient MD simulations of any kind. On top of that, and based on a description of protein dynamics within the framework of non-equilibrium statistical mechanics, a method to predict the reaction pathway and target structure of slow conformational transitions ('conformational flooding') is described. To enable treatment of localized chemical reactions, e.g., at the active sites of enzymes, and utilizing the efficient FAMUSAMM description, an interface to the CPMD density functional package by M. Parrinello et al. (J. Hutter et al., MPI für Festkörperforschung, Stuttgart, 1995-1998) is introduced, which allows hybrid quantum mechanical / molecular dynamics (QM/MD) simulations. All these methods have been integrated within the parallel MD program EGO.