Parallelization and improvements of the generalized born model with a simple sWitching function for modern graphics processors

Evan J. Arthur, Charles L. Brooks
2016 Journal of Computational Chemistry  
Two fundamental challenges of simulating biologically relevant systems are the rapid calculation of the energy of solvation, and the trajectory length of a given simulation. The Generalized Born model with a Simple sWitching function (GBSW) addresses these issues by using an efficient approximation of Poisson-Boltzmann (PB) theory to calculate each solute atom's free energy of solvation, the gradient of this potential, and the subsequent forces of solvation without the need for explicit solvent
more » ... molecules. This study presents a parallel refactoring of the original GBSW algorithm and its implementation on newly available, low cost graphics chips with thousands of processing cores. Depending on the system size and nonbonded force cutoffs, the new GBSW algorithm offers speed increases of between one and two orders of magnitude over previous implementations while maintaining similar levels of accuracy. We find that much of the algorithm scales linearly with an increase of system size, which makes this water model cost effective for solvating large systems. Additionally, we utilize our GPU-accelerated GBSW model to fold the model system chignolin, and in doing so we demonstrate that these speed enhancements now make accessible folding studies of peptides and potentially small proteins. Here represents the Born radius of atom a, r ab is the distance between atoms a and b, and q is the partial charge of the atoms. τ is the conversion factor that scales the Born energy by the difference in dielectric values at the dielectric boundary. (eq. 3) Arthur and Brooks
doi:10.1002/jcc.24280 pmid:26786647 pmcid:PMC4801733 fatcat:ngj3p632kbgzzbynoxnvgrddvm