GRID-BASED CONFORMATIONAL SAMPLING

Computational simulations of conformational sampling in general, and of macromolecular folding in particular represent one of the most important and yet one of the most challenging applications of computer science in biology and medicinal chemistry. This paper presents a massively parallel GRID-based conformational sampling strategy, exploring the extremely rugged energy response surface in function of molecular geometry, in search of low energy zones through phase spaces of hundreds of degrees of freedom. We have generalized the classical island model deployment of Genetic Algorithms (GA) to a "planetary" model where each node of the GRID is assimilated to a "planet" harboring quasi-independent multi-island simulations using a hybrid GA. Although different "planets" do not communicate to each other-thus minimizing inter-CPU exchanges on the GRID-each new simulation will benefit from the preliminary knowledge of already visited geometries, located on the dispatcher machine, and which are disseminated to any new "planet". This "panspermic" strategy allows new simulations to be conducted such as to either be attracted towards an apparently promising phase space zone (biasing strategies, intensification procedures) or to avoid already in-depth sampled (tabu) areas. Successful all-atom folding of mini-proteins typically used in benchmarks (the Trp cage 1L2Y and the Trp zipper 1LE1) has been observed, although the reproducibility of these highly stochastic simulations in huge problem spaces is still in need of improvement.