Protein structure prediction using distributed parallel particle swarm optimization

Particle swarm optimization is a powerful technique for computer aided prediction of proteins' three-dimensional structure. In this work, employing an all-atom force field and the standard algorithm, as implemented in the ArFlock library in previous work, the low-energy conformations of several peptides of different sizes in vacuum starting from completely extended conformations are investigated. The computed structures are in good overall agreement with experimental data and results from other computer simulations. Periodic boundary conditions applied to the search space improve the performance of the method dramatically, especially when the linear velocity update rule is used. It is also shown that asynchronous parallelization speeds up the simulation better than the synchronous one and reduces the effective time for predictions significantly.