Particle swarm optimization (PSO) is a powerful technique for computer aided prediction of proteins' three-dimensional structure. In this work, employing an all-atom force field we demonstrate the efficiency of the standard PSO algorithm, as implemented in the ArFlock library, for finding the folded state of two proteins of different sizes starting from completely extended conformations. In particular, the predicted structure of the larger protein is in good agreement with the structure from the Protein Data Bank within the experimental resolution. We also show that parallelization of the PSO speeds up the simulation linearly with the number of workers and reduces the time for predictions dramatically without loss of accuracy.
Protein Structure Prediction using Particle Swarm Optimization and a Distributed Parallel Approach
I. Kondov and R. Berlich
Proc. "Biologically inspired algorithms for distributed systems", ICAC '11 8th International Conference on Autonomic Computing Karlsruhe, Germany, pp. 35-42, ACM