Collaborative Parallel Local Search for Simplified Protein Structure Prediction
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Protein structure prediction is a challenging optimisation problem to the computer scientists. A large number of existing single-point search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multi-point local search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a pre-defined period of time. The improved solutions are stored thread-wise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. We tested our approach on large sized proteins for simplified models. The experimental results show that our new parallel framework significantly improves over the results obtained by the state-of-the-art single-point search approaches for the hydrophobic-polar energy model and the three dimensional face-centred-cubic lattice.
12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-13)
Artificial Intelligence and Image Processing not elsewhere classified