Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt\nto solve the problem by exploring possible structures and finding the one with the minimum free energy.However, these algorithms\nperform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral\nsearch framework that uses parallel processing techniques to expedite exploration by starting fromdifferent points. In our approach,\na set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined\nperiod of time. The improved solutions are stored threadwise.When the threads finish, the solutions are merged together and the\nduplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure\nprediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping.We use both the low\nresolution hydrophobic-polar energy model and the high-resolution 20 Ã?â?? 20 energy model for search guiding. The experimental\nresults show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search\napproaches for both energy models on three-dimensional face-centred-cubic lattice.We also experimentally show the effectiveness\nof mixing energy models within parallel threads.
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