Rashid Mahmood A, Shatabda Swakkhar, Newton M A Hakim, Hoque Md Tamjidul, Sattar Abdul
Institute for Integrated & Intelligent Systems, Science 2 (N34) 1.45, 170 Kessels Road, Nathan, QLD 4111, Australia ; Queensland Research Lab, National ICT Australia, Level 8, Y Block, 2 George Street, Brisbane, QLD 4000, Australia.
Institute for Integrated & Intelligent Systems, Science 2 (N34) 1.45, 170 Kessels Road, Nathan, QLD 4111, Australia.
Adv Bioinformatics. 2014;2014:985968. doi: 10.1155/2014/985968. Epub 2014 Mar 16.
Protein structure prediction is computationally a very challenging problem. A large number of existing 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 multipoint spiral 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 predefined period of time. The improved solutions are stored threadwise. 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. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20 × 20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads.
蛋白质结构预测在计算上是一个极具挑战性的问题。大量现有的搜索算法试图通过探索可能的结构并找到具有最小自由能的结构来解决该问题。然而，由于搜索空间极其广阔，这些算法在大型蛋白质上表现不佳。在本文中，我们提出了一种多点螺旋搜索框架，该框架使用并行处理技术，通过从不同点开始来加速探索。在我们的方法中，生成一组随机的初始解并将其分配到不同的线程。我们允许每个线程运行预定义的时间段。改进后的解按线程存储。当线程完成后，将这些解合并在一起并去除重复项。然后将一组选定的不同解再次拆分为不同的线程。在我们的从头算蛋白质结构预测方法中，我们使用三维面心立方晶格进行结构骨架映射。我们使用低分辨率的疏水 - 极性能量模型和高分辨率的20×20能量模型来指导搜索。实验结果表明，我们的新并行框架在三维面心立方晶格上，对于两种能量模型，都显著改进了通过最先进的单点搜索方法获得的结果。我们还通过实验证明了在并行线程中混合能量模型的有效性。
