Accelerating Molecular Docking Calculations Using Graphics Processing Units
Accelerating Molecular Docking Calculations Using Graphics Processing Units
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DOI：
10.1021/ci100459b
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发表时间：
2011-04-01
影响因子：
5.6
通讯作者：
Exner, Thomas E.
中科院分区：
文献类型：
--
作者：
Korb, Oliver;Stutzle, Thomas;Exner, Thomas E.
分子构象的生成和相互作用势的评估是分子建模应用中的常见任务，特别是在蛋白质-配体或蛋白质-蛋白质对接程序中。在这项工作中，我们提出了一个GPU加速的方法，能够大大加快这些任务。对于刚性蛋白质-蛋白质对接背景下的相互作用势的评估，与优化的基于CPU的实现相比，GPU加速方法达到了高达50以上的加速因子。将蛋白质结合位点中的配体和供体基团处理为灵活的，在蛋白质-配体相互作用势的评估中可以观察到高达16的加速因子。此外，我们引入了一个并行版本的蛋白质配体对接算法植物，可以利用这个GPU加速的评分功能评估。我们将GPU加速的并行版本与CPU上运行的相同算法以及高度优化的基于CPU的顺序版本进行了比较。根据配体尺寸和可旋转键数的依赖性，可以观察到分别高达10和7的加速因子。最后，在刚性蛋白质-蛋白质对接的背景下进行适应度景观分析。使用基于网格的系统搜索方法，GPU加速版本的性能优于基于CPU的版本，加速因子高达60。
The generation of molecular conformations and the evaluation of interaction potentials are common tasks in molecular modeling applications, particularly in protein-ligand or protein-protein docking programs. In this work, we present a GPU-accelerated approach capable of speeding up these tasks considerably. For the evaluation of interaction potentials in the context of rigid protein-protein docking, the GPU-accelerated approach reached speedup factors of up to over 50 compared to an optimized CPU-based implementation. Treating the ligand and donor groups in the protein binding site as flexible, speedup factors of up to 16 can be observed in the evaluation of protein-ligand interaction potentials. Additionally, we introduce a parallel version of our protein-ligand docking algorithm PLANTS that can take advantage of this GPU-accelerated scoring function evaluation. We compared the GPU-accelerated parallel version to the same algorithm running on the CPU and also to the highly optimized sequential CPU-based version. In terms of dependence of the ligand size and the number of rotatable bonds, speedup factors of up to 10 and 7, respectively, can be observed. Finally, a fitness landscape analysis in the context of rigid protein-protein docking was performed. Using a systematic grid-based search methodology, the GPU-accelerated version outperformed the CPU-based version with speedup factors of up to 60.