Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.
Department of Physics & Astronomy, Rice University, Houston, Texas 77005, United States.
J Chem Theory Comput. 2020 Dec 8;16(12):7915-7925. doi: 10.1021/acs.jctc.0c00991. Epub 2020 Nov 10.
The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of high-performance computer (HPC) systems. Utilizing only "brute force" molecular dynamics (MD) simulations requires an unacceptably long time to solution. Adaptive sampling methods allow a more effective sampling of protein dynamics than standard MD simulations. Depending on the restarting strategy, the speed up can be more than 1 order of magnitude. One challenge limiting the utilization of adaptive sampling by domain experts is the relatively high complexity of efficiently running adaptive sampling on HPC systems. We discuss how the ExTASY framework can set up new adaptive sampling strategies and reliably execute resulting workflows at scale on HPC platforms. Here, the folding dynamics of four proteins are predicted with no a priori information.
尽管已经使用了高性能计算机(HPC)系统,但是准确地获取蛋白质动力学仍然是一个持续的挑战。仅使用“暴力”分子动力学(MD)模拟需要花费无法接受的长时间才能得到结果。自适应采样方法比标准 MD 模拟更有效地对蛋白质动力学进行采样。根据重新启动策略,加速可以超过 1 个数量级。限制领域专家使用自适应采样的一个挑战是在 HPC 系统上高效运行自适应采样的相对较高的复杂性。我们讨论了 ExTASY 框架如何设置新的自适应采样策略,并在 HPC 平台上可靠地大规模执行由此产生的工作流程。在这里,我们使用无先验信息预测了四个蛋白质的折叠动力学。