Matsunaga Yasuhiro, Komuro Yasuaki, Kobayashi Chigusa, Jung Jaewoon, Mori Takaharu, Sugita Yuji
RIKEN Advanced Institute for Computational Science , 7-1-26 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.
RIKEN, Theoretical Molecular Science Laboratory , 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.
J Phys Chem Lett. 2016 Apr 21;7(8):1446-51. doi: 10.1021/acs.jpclett.6b00317. Epub 2016 Apr 6.
Collective variables (CVs) are often used in molecular dynamics simulations based on enhanced sampling algorithms to investigate large conformational changes of a protein. The choice of CVs in these simulations is essential because it affects simulation results and impacts the free-energy profile, the minimum free-energy pathway (MFEP), and the transition-state structure. Here we examine how many CVs are required to capture the correct transition-state structure during the open-to-close motion of adenylate kinase using a coarse-grained model in the mean forces string method to search the MFEP. Various numbers of large amplitude principal components are tested as CVs in the simulations. The incorporation of local coordinates into CVs, which is possible in higher dimensional CV spaces, is important for capturing a reliable MFEP. The Bayesian measure proposed by Best and Hummer is sensitive to the choice of CVs, showing sharp peaks when the transition-state structure is captured. We thus evaluate the required number of CVs needed in enhanced sampling simulations for describing protein conformational changes.
集体变量（CVs）常用于基于增强采样算法的分子动力学模拟中，以研究蛋白质的大尺度构象变化。在这些模拟中，集体变量的选择至关重要，因为它会影响模拟结果，并对自由能分布、最小自由能路径（MFEP）和过渡态结构产生影响。在这里，我们使用平均力弦方法中的粗粒度模型来搜索MFEP，研究在腺苷酸激酶从开放到关闭的运动过程中，需要多少个集体变量才能捕捉到正确的过渡态结构。在模拟中测试了各种数量的大振幅主成分作为集体变量。在更高维的集体变量空间中，可以将局部坐标纳入集体变量，这对于捕捉可靠的MFEP很重要。Best和Hummer提出的贝叶斯度量对集体变量的选择很敏感，当捕捉到过渡态结构时会出现尖锐的峰值。因此，我们评估了在增强采样模拟中描述蛋白质构象变化所需的集体变量数量。
