CoCo-MD: A Simple and Effective Method for the Enhanced Sampling of Conformational Space.
CoCo-MD: A Simple and Effective Method for the Enhanced Sampling of Conformational Space.
复制标题
DOI：
10.1021/acs.jctc.8b00657
复制
发表时间：
2019-01
影响因子：
5.5
通讯作者：
Ardita Shkurti;I. D. Styliari;Vivek Balasubramanian;I. Bethune;Conrado Pedebos;S. Jha;C. Laughton
中科院分区：
文献类型：
--
作者：
Ardita Shkurti;I. D. Styliari;Vivek Balasubramanian;I. Bethune;Conrado Pedebos;S. Jha;C. Laughton
CoCo（“互补坐标”）是一种基于主成分分析（PCA）的系综富集方法，最初是为核磁共振数据的研究而开发的。在这里，我们研究了CoCo方法与分子动力学模拟（CoCo- md）相结合的潜力，以更广泛地用于增强构象空间的采样。使用丙氨酸五肽作为模型系统，我们发现一个迭代的工作流程，将短的多步行者MD模拟与通过CoCo分析通知的构象空间的远程跳跃交叉，可以在相同的计算工作量（MD时间步长的总数）下将构象空间的采样率提高10倍。结合水库- remd方法，可以很容易地计算出自由能。本文还描述了一种近似但快速且实用的替代方法来生成无偏CoCo-MD数据。应用于环孢素A，我们可以实现比以前报道的更大的构象采样，使用一小部分计算资源。麦芽糖结合蛋白的模拟，从“开放”状态开始，有效地采样了与配体结合相关的“封闭”构象。基于pca的方法意味着增强采样的最佳集体变量不需要由用户预先定义，而是自动识别并自适应，响应发展中的集合的特征。此外，该方法不需要对相关的MD代码进行任何调整，并且与任何传统的MD包兼容。
CoCo ("complementary coordinates") is a method for ensemble enrichment based on principal component analysis (PCA) that was developed originally for the investigation of NMR data. Here we investigate the potential of the CoCo method, in combination with molecular dynamics simulations (CoCo-MD), to be used more generally for the enhanced sampling of conformational space. Using the alanine penta-peptide as a model system, we find that an iterative workflow, interleaving short multiple-walker MD simulations with long-range jumps through conformational space informed by CoCo analysis, can increase the rate of sampling of conformational space up to 10 times for the same computational effort (total number of MD timesteps). Combined with the reservoir-REMD method, free energies can be readily calculated. An alternative, approximate but fast and practically useful, alternative approach to unbiasing CoCo-MD generated data is also described. Applied to cyclosporine A, we can achieve far greater conformational sampling than has been reported previously, using a fraction of the computational resource. Simulations of the maltose binding protein, begun from the "open" state, effectively sample the "closed" conformation associated with ligand binding. The PCA-based approach means that optimal collective variables to enhance sampling need not be defined in advance by the user but are identified automatically and are adaptive, responding to the characteristics of the developing ensemble. In addition, the approach does not require any adaptations to the associated MD code and is compatible with any conventional MD package.