Size-and-Shape Space Gaussian Mixture Models for Structural Clustering of Molecular Dynamics Trajectories.
Size-and-Shape Space Gaussian Mixture Models for Structural Clustering of Molecular Dynamics Trajectories.
复制标题
大小和形状空间高斯混合模型，用于分子动力学轨迹的结构聚类。
DOI：
10.1021/acs.jctc.1c01290
复制
发表时间：
2022-05-10
影响因子：
5.5
通讯作者：
McCullagh, Martin
中科院分区：
文献类型：
--
作者：
Klem, Heidi;Hocky, Glen M.;McCullagh, Martin
从分子构型的集合中确定结构簇的最佳数量和特性仍然是一个挑战。最近的结构聚类方法主要集中在内部坐标的使用上，因为这些特征具有固有的旋转和平移不变性。大量可能的内部坐标需要一个特征空间监督步骤，以使聚类易于处理，但产生一个可以特定于系统类型的协议。粒子位置提供了一个有吸引力的替代内部坐标，但缺乏旋转和平移不变性，以及对结构不相似区域的感知不敏感。在这里，我们提出了一种称为shape-GMM的方法，该方法克服了使用加权最大似然（ML）对齐过程中粒子位置的缺点。然后将这种校准策略构建到期望最大化高斯混合模型（GMM）程序中，以捕获自由能景观中的亚稳态。所得到的算法区分了各种不同的结构，包括RMSD和成对距离无法区分的结构，如几个模型系统所示。Shape-GMM对快速折叠HP35 Nle/Nle突变蛋白的广泛模拟结果支持四态折叠/展开机制，这与先前的实验结果一致，并提供与先前最先进的聚类方法相媲美的动力学细节，如VAMP-2评分所衡量的那样。目前，为了估计高维协方差矩阵和平衡计算费用，建议对可由≤200个粒子和≤100K组态表示的系统（或子系统）进行形状- gmm的训练。一旦训练了形状- gmm，它就可以用来预测数百万种配置的簇身份。
Determining the optimal number and identity of structural clusters from an ensemble of molecular configurations continues to be a challenge. Recent structural clustering methods have focused on the use of internal coordinates due to the innate rotational and translational invariance of these features. The vast number of possible internal coordinates necessitates a feature space supervision step to make clustering tractable, but yields a protocol that can be system type specific. Particle positions offer an appealing alternative to internal coordinates, but suffer from a lack of rotational and translational invariance, as well as a perceived insensitivity to regions of structural dissimilarity. Here, we present a method, denoted shape-GMM, that overcomes the shortcomings of particle positions using a weighted maximum likelihood (ML) alignment procedure. This alignment strategy is then built into an expectation maximization Gaussian mixture model (GMM) procedure to capture metastable states in the free energy landscape. The resulting algorithm distinguishes between a variety of different structures, including those indistinguishable by RMSD and pair-wise distances, as demonstrated on several model systems. Shape-GMM results on an extensive simulation of the the fast-folding HP35 Nle/Nle mutant protein support a 4-state folding/unfolding mechanism which is consistent with previous experimental results and provides kinetic detail comparable to previous state of the art clustering approaches, as measured by the VAMP-2 score. Currently, training of shape-GMMs is recommended for systems (or subsystems) that can be represented by ≲ 200 particles and ≲ 100K configurations to estimate high-dimensional covariance matrices and balance computational expense. Once a shape-GMM is trained, it can be used to predict the cluster identities of millions of configurations.
登录
查看更多内容
影响因子：
5.6
作者：
Damjanovic J;Murphy JM;Lin YS
通讯作者：
Lin YS
影响因子：
4.4
作者：
Hartmann, Michael J.;Singh, Yuvraj;Hocky, Glen M.
通讯作者：
Hocky, Glen M.
影响因子：
3.3
作者：
Jain, Abhinav;Stock, Gerhard
通讯作者：
Stock, Gerhard
影响因子：
46.9
作者：
Do, Chuong B.;Batzoglou, Serafim
通讯作者：
Batzoglou, Serafim
DOI：
10.1073/pnas.0502495102
发表时间：
2005-05-24
影响因子：
11.1
作者：
Chiu, TK;Kubelka, J;Davies, DR
通讯作者：
Davies, DR