Atom-specific persistent homology and its application to protein flexibility analysis.
Atom-specific persistent homology and its application to protein flexibility analysis.
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DOI：
10.1515/cmb-2020-0001
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发表时间：
2020-01
影响因子：
--
通讯作者：
Wei GW
中科院分区：
文献类型：
--
作者：
Bramer D;Wei GW
最近，持续同源性在生物分子数据分析中取得了巨大的成功。它的工作原理是在各种尺度下检查分子中一组原子的拓扑关系或连通性，然后绘制分子的拓扑表示族。然而，持久同源性很少用于原子性质的分析，例如生物分子柔性分析或B因子预测。这项工作引入了原子特异性的持久同源性，通过全局拓扑工具提供分子的局部原子水平表示。这是通过构造一对共轭原子集和相应的共轭单纯复形以及共轭拓扑空间来实现的。共轭集对的拓扑不变量之间的差异是衡量瓶颈和Wasserstein度量，并导致在分子中的单个原子性质的原子特定的拓扑表示。原子特定的拓扑特征与各种机器学习算法相结合，包括梯度提升树和卷积神经网络，用于蛋白质热波动分析和B因子预测。大量的数值结果表明，该方法为分析和预测复杂大分子中的局部信息提供了一个强大的拓扑工具。
Recently, persistent homology has had tremendous success in biomolecular data analysis. It works by examining the topological relationship or connectivity of a group of atoms in a molecule at a variety of scales, then rendering a family of topological representations of the molecule. However, persistent homology is rarely employed for the analysis of atomic properties, such as biomolecular flexibility analysis or B-factor prediction. This work introduces atom-specific persistent homology to provide a local atomic level representation of a molecule via a global topological tool. This is achieved through the construction of a pair of conjugated sets of atoms and corresponding conjugated simplicial complexes, as well as conjugated topological spaces. The difference between the topological invariants of the pair of conjugated sets is measured by Bottleneck and Wasserstein metrics and leads to an atom-specific topological representation of individual atomic properties in a molecule. Atom-specific topological features are integrated with various machine learning algorithms, including gradient boosting trees and convolutional neural network for protein thermal fluctuation analysis and B-factor prediction. Extensive numerical results indicate the proposed method provides a powerful topological tool for analyzing and predicting localized information in complex macromolecules.