Nguyen Phuong H
Institute of Physical and Theoretical Chemistry, J. W. Goethe University, Max-von-Laue-Str. 7, D-60438 Frankfurt, Germany.
Proteins. 2007 May 15;67(3):579-92. doi: 10.1002/prot.21317.
Principal component analysis is a powerful method for projecting multidimensional conformational space of peptides or proteins onto lower dimensional subspaces in which the main conformations are present, making it easier to reveal the structures of molecules from e.g. molecular dynamics simulation trajectories. However, the identification of all conformational states is still difficult if the subspaces consist of more than two dimensions. This is mainly due to the fact that the principal components are not independent with each other, and states in the subspaces cannot be visualized. In this work, we propose a simple and fast scheme that allows one to obtain all conformational states in the subspaces. The basic idea is that instead of directly identifying the states in the subspace spanned by principal components, we first transform this subspace into another subspace formed by components that are independent of one other. These independent components are obtained from the principal components by employing the independent component analysis method. Because of independence between components, all states in this new subspace are defined as all possible combinations of the states obtained from each single independent component. This makes the conformational analysis much simpler. We test the performance of the method by analyzing the conformations of the glycine tripeptide and the alanine hexapeptide. The analyses show that our method is simple and quickly reveal all conformational states in the subspaces. The folding pathways between the identified states of the alanine hexapeptide are analyzed and discussed in some detail.
主成分分析是一种强大的方法，可将肽或蛋白质的多维构象空间投影到存在主要构象的低维子空间上，从而更容易从例如分子动力学模拟轨迹中揭示分子结构。然而，如果子空间由超过两个维度组成，识别所有构象状态仍然很困难。这主要是由于主成分彼此不独立，并且子空间中的状态无法可视化。在这项工作中，我们提出了一种简单快速的方案，该方案允许人们获得子空间中的所有构象状态。基本思想是，我们不是直接在由主成分所跨越的子空间中识别状态，而是首先将这个子空间变换为另一个由相互独立的成分所形成的子空间。这些独立成分是通过使用独立成分分析方法从主成分中获得的。由于成分之间的独立性，这个新子空间中的所有状态都被定义为从每个单个独立成分中获得的状态的所有可能组合。这使得构象分析更加简单。我们通过分析甘氨酸三肽和丙氨酸六肽的构象来测试该方法的性能。分析表明，我们的方法简单且能快速揭示子空间中的所有构象状态。我们还对丙氨酸六肽已识别状态之间的折叠途径进行了较为详细的分析和讨论。
