Bio3D是一款依托于R语言的工具包，能够分析生物大分子的序列，结构和模拟结果等数据。本文将记录Bio3D中”md”这个demo的运算流程。

Note:

• R中，关于每一个函数的详细文档和示例，可通过 help() 和 example() 函数进行查看。比如，help(read.pdb)。
• 示例文件中分析的是对HIVpr的模拟轨迹，此轨迹为CHARMM/NAMD DCD 格式，并且只包含CA原子。

# Principal Component Analysis

Bio3D中，可使用 pca.xyz() 或者 pca.tor() 进行PCA的分析。

L为特征值(从大到小排列)，U为特征向量矩阵(每一列为一个归一化的特征向量)，au的维度为原子数目*特征向量个数，每一列表示不同原子对某一个pc的贡献值。

Below we perform a quick clustering in PC-space to further highlight these distinct conformers.

To further aid interpretation, a PDB format trajectory can be produced that interpolates between the most dissimilar structures in the distribution along a given principal component. This involves dividing the difference between the conformers into a number of evenly spaced steps along the principal components, forming the frames of the output multi-model PDB trajectory.
Such trajectories can be directly visualized in a molecular graphics program, such as VMD (Humphrey 1996).
Furthermore, the interpolated structures can be analyzed for possible domain and shear movements with other Bio3D functions, or used as initial seed structures for reaction path refinement methods (note you will likely want to perform all heavy atom PCA for such applications).

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