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A mathematical representation of protein binding sites using structural dispersion of atoms from principal axes for classification of binding ligands

机译：蛋白质结合位点的数学表示使用原子的结构分散来自主轴的结合配体分类

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Many researchers have studied the relationship between the biological functions of proteins and the structures of both their overall backbones of amino acids and their binding sites. A large amount of the work has focused on summarizing structural features of binding sites as scalar quantities, which can result in a great deal of information loss since the structures are three-dimensional. Additionally, a common way of comparing binding sites is via aligning their atoms, which is a computationally intensive procedure that substantially limits the types of analysis and modeling that can be done. In this work, we develop a novel encoding of binding sites as covariance matrices of the distances of atoms to the principal axes of the structures. This representation is invariant to the chosen coordinate system for the atoms in the binding sites, which removes the need to align the sites to a common coordinate system, is computationally efficient, and permits the development of probability models. These can then be used to both better understand groups of binding sites that bind to the same ligand and perform classification for these ligand groups. We demonstrate the utility of our method for discrimination of binding ligand through classification studies with two benchmark datasets using nearest mean and polytomous logistic regression classifiers.
机译：许多研究人员研究了蛋白质生物学功能与其整体骨干的结构与其结合位点之间的关系。大量的工作已经侧重于将绑定部位的结构特征概括为标量数量，这可能导致由于结构是三维的，因此可以导致大量信息丢失。另外，比较粘合位点的常见方式是通过对准它们的原子，这是一种计算密集型过程，其基本上限制了可以完成的分析和建模的类型。在这项工作中，我们开发了绑定站点的新颖编码，作为原子距离到结构的主要轴的距离的协方差矩阵。该表示是不变的，绑定站点中的原子中所选择的坐标系，其去除将站点对准到公共坐标系的需要，是计算有效的，并且允许开发概率模型。然后可以用于更好地了解结合到相同配体的结合位点和对这些配体基团进行分类的结合位点。我们证明了我们通过使用最近平均值和多种物流回归分类器的两个基准数据集来辨别结合配体的方法的效用。

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