Ioerger T R, Holton T, Christopher J A, Sacchettini J C
Department of Computer Science, Texas A&M University, USA.
Proc Int Conf Intell Syst Mol Biol. 1999:130-7.
X-ray crystallography is the most widely used method for determining the three-dimensional structures of proteins and other macromolecules. One of the most difficult steps in crystallography is interpreting the electron density map to build the final model. This is often done manually by crystallographers and is very time-consuming and error-prone. In this paper, we introduce a new automated system called TEXTAL for interpreting electron density maps using pattern recognition. Given a map to be modeled, TEXTAL divides the map into small regions and then finds regions with a similar pattern of density in a database of maps for proteins whose structures have already been solved. When a match is found, the coordinates of atoms in the region are inferred by analogy. The key to making the database lookup efficient is to extract numeric features that represent the patterns in each region and to compare feature values using a weighted Euclidean distance metric. It is crucial that the features be rotation-invariant, since regions with similar patterns of density can be oriented in any arbitrary way. This pattern-recognition approach can take advantage of data accumulated in large crystallographic databases to effectively learn the association between electron density and molecular structure by example.
X射线晶体学是用于确定蛋白质和其他大分子三维结构的最广泛使用的方法。晶体学中最困难的步骤之一是解释电子密度图以构建最终模型。这通常由晶体学家手动完成，非常耗时且容易出错。在本文中，我们介绍了一种名为TEXTAL的新自动化系统，用于使用模式识别来解释电子密度图。给定一个要建模的图，TEXTAL将该图划分为小区域，然后在已解决结构的蛋白质图数据库中找到具有相似密度模式的区域。当找到匹配项时，通过类比推断该区域中原子的坐标。使数据库查找高效的关键是提取代表每个区域模式的数字特征，并使用加权欧几里得距离度量比较特征值。特征必须是旋转不变的，这一点至关重要，因为具有相似密度模式的区域可以以任何任意方式定向。这种模式识别方法可以利用大型晶体学数据库中积累的数据，通过示例有效地学习电子密度与分子结构之间的关联。
