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PROTEIN STRUCTURE–STRUCTURE ALIGNMENT WITH DISCRETE FRéCHET DISTANCE

机译:离散距离的蛋白质结构-结构对齐

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摘要

Matching two geometric objects in two-dimensional (2D) and three-dimensional (3D) spaces is a central problem in computer vision, pattern recognition, and protein structure prediction. In particular, the problem of aligning two polygonal chains under translation and rotation to minimize their distance has been studied using various distance measures. It is well known that the Hausdorff distance is useful for matching two point sets, and that the Fréchet distance is a superior measure for matching two polygonal chains. The discrete Fréchet distance closely approximates the (continuous) Fréchet distance, and is a natural measure for the geometric similarity of the folded 3D structures of biomolecules such as proteins. In this paper, we present new algorithms for matching two polygonal chains in two dimensions to minimize their discrete Fréchet distance under translation and rotation, and an effective heuristic for matching two polygonal chains in three dimensions. We alsodescribe our empirical results on the application of the discrete Fréchet distance to protein structure–structure alignment.
机译:在二维(2D)和三维(3D)空间中匹配两个几何对象是计算机视觉,模式识别和蛋白质结构预测中的核心问题。特别地,已经使用各种距离量度研究了在平移和旋转下对准两个多边形链以最小化它们的距离的问题。众所周知,Hausdorff距离对于匹配两个点集很有用,而Fréchet距离对于匹配两个多边形链是一种更好的度量。离散Fréchet距离非常接近(连续)Fréchet距离,并且是生物分子(如蛋白质)折叠3D结构的几何相似性的自然度量。在本文中,我们提出了在二维上匹配两个多边形链以最小化它们在平移和旋转下的离散Fréchet距离的新算法,以及在三个维度上匹配两个多边形链的有效启发式算法。我们还描述了将离散Fréchet距离应用于蛋白质结构-结构比对的经验结果。

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