Three-dimensional Krawtchouk descriptors for protein local surface shape comparison
Three-dimensional Krawtchouk descriptors for protein local surface shape comparison
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DOI：
10.1016/j.patcog.2019.05.019
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发表时间：
2018-12
影响因子：
8
通讯作者：
Atilla Sit;Woong-Hee Shin;D. Kihara
中科院分区：
文献类型：
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作者：
Atilla Sit;Woong-Hee Shin;D. Kihara
由于需要对对象进行平移、旋转和缩放来评估其相似性，因此直接比较三维（3D）对象的计算成本很高。在3D对象比较的应用中，通常识别对象的特定局部区域是特别感兴趣的。我们最近开发了一套基于离散正交Krawtchouk多项式的二维不变矩，用于比较局部图像补丁。在这项工作中，我们将它们扩展到3D和构建3D Krawtchouk描述符（3DKDs）是不变的平移，旋转和缩放。新的描述符有能力从任何感兴趣的区域中提取3D表面的局部特征。此属性允许比较来自不同3D对象的两个任意局部曲面区域。我们提出了新的配方的3DKD，并将其应用于蛋白质表面的局部形状比较，以预测结合查询蛋白质的配体分子。
Direct comparison of three-dimensional (3D) objects is computationally expensive due to the need for translation, rotation, and scaling of the objects to evaluate their similarity. In applications of 3D object comparison, often identifying specific local regions of objects is of particular interest. We have recently developed a set of 2D moment invariants based on discrete orthogonal Krawtchouk polynomials for comparison of local image patches. In this work, we extend them to 3D and construct 3D Krawtchouk descriptors (3DKDs) that are invariant under translation, rotation, and scaling. The new descriptors have the ability to extract local features of a 3D surface from any region-of-interest. This property enables comparison of two arbitrary local surface regions from different 3D objects. We present the new formulation of 3DKDs and apply it to the local shape comparison of protein surfaces in order to predict ligand molecules that bind to query proteins.