School of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China.
School of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Gene. 2018 Jul 15;663:138-147. doi: 10.1016/j.gene.2018.04.049. Epub 2018 Apr 21.
The analysis of protein similarity is a matter of concern in the bioinformatics field, since studying the protein similarity can help understand the protein structure-function relationship. To this aim, several methods have been proposed, but currently, protein similarity results are still not satisfactory. Here we presented a novel method for evaluating the similarity of 3D protein models based on hybrid features, including the local diameter (LD), the salient geometric feature (SGF) and the heat kernel signature (HKS). LD is suitable to the topological deformation of 3D models, SGF is an important local feature on the protein model surface, and HKS is invariant under isometric deformations. Our method provides the improved feature extraction procedure to calculate LD, SGF and HKS of a protein model, and then uses these features to construct a tensor based feature descriptor for 3D protein models. The method finally analyzes the similarity of 3D protein models by using this tensor descriptor and the extended grey relation analysis. Experimental data indicated that our method is effective and can outperform the existing similarity analysis results obtained by previously reported methods.
蛋白质相似性分析是生物信息学领域关注的问题，因为研究蛋白质相似性有助于理解蛋白质的结构-功能关系。为此，已经提出了几种方法，但目前蛋白质相似性的结果仍然不尽如人意。在这里，我们提出了一种基于混合特征（包括局部直径（LD）、显著几何特征（SGF）和热核签名（HKS））评估 3D 蛋白质模型相似性的新方法。LD 适用于 3D 模型的拓扑变形，SGF 是蛋白质模型表面的重要局部特征，HKS 在等距变形下是不变的。我们的方法提供了改进的特征提取过程来计算蛋白质模型的 LD、SGF 和 HKS，然后使用这些特征构建基于张量的 3D 蛋白质模型特征描述符。该方法最终通过使用这个张量描述符和扩展的灰色关联分析来分析 3D 蛋白质模型的相似性。实验数据表明，我们的方法是有效的，可以优于以前报道的方法获得的现有相似性分析结果。
