Development of protein 3-D structural comparison methods is important in understanding protein functions. At the same time, developing such a method is very challenging. In the last 40 years, ever since the development of the first automated structural method, ~200 papers were published using different representations of structures. The existing methods can be divided into five categories: sequence-, distance-, secondary structure-, geometry-based, and network-based structural comparisons. Each has its uniqueness, but also limitations. We have developed a novel method where the 3-D structure of a protein is modeled using the concept of Triangular Spatial Relationship (TSR), where triangles are constructed with the C_α atoms of a protein as vertices. Every triangle is represented using an integer, which we denote as “key,” A key is computed using the length, angle, and vertex labels based on a rule-based formula, which ensures assignment of the same key to identical TSRs across proteins. A structure is thereby represented by a vector of integers. Our method is able to accurately quantify similarity of structure or substructure by matching numbers of identical keys between two proteins. The uniqueness of our method includes: (i) a unique way to represent structures to avoid performing structural superimposition; (ii) use of triangles to represent substructures as it is the simplest primitive to capture shape; (iii) complex structure comparison is achieved by matching integers corresponding to multiple TSRs. Every substructure of one protein is compared to every other substructure in a different protein. The method is used in the studies of proteases and kinases because they play essential roles in cell signaling, and a majority of these constitute drug targets. The new motifs or substructures we identified specifically for proteases and kinases provide a deeper insight into their structural relations. Furthermore, the method provides a unique way to study protein conformational changes. In addition, the results from CATH and SCOP data sets clearly demonstrate that our method can distinguish alpha helices from beta pleated sheets and vice versa. Our method has the potential to be developed into a powerful tool for efficient structure-BLAST search and comparison, just as BLAST is for sequence search and alignment.

蛋白质3-D结构比较方法的开发对于理解蛋白质功能很重要。同时，开发这种方法非常具有挑战性。在过去的40年中，自从第一种自动化结构方法的开发以来，使用结构的不同表示形式发表了约200篇论文。现有的方法可以分为五类：顺序，距离，二级结构，基于几何和基于网络的结构比较。每个都有其独特性，也有局限性。我们已经开发出其中的蛋白的3-d结构使用三角空间关系（TSR），其中三角形构造与C的概念模型化的新方法_α蛋白质的原子作为顶点。每个三角形均使用整数表示，我们将其表示为“键”。根据基于规则的公式，使用长度，角度和顶点标签来计算键，以确保将相同的键分配给跨蛋白质的相同TSR。因此，结构由整数向量表示。我们的方法能够通过匹配两个蛋白质之间相同键的数量来准确地量化结构或亚结构的相似性。我们方法的独特性包括：（i）一种独特的方式来表示结构，以避免执行结构叠加；（ii）使用三角形表示子结构，因为它是捕获形状的最简单图元；（iii）通过匹配对应于多个TSR的整数来实现复杂的结构比较。将一种蛋白质的每个子结构与不同蛋白质中的其他每个子结构进行比较。该方法用于蛋白酶和激酶的研究，因为它们在细胞信号传导中起着至关重要的作用，并且其中大多数构成了药物靶标。我们专门为蛋白酶和激酶确定的新基序或亚结构为它们的结构关系提供了更深入的了解。此外，该方法提供了研究蛋白质构象变化的独特方法。此外，CATH和SCOP数据集的结果清楚地表明，我们的方法可以区分β褶皱床单和 我们专门为蛋白酶和激酶确定的新基序或亚结构为它们的结构关系提供了更深入的了解。此外，该方法提供了研究蛋白质构象变化的独特方法。此外，CATH和SCOP数据集的结果清楚地表明，我们的方法可以区分β褶皱床单和 我们专门为蛋白酶和激酶确定的新基序或亚结构为它们的结构关系提供了更深入的了解。此外，该方法提供了研究蛋白质构象变化的独特方法。此外，CATH和SCOP数据集的结果清楚地表明，我们的方法可以区分β褶皱床单和反之亦然。就像BLAST用于序列搜索和比对一样，我们的方法有可能被开发为用于高效结构-BLAST搜索和比较的强大工具。