The presence of receptors and the specific binding of the ligands determine nearly all cellular responses. Binding of a ligand to its receptor causes conformational changes of the receptor that triggers the subsequent signaling cascade. Therefore, systematically studying structures of receptors will provide insight into their functions. We have developed the triangular spatial relationship (TSR)-based method where all possible triangles are constructed with C_α atoms of a protein as vertices. Every triangle is represented by an integer denoted as a “key” computed through the TSR algorithm. A structure is thereby represented by a vector of integers. In this study, we have first defined substructures using different types of keys. Second, using different types of keys represents a new way to interpret structure hierarchical relations and differences between structures and sequences. Third, we demonstrate the effects of sequence similarity as well as sample size on the structure-based classifications. Fourth, we show identification of structure motifs, and the motifs containing multiple triangles connected by either an edge or a vertex are mapped to the ligand binding sites of the receptors. The structure motifs are valuable resources for the researchers in the field of signal transduction. Next, we propose amino-acid scoring matrices that capture “evolutionary closeness” information based on BLOSUM62 matrix, and present the development of a new visualization method where keys are organized according to evolutionary closeness and shown in a 2D image. This new visualization opens a window for developing tools with the aim of identification of specific and common substructures by scanning pixels and neighboring pixels. Finally, we report a new algorithm called as size filtering that is designed to improve structure comparison of large proteins with small proteins. Collectively, we provide an in-depth interpretation of structure relations through the detailed analyses of different types of keys and their associated key occurrence frequencies, geometries, and labels. In summary, we consider this study as a new computational platform where keys are served as a bridge to connect sequence and structure as well as structure and function for a deep understanding of sequence, structure, and function relationships of the protein family.

中文翻译：

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使用基于三角空间关系的结构比较方法研究蛋白质的层次结构和受体的配体结合位点，并开发用于比较不同大小的蛋白质结构的大小过滤特征

受体的存在和配体的特异性结合决定了几乎所有的细胞反应。配体与其受体的结合会引起受体的构象变化，从而触发随后的信号级联反应。因此，研究受体的结构将有助于深入了解它们的功能。我们开发了基于三角形空间关系 (TSR) 的方法，其中所有可能的三角形都用 C _α构造蛋白质的原子作为顶点。每个三角形都由一个整数表示，该整数表示为通过 TSR 算法计算的“密钥”。因此，结构由整数向量表示。在这项研究中，我们首先使用不同类型的键定义了子结构。其次，使用不同类型的键代表了一种解释结构层次关系以及结构和序列之间差异的新方法。第三，我们展示了序列相似性以及样本大小对基于结构的分类的影响。第四，我们展示了结构基序的识别，包含由边或顶点连接的多个三角形的基序被映射到受体的配体结合位点。结构基序是信号传输领域研究人员的宝贵资源。下一个，我们提出了基于 BLOSUM62 矩阵捕获“进化接近度”信息的氨基酸评分矩阵，并提出了一种新的可视化方法的开发，其中键根据进化接近度组织并显示在 2D 图像中。这种新的可视化为开发工具打开了一个窗口，目的是通过扫描像素和相邻像素来识别特定和常见的子结构。最后，我们报告了一种称为尺寸过滤的新算法，旨在改进大蛋白质与小蛋白质的结构比较。总的来说，我们通过对不同类型的键及其相关键出现频率、几何形状和标签的详细分析，对结构关系进行了深入的解释。总之，