Bioinformatics Institute, 30 Biopolis Street, #07-01 Matrix, Singapore 138671.
Nucleic Acids Res. 2011 Aug;39(14):e94. doi: 10.1093/nar/gkr348. Epub 2011 May 19.
Comparing and classifying the three-dimensional (3D) structures of proteins is of crucial importance to molecular biology, from helping to determine the function of a protein to determining its evolutionary relationships. Traditionally, 3D structures are classified into groups of families that closely resemble the grouping according to their primary sequence. However, significant structural similarities exist at multiple levels between proteins that belong to these different structural families. In this study, we propose a new algorithm, CLICK, to capture such similarities. The method optimally superimposes a pair of protein structures independent of topology. Amino acid residues are represented by the Cartesian coordinates of a representative point (usually the C(α) atom), side chain solvent accessibility, and secondary structure. Structural comparison is effected by matching cliques of points. CLICK was extensively benchmarked for alignment accuracy on four different sets: (i) 9537 pair-wise alignments between two structures with the same topology; (ii) 64 alignments from set (i) that were considered to constitute difficult alignment cases; (iii) 199 pair-wise alignments between proteins with similar structure but different topology; and (iv) 1275 pair-wise alignments of RNA structures. The accuracy of CLICK alignments was measured by the average structure overlap score and compared with other alignment methods, including HOMSTRAD, MUSTANG, Geometric Hashing, SALIGN, DALI, GANGSTA(+), FATCAT, ARTS and SARA. On average, CLICK produces pair-wise alignments that are either comparable or statistically significantly more accurate than all of these other methods. We have used CLICK to uncover relationships between (previously) unrelated proteins. These new biological insights include: (i) detecting hinge regions in proteins where domain or sub-domains show flexibility; (ii) discovering similar small molecule binding sites from proteins of different folds and (iii) discovering topological variants of known structural/sequence motifs. Our method can generally be applied to compare any pair of molecular structures represented in Cartesian coordinates as exemplified by the RNA structure superimposition benchmark.
比较和分类蛋白质的三维（3D）结构对于分子生物学至关重要，从帮助确定蛋白质的功能到确定其进化关系。传统上，3D 结构根据其一级序列分为密切相似的家族组。然而，属于这些不同结构家族的蛋白质之间在多个层次上存在显著的结构相似性。在这项研究中，我们提出了一种新的算法 CLICK，以捕捉这种相似性。该方法独立于拓扑优化地叠加一对蛋白质结构。氨基酸残基由代表点（通常为 Cα 原子）、侧链溶剂可及性和二级结构的笛卡尔坐标表示。结构比较通过匹配点的团块来实现。CLIQUE 在四个不同数据集上进行了广泛的对齐精度基准测试：（i）具有相同拓扑结构的两个结构之间的 9537 对对齐；（ii）从组（i）中选择的 64 个对齐，这些对齐被认为是构成困难对齐的案例；（iii）具有相似结构但不同拓扑的蛋白质之间的 199 对对齐；和（iv）1275 个 RNA 结构的对。CLIQUE 对齐的准确性通过平均结构重叠分数来衡量，并与其他对齐方法进行比较，包括 HOMSTRAD、MUSTANG、几何哈希、SALIGN、DALI、GANGSTA(+)、FATCAT、ARTS 和 SARA。平均而言，CLIQUE 产生的对比对方法要么可比，要么在统计学上明显更准确。我们已经使用 CLICK 发现了（以前）不相关的蛋白质之间的关系。这些新的生物学见解包括：（i）检测蛋白质中域或子域表现出灵活性的铰链区域；（ii）发现不同折叠的蛋白质中相似的小分子结合位点；（iii）发现已知结构/序列基序的拓扑变体。我们的方法通常可以应用于比较以笛卡尔坐标表示的任何一对分子结构，如 RNA 结构叠加基准测试所示。
