INSERM UMR-S 665, Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), Université Paris Diderot-Paris 7, Institut National de la Transfusion Sanguine (INTS), 6, rue Alexandre Cabanel, 75739 Paris cedex 15, France.
Protein Sci. 2010 Apr;19(4):847-67. doi: 10.1002/pro.364.
Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.
配体-蛋白质相互作用对于生物过程至关重要，而精确描述蛋白质结合位点对于理解蛋白质功能至关重要。MED-SuMo 是一种强大的技术，可用于定位蛋白质表面上类似的局部区域。它的启发式方法基于使用特定表面化学特征将大分子表示为 3D，将化学特征与几何特性相关联。MED-SMA 是一种自动化且快速的分类结合位点的方法。它基于 MED-SuMo 技术构建相似性图，并使用 Markov 聚类算法。嘌呤结合位点是研究药物靶点的良好模型。在这里，对蛋白质数据库（PDB）中的嘌呤结合位点进行分类。收集具有相同机制的潜在抑制剂或激活剂的蛋白质。根据 PROSITE 注释和从 PDB 中仔细提取的功能注释对结果进行分析。正如预期的那样，聚集了与相关机制相关的结合位点，例如小分子 GTPases。然而，不同激酶组家族的蛋白激酶也聚集在一起，例如 Aurora-A 和 CDK2 蛋白，它们被相同的药物抑制。展示了不同簇的代表性示例。MED-SMA 方法的有效性得到了证明，因为它可以聚集具有相似结构-活性关系的蛋白质的结合位点。此外，一种有效的新协议将缺乏共结晶配体的结构与嘌呤簇相关联，从而可以将这些结构与特定的结合机制相关联。这种基于结合模式相似性的分类的应用包括基于靶标的药物设计以及预测交叉反应性，因此可能存在潜在的毒副作用。
