Deng Zhan, Chuaqui Claudio, Singh Juswinder
Department of Structural Informatics, Biogen, Inc., 12 Cambridge Center, Cambridge, Massachusetts 02142, USA.
J Med Chem. 2004 Jan 15;47(2):337-44. doi: 10.1021/jm030331x.
Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based docking experiments. Here, we present SIFt (structural interaction fingerprint), a novel method for representing and analyzing 3D protein-ligand binding interactions. Key to this approach is the generation of an interaction fingerprint that translates 3D structural binding information from a protein-ligand complex into a one-dimensional binary string. Each fingerprint represents the "structural interaction profile" of the complex that can be used to organize, analyze, and visualize the rich amount of information encoded in ligand-receptor complexes and also to assist database mining. We have applied SIFt to tackle three common tasks in structure-based drug design. The first involved the analysis and organization of a typical set of results generated from a docking study. Using SIFt, docking poses with similar binding modes were identified, clustered, and subsequently compared with conventional scoring function information. A second application of SIFt was to analyze approximately 90 known X-ray crystal structures of protein kinase-inhibitor complexes obtained from the Protein Databank. Using SIFt, we were able to organize the structures and reveal striking similarities and diversity between their small molecule binding interactions. Finally, we have shown how SIFt can be used as an effective molecular filter during the virtual chemical library screening process to select molecules with desirable binding mode(s) and/or desirable interaction patterns with the protein target. In summary, SIFt shows promise to fully leverage the wealth of information being generated in rational drug design.
表征和理解蛋白质-配体复合物的三维（3D）结构信息是合理药物发现过程中的关键步骤。传统分析方法在处理由X射线晶体学、核磁共振（NMR）以及基于结构的对接实验等计算机方法产生的大量结构信息时，已证明存在不足且效率低下。在此，我们提出了SIFt（结构相互作用指纹），这是一种用于表征和分析3D蛋白质-配体结合相互作用的新方法。该方法的关键在于生成一种相互作用指纹，它将蛋白质-配体复合物的3D结构结合信息转化为一维二进制字符串。每个指纹代表复合物的“结构相互作用概况”，可用于整理、分析和可视化配体-受体复合物中编码的大量信息，还可辅助数据库挖掘。我们已将SIFt应用于解决基于结构的药物设计中的三个常见任务。第一个任务涉及对对接研究产生的一组典型结果进行分析和整理。使用SIFt，识别并聚类了具有相似结合模式的对接姿势，随后将其与传统评分函数信息进行比较。SIFt的第二个应用是分析从蛋白质数据库获得的约90个已知的蛋白激酶-抑制剂复合物的X射线晶体结构。使用SIFt，我们能够整理这些结构，并揭示它们小分子结合相互作用之间显著的相似性和多样性。最后，我们展示了SIFt如何在虚拟化学库筛选过程中用作有效的分子过滤器，以选择具有理想结合模式和/或与蛋白质靶点具有理想相互作用模式的分子。总之，SIFt有望充分利用合理药物设计中产生的丰富信息。
