Chuaqui Claudio, Deng Zhan, Singh Juswinder
Computational Drug Design Group, Department of Research Informatics, Biogen Idec, Inc., 14 Cambridge Center, Cambridge, Massachusetts 01242, USA.
J Med Chem. 2005 Jan 13;48(1):121-33. doi: 10.1021/jm049312t.
A major challenge facing structure-based drug discovery efforts is how to leverage the massive amount of experimental (X-ray and NMR) and virtual structural information generated from drug discovery projects. Many important drug targets have large numbers of protein-inhibitor complexes, necessitating tools to compare and contrast their similarities and differences. This information would be valuable for understanding potency and selectivity of inhibitors and could be used to define target constraints to assist virtual screening. We describe a profile-based approach that enables us to capture the conservation of interactions between a set of protein-ligand receptor complexes. The use of profiles provides a sensitive means to compare multiple inhibitors binding to a drug target. We demonstrate the utility of profile-based analysis of small molecule complexes from the protein-kinase family to identify similarities and differences in binding of ATP, p38, and CDK2 compounds to kinases and how these profiles can be applied to differentiate the selectivity of these inhibitors. Importantly, our virtual screening results demonstrate superior enrichment of kinase inhibitors using profile-based methods relative to traditional scoring functions. Interaction-based analysis should provide a valuable tool for understanding inhibitor binding to other important drug targets.
基于结构的药物发现工作面临的一个主要挑战是如何利用药物发现项目产生的大量实验性(X射线和核磁共振)及虚拟结构信息。许多重要的药物靶点有大量的蛋白质-抑制剂复合物,因此需要工具来比较和对比它们的异同。这些信息对于理解抑制剂的效力和选择性很有价值,并且可用于定义靶点限制条件以辅助虚拟筛选。我们描述了一种基于图谱的方法,该方法能使我们捕捉一组蛋白质-配体受体复合物之间相互作用的保守性。图谱的使用提供了一种灵敏的手段来比较结合到药物靶点的多种抑制剂。我们展示了基于图谱分析蛋白激酶家族小分子复合物的效用,以识别ATP、p38和CDK2化合物与激酶结合的异同,以及这些图谱如何用于区分这些抑制剂的选择性。重要的是,我们的虚拟筛选结果表明,相对于传统评分函数,基于图谱的方法在激酶抑制剂富集方面表现更优。基于相互作用的分析应为理解抑制剂与其他重要药物靶点的结合提供一个有价值的工具。