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从实验设计到验证的活性化合物:使用表面等离子体共振进行片段先导物识别的全面指南

From experimental design to validated hits a comprehensive walk-through of fragment lead identification using surface plasmon resonance.

作者信息

Giannetti Anthony M

机构信息

Genentech Inc., 1 DNA Way, South San Francisco, California, USA.

出版信息

Methods Enzymol. 2011;493:169-218. doi: 10.1016/B978-0-12-381274-2.00008-X.

Abstract

The detection and characterization of fragment binding requires the use of technologies with extreme sensitivity to observe the binding interactions of low-affinity and low-molecular weight compounds to proteins. A number of methods have emerged capable of providing fragment hits to project teams including, but certainly not limited to, NMR, X-ray crystallography, and surface plasmon resonance (SPR). SPR-based biosensors are sufficiently sensitive and high throughput to provide complete fragment screens on libraries of several thousand compounds in just a few weeks per target. Biosensors provide quantitative binding information for ranking fragments by affinity and ligand efficiency and can support ongoing quantitative structure-activity efforts during fragment hit-to-lead development. The combination of speed and binding quantitation makes SPR a valuable technology in pharmaceutical fragment-based drug discovery and development. Successful implementation of SPR biosensors in fragment efforts requires specialized methods for instrument preparation, assay development, primary compound handling, primary screening, confirmation testing, and data analysis. In this chapter, each of these topics is discussed in detail with general best practices for maintaining the highest throughput while maximizing data quality.

摘要

片段结合的检测与表征需要使用具有极高灵敏度的技术,以观察低亲和力和低分子量化合物与蛋白质的结合相互作用。已经出现了许多能够为项目团队提供片段命中物的方法,包括但不限于核磁共振(NMR)、X射线晶体学和表面等离子体共振(SPR)。基于SPR的生物传感器具有足够的灵敏度和高通量,能够在短短几周内针对每个靶点对数千种化合物的文库进行完整的片段筛选。生物传感器提供定量结合信息,以便根据亲和力和配体效率对片段进行排名,并能够在片段从命中物到先导物的开发过程中支持正在进行的定量构效关系研究。速度和结合定量的结合使得SPR成为药物基于片段的药物发现和开发中的一项有价值的技术。在片段研究中成功实施SPR生物传感器需要用于仪器准备、分析方法开发、原始化合物处理、初步筛选、确认测试和数据分析的专门方法。在本章中,将详细讨论这些主题中的每一个,并介绍保持最高通量同时最大化数据质量的一般最佳实践。

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