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基于负图像的筛选:利用腔信息进行刚性对接。

Negative Image-Based Screening: Rigid Docking Using Cavity Information.

作者信息

Postila Pekka A, Kurkinen Sami T, Pentikäinen Olli T

机构信息

Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, Turku, Finland.

Aurlide Ltd., Turku, Finland.

出版信息

Methods Mol Biol. 2021;2266:125-140. doi: 10.1007/978-1-0716-1209-5_7.

Abstract

Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.

摘要

合理的药物发现严重依赖基于分子对接的虚拟筛选,这种方法可以灵活地针对目标蛋白质的结构对配体结合姿势进行采样。灵活对接的优点是生成的对接姿势的几何形状会进行调整,以匹配目标蛋白质配体结合口袋内的残基排列。缺点是灵活对接需要大量计算资源,而且无论如何,要获得相当程度的富集通常需要进一步重新评分或后处理。基于负像的筛选是一种刚性对接技术,速度极快且计算量小,但大量的基准测试和筛选实验证明其同样有效。在基于负像的筛选中,目标蛋白质腔的形状/静电学与从头生成的配体三维构象进行比对。在本章中,将从实践层面解释基于负像的筛选方法,并坦率地讨论其优缺点。

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