El-Hachem Nehme, Haibe-Kains Benjamin, Khalil Athar, Kobeissy Firas H, Nemer Georges
Integrative Computational Systems Biology, Institut de Recherches Cliniques de Montreal, Montreal, QC, Canada.
Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
Methods Mol Biol. 2017;1598:391-403. doi: 10.1007/978-1-4939-6952-4_20.
Computational docking and scoring techniques have revolutionized structural bioinformatics by providing unprecedented insights on key aspects of ligand-receptor interaction. Docking is used for optimizing known drugs and for identifying novel binders by predicting their binding mode and affinity. AutoDock and AutoDockTools are free of charge techniques that have been extensively cited in the literature as essential tools in structure-based drug design. Moreover, these methods are fast enough to permit virtual screening of ligand libraries containing tens of thousands of compounds. However using Autodock requires some knowledge in programming which creates a limitation for biologists and makes them prone for commercial applications. Here, we selected a relevant target involved in the progression of Alzheimer disease and provided a fully reproducible docking protocol. This example will show how docking techniques would be an important asset to identify new BACE1 inhibitors. The following friendly user tutorial targets both undergraduate and graduate students, allowing them to understand docking as a computational tool for structure-based drug design.
计算对接和评分技术通过提供有关配体-受体相互作用关键方面的前所未有的见解，彻底改变了结构生物信息学。对接用于优化已知药物，并通过预测其结合模式和亲和力来识别新型结合剂。AutoDock和AutoDockTools是免费技术，在文献中被广泛引用为基于结构的药物设计的重要工具。此外，这些方法速度足够快，可以对包含数万种化合物的配体库进行虚拟筛选。然而，使用AutoDock需要一些编程知识，这对生物学家来说是一个限制，使他们倾向于商业应用。在这里，我们选择了一个与阿尔茨海默病进展相关的相关靶点，并提供了一个完全可重复的对接方案。这个例子将展示对接技术如何成为识别新的β-分泌酶1（BACE1）抑制剂的重要资产。以下友好的用户教程面向本科生和研究生，使他们能够将对接理解为基于结构的药物设计的计算工具。
