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Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

机译:基于结构的药物发现虚拟筛选:原理,应用和最新进展

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Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of top-scoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3K alpha kinase. Second, we discuss a strategy for the identification of selective binders for the RXR alpha nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.
机译:基于结构的药物发现(SBDD)成为协助快速且经济高效的线索发现和优化的重要工具。事实证明,合理的,基于结构的药物设计方法比传统的药物发现方法更有效,因为它旨在了解疾病的分子基础并利用生物学靶标中三维靶标的三维结构知识。处理。在这篇综述中,我们将重点关注SBDD范围内虚拟筛选(VS)的原理和应用,并研究不同的程序,从包括受体和文库预处理在内的整个过程的初始阶段,到对接,评分和后期处理。处理得分最高的匹配。还讨论了通过集成对接,诱导拟合和共识对接在基于结构的虚拟筛选(SBVS)效率方面的最新改进。这篇综述突出了在成功导致VS直接抑制nM的几项成功研究的框架内该领域的进展,并提供了图书馆设计的最新趋势以及该方法的局限性。还综述了SBVS在工程蛋白底物设计中的应用,这些蛋白能够发现新的代谢和信号转导途径,并设计了多功能蛋白的抑制剂。最后,我们贡献了我们最近开发的两个有希望的VS方案,旨在增加抑制剂的选择性。在第一个协议中,我们描述了通过旨在抑制突变型H1047R PI3Kα激酶的SBVS发现微摩尔抑制剂的过程。其次,我们讨论了识别RXRα核受体的选择性结合剂的策略。在该协议中,基于结合位点形状表征和聚类,构建了一组用于对接的靶结构,旨在通过SBVS过程提高针对所需蛋白靶的选择性抑制剂的命中率。

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