Wagner Jeffrey R, Lee Christopher T, Durrant Jacob D, Malmstrom Robert D, Feher Victoria A, Amaro Rommie E
Department of Chemistry & Biochemistry and ‡National Biomedical Computation Resource, University of California, San Diego , La Jolla, California 92093, United States.
Chem Rev. 2016 Jun 8;116(11):6370-90. doi: 10.1021/acs.chemrev.5b00631. Epub 2016 Apr 13.
Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.
变构药物开发有望提供比靶向正构位点的药物更具选择性且毒性更低的药物。迄今为止，变构结合位点和先导化合物的发现大多是偶然的，是通过高通量筛选实现的。在过去十年中，对于更大的蛋白质系统和更多的膜蛋白类别（如G蛋白偶联受体和离子通道），结构数据变得更容易获得，而这些都是常见的变构药物靶点。与此同时，改进的模拟方法现在能对蛋白质动力学和协同运动提供更好的原子水平理解，而这些对于变构机制至关重要。由于这些进展，预测性变构药物开发领域现在正处于基于合理结构的计算方法新时代的边缘。在此，我们回顾基于序列数据和分子动力学模拟预测变构位点的算法，描述评估这些口袋可成药性的工具，并讨论马尔可夫状态模型和拓扑分析如何深入了解蛋白质动力学与变构药物结合之间的关系。在每个部分中，我们首先概述各种方法类别，然后再描述相关算法和软件包。
