Anighoro Andrew, de la Vega de León Antonio, Bajorath Jürgen
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, 53113, Bonn, Germany.
J Comput Aided Mol Des. 2016 Oct;30(10):841-849. doi: 10.1007/s10822-016-9973-5. Epub 2016 Sep 21.
Macrocyclic compounds experience increasing interest in drug discovery. It is often thought that these large and chemically complex molecules provide promising candidates to address difficult targets and interfere with protein-protein interactions. From a computational viewpoint, these molecules are difficult to treat. For example, flexible docking of macrocyclic compounds is hindered by the limited ability of current docking approaches to optimize conformations of extended ring systems for pose prediction. Herein, we report predictions of bioactive conformations of macrocycles using conformational search and binding modes using docking. Conformational ensembles generated using specialized search technique of about 70 % of the tested macrocycles contained accurate bioactive conformations. However, these conformations were difficult to identify on the basis of conformational energies. Moreover, docking calculations with limited ligand flexibility starting from individual low energy conformations rarely yielded highly accurate binding modes. In about 40 % of the test cases, binding modes were approximated with reasonable accuracy. However, when conformational ensembles were subjected to rigid body docking, an increase in meaningful binding mode predictions to more than 50 % of the test cases was observed. Electrostatic effects did not contribute to these predictions in a positive or negative manner. Rather, achieving shape complementarity at macrocycle-target interfaces was a decisive factor. In summary, a combined computational protocol using pre-computed conformational ensembles of macrocycles as a starting point for docking shows promise in modeling binding modes of macrocyclic compounds.
大环化合物在药物研发中越来越受到关注。人们通常认为，这些大的且化学结构复杂的分子为攻克棘手靶点和干扰蛋白质-蛋白质相互作用提供了有前景的候选物。从计算的角度来看，这些分子很难处理。例如，大环化合物的柔性对接受到当前对接方法在优化用于构象预测的扩展环系统构象方面能力有限的阻碍。在此，我们报告了使用构象搜索对大环化合物生物活性构象的预测以及使用对接对其结合模式的预测。使用专门搜索技术生成的构象集合中，约70%的测试大环化合物包含准确的生物活性构象。然而，基于构象能量很难识别这些构象。此外，从单个低能构象开始进行有限配体柔性的对接计算很少能产生高度准确的结合模式。在约40%的测试案例中，结合模式能以合理的准确度进行近似。然而，当对构象集合进行刚体对接时，观察到有意义的结合模式预测增加到超过50%的测试案例。静电效应并未对这些预测产生积极或消极的影响。相反，在大环-靶点界面实现形状互补是一个决定性因素。总之，使用预先计算的大环化合物构象集合作为对接起点的组合计算方案在模拟大环化合物的结合模式方面显示出前景。
