Clark Anthony J, Tiwary Pratyush, Borrelli Ken, Feng Shulu, Miller Edward B, Abel Robert, Friesner Richard A, Berne B J
Department of Chemistry, Columbia University , New York, New York 10027, United States.
Schrödinger, Inc. , 120 West 45th Street, New York, New York 10036, United States.
J Chem Theory Comput. 2016 Jun 14;12(6):2990-8. doi: 10.1021/acs.jctc.6b00201. Epub 2016 May 13.
Ligand docking is a widely used tool for lead discovery and binding mode prediction based drug discovery. The greatest challenges in docking occur when the receptor significantly reorganizes upon small molecule binding, thereby requiring an induced fit docking (IFD) approach in which the receptor is allowed to move in order to bind to the ligand optimally. IFD methods have had some success but suffer from a lack of reliability. Complementing IFD with all-atom molecular dynamics (MD) is a straightforward solution in principle but not in practice due to the severe time scale limitations of MD. Here we introduce a metadynamics plus IFD strategy for accurate and reliable prediction of the structures of protein-ligand complexes at a practically useful computational cost. Our strategy allows treating this problem in full atomistic detail and in a computationally efficient manner and enhances the predictive power of IFD methods. We significantly increase the accuracy of the underlying IFD protocol across a large data set comprising 42 different ligand-receptor systems. We expect this approach to be of significant value in computationally driven drug design.
配体对接是一种广泛应用于先导化合物发现和基于结合模式预测的药物发现的工具。对接过程中最大的挑战在于当受体在小分子结合时发生显著的重排，因此需要采用诱导契合对接（IFD）方法，即允许受体移动以便与配体实现最佳结合。IFD方法虽取得了一些成功，但存在可靠性不足的问题。原则上，用全原子分子动力学（MD）来补充IFD是一种直接的解决方案，但由于MD存在严重的时间尺度限制，在实际操作中并非如此。在此，我们引入一种元动力学加IFD的策略，以实用的计算成本准确可靠地预测蛋白质-配体复合物的结构。我们的策略能够以全原子细节且计算高效的方式处理这个问题，并增强IFD方法的预测能力。在包含42个不同配体-受体系统的大数据集上，我们显著提高了基础IFD协议的准确性。我们预计这种方法在计算驱动的药物设计中将具有重要价值。
