Rutgers, the State University of New Jersey, Laboratory for Biomolecular Simulation Research, and Department of Chemistry and Chemical Biology, New Brunswick, New Jersey 08901-8554, United States.
Silicon Therapeutics, Boston, Massachusetts 02210, United States.
J Chem Inf Model. 2020 Nov 23;60(11):5595-5623. doi: 10.1021/acs.jcim.0c00613. Epub 2020 Sep 16.
Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.
预测蛋白质-配体结合亲和力和生物分子识别的相关热力学是基于结构的药物设计的主要目标。 热力学模拟提供了一种高度准确和计算效率高的方法来实现这一目标。 虽然 AMBER 分子动力学包在学术研究小组中成功地用于热力学模拟已有数十年的历史,但由于 AMBER 热力学代码以前的限制,以及系统设置和后处理工作流程方面的挑战,在工业药物发现环境中的广泛影响微乎其微。 通过密切的学术界-工业合作,我们解决了以前的许多限制,旨在提高工业药物发现应用中热力学模拟的准确性、效率和稳健性。 在这里,我们重点介绍 AMBER20 的一些最新进展,重点介绍热力学模拟,它比探索完整结合/解吸路径的替代热力学模拟方法计算量更小。 除了 AMBER20 中的科学和技术进步,我们还描述了运行相对热力学模拟的基本实践方面,以及最佳实践建议,强调不仅要重视热力学模拟代码,还要重视获得准确可靠结果所需的辅助功能和专业知识。 这项工作旨在提供一个有关在 AMBER20 中运行相对热力学模拟的科学、技术和实践问题的现代概述,重点是实际的药物发现应用。