Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands
Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands
复制标题
DOI：
10.1021/acs.jcim.3c01705
复制
发表时间：
2023-12-26
影响因子：
5.6
通讯作者：
Gallicchio，Emilio
中科院分区：
文献类型：
--
作者：
Chen，Lieyang;Wu，Yujie;Gallicchio，Emilio
炼金术转移方法(ATM)在此对照一组不同的蛋白质-配体复合体的相对自由结合能(RBFE)进行验证。我们使用简化的设置工作流程、定制的力场和Atom-OpenMM软件来计算由迅达和默克KGaA的合作者准备的基准集的RBFE。该基准集包括标准小R基团配体修饰的例子以及更具挑战性的场景，例如大R基团改变、支架跳跃、形式电荷改变和电荷移位转换。新的坐标摄动方案和ATM的双拓扑方法解决了单拓扑炼金术RBFE方法的一些挑战。具体地说，ATM消除了分裂静电和Lennard-Jones相互作用、原子映射、定义配位体区域和电荷变化扰动的后校正的需要。因此，ATM比传统的炼金术方法更简单，适用范围更广，特别是对于支架跳跃和电荷改变变换。在这里，我们对8个蛋白质目标进行了超过500个RBFE计算，发现ATM达到了与现有最先进方法相当的精度，尽管有更大的统计波动。我们将讨论对ATM方法的具体优点和缺点的见解，这些优点和缺点将为未来的部署提供参考。这项研究证实，ATM可以作为一种生产工具，在统一的、开放源代码的框架内，对各种扰动类型进行RBFE预测。
The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein–ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.