Alchemical Transfer Method（炼金术转移方法，ATM）用于多样化配体结合自由能预测的性能与分析
Performance and Analysis of the Alchemical Transfer Method for Binding Free Energy Predictions of Diverse Ligands
AI总结：
本文验证 ATM 在多样化蛋白质-配体复合物 RBFE 预测中的表现，基于 AToM-OpenMM 完成 8 个靶点、500 余次计算，证明其准确度可比肩现有最先进方法且适用范围更广。
AI中文摘要：
本文针对一系列多样化的蛋白质-配体复合物的相对结合自由能，对 Alchemical Transfer Method（炼金术转移方法，ATM）进行了验证。我们采用简化的设置工作流程、定制力场以及 AToM-OpenMM 软件，计算了由 Merck KGaA 的 Schindling 及其合作者制备的基准集的相对结合自由能（RBFE）。该基准集既包括标准的小型 R 基团配体修饰示例，也包括更具挑战性的情形，例如大型 R 基团变化、骨架跃迁、形式电荷变化以及电荷转移转化。ATM 的新型坐标微扰方案和双拓扑方法解决了单拓扑炼金术相对结合自由能方法面临的一些挑战。具体而言，ATM 无需拆分静电相互作用和 Lennard-Jones 相互作用、无需原子映射、无需定义配体区域，也无需对电荷变化微扰进行事后校正。因此，ATM 比传统炼金术方法更简单且适用范围更广，尤其适用于骨架跃迁和电荷变化转化。在本研究中，我们针对 8 个蛋白质靶点进行了远超 500 次的相对结合自由能计算，发现 ATM 达到了与现有最先进方法相当的准确度，尽管其统计波动更大。我们讨论了关于 ATM 方法具体优势与弱点的洞见，以为未来的部署提供参考。本研究证实，在统一的开源框架内，ATM 可作为生产工具，适用于广泛微扰类型的相对结合自由能（RBFE）预测。
英文摘要：
The Alchemical Transfer Method (ATM) is herein validated against the relative binding free energies of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and the AToM-OpenMM software to compute the relative binding free energies (RBFE) 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 relative binding free energy methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and post-corrections 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 relative binding free energy calculations for eight protein targets and found that ATM achieves accuracy comparable to existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM is applicable as a production tool for relative binding free energy (RBFE) predictions across a wide range of perturbation types within a unified, open-source framework.
