评估用于相对结合自由能计算的静电嵌入MLIP/MM方法
Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations
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中文总结 AI 辅助
本研究将Semelak等人的静电嵌入MLIP/MM方案应用于蛋白质-配体RBFE计算，发现其可提升TYK2的RBFE精度，但在CDK2等4个靶点表现与基线相当，且单分子基准无法预测靶点依赖结果。
中文摘要 AI 辅助
炼金术相对结合自由能（RBFE）计算受限于经典力场的固定电荷近似。混合机器学习原子间势/分子力学（MLIP/MM）方案可修正配体应变，但在机械嵌入下仍用静态点电荷描述配体-环境静电作用。已有将机器学习电荷与MM环境耦合的静电嵌入方案，并在简单系统中与QM/MM对比验证，但未在生产级炼金术工作流中测试。本研究采用Semelak等人的静电嵌入方案，在蛋白质-配体RBFE任务中评估：在包含10^6个构象的AceFF数据集上训练TensorNet2模型“AceFF-2-RESP-1”，联合预测能量、力与约束静电势（RESP）电荷；选择RESP而非MBIS，以适配其耦合的AMBER家族力场；预测电荷进入粒子网格Ewald求和的短程实空间部分，采用Thole阻尼防止炼金术转化期间的极化灾难。在Wang等人基准集的5个靶点上测试该方案，每个边设3次重复并采用匹配协议。静电嵌入提升了TYK2的所有精度与相关性指标（ΔΔG RMSE从0.86降至0.45 kcal/mol，对比GAFF2），但与经典及机械嵌入基线在CDK2、凝血酶、p38、JNK1上表现相当；标准单分子能量与电荷基准无法预测该靶点依赖结果，TYK2兼具良好ΔΔG精度与Schrödinger基准最低力误差，但该模式未在其他靶点成立。
英文摘要
Alchemical relative binding free energy (RBFE) calculations are limited by the fixed-charge approximation of classical force fields. Hybrid machine learning interatomic potential/molecular mechanics (MLIP/MM) schemes correct ligand strain, but under mechanical embedding still describe ligand--environment electrostatics with static point charges. Electrostatic embedding schemes coupling machine-learned charges to the MM environment have been proposed and validated against QM/MM for simple systems, but not tested in a production alchemical workflow. We take the electrostatic embedding scheme of Semelak et al.\ and evaluate it on protein--ligand RBFE. We trained a TensorNet2 model, \texttt{AceFF-2-RESP-1}, on $10^{6}$ conformations from the AceFF dataset, jointly predicting energies, forces and Restrained Electrostatic Potential (RESP) charges. We chose RESP over MBIS for commensurability with the AMBER-family force field it couples to. The predicted charges enter the short-range direct-space part of the particle mesh Ewald sum, with Thole damping to prevent polarization catastrophes during alchemical transformations. We tested the scheme across five targets from the Wang et al.\ benchmark set, fixed in advance by a prior study, with three replicates per edge and matched protocols. Electrostatic embedding improved every accuracy and correlation metric for TYK2 ($ΔΔG$ RMSE $0.86 \rightarrow 0.45$~kcal/mol against GAFF2), but performed comparably to the classical and mechanical-embedding baselines for CDK2, thrombin, p38 and JNK1. Standard single-molecule energy and charge benchmarks were not good predictors of this target-dependent outcome. TYK2 combined good $ΔΔG$ accuracy with the lowest force error on the Schrödinger benchmark, but this pattern did not hold for the other targets.
