Yao Yufen, Liu Runduo, Li Wenchao, Huang Wanyi, Lai Yijun, Luo Hai-Bin, Li Zhe
State Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, China.
Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou 570228, China.
J Chem Theory Comput. 2024 Sep 5. doi: 10.1021/acs.jctc.4c00939.
The free energy perturbation (FEP) method is a powerful technique for accurate binding free energy calculations, which is crucial for identifying potent ligands with a high affinity in drug discovery. However, the widespread application of FEP is limited by the high computational cost required to achieve equilibrium sampling and the challenges in obtaining converged predictions. In this study, we present the convergence-adaptive roundtrip (CAR) method, which is an enhanced adaptive sampling approach, to address the key challenges in FEP calculations, including the precision-efficiency tradeoff, sampling efficiency, and convergence assessment. By employing on-the-fly convergence analysis to automatically adjust simulation times, enabling efficient traversal of the important phase space through rapid propagation of conformations between different states and eliminating the need for multiple parallel simulations, the CAR method increases convergence and minimizes computational overhead while maintaining calculation accuracy. The performance of the CAR method was evaluated through relative binding free energy (RBFE) calculations on benchmarks comprising four diverse protein-ligand systems. The results demonstrated a significant speedup of over 8-fold compared to conventional FEP methods while maintaining high accuracy. The overall values of 0.65 and 0.56 were obtained using the combined-structure FEP approach and the single-step FEP approach, respectively, in conjunction with the CAR method. In-depth case studies further highlighted the superior performance of the CAR method in terms of convergence acceleration, improved predicted correlations, and reduced computational costs. The advancement of the CAR method makes it a highly effective approach, enhancing the applicability of FEP in drug discovery.
自由能微扰（FEP）方法是一种用于精确计算结合自由能的强大技术，这对于在药物发现中识别具有高亲和力的强效配体至关重要。然而，FEP的广泛应用受到实现平衡采样所需的高计算成本以及获得收敛预测方面的挑战的限制。在本研究中，我们提出了收敛自适应往返（CAR）方法，这是一种增强的自适应采样方法，以解决FEP计算中的关键挑战，包括精度-效率权衡、采样效率和收敛评估。通过采用实时收敛分析来自动调整模拟时间，通过不同状态之间构象的快速传播实现对重要相空间的高效遍历，并且无需进行多个并行模拟，CAR方法提高了收敛性并在保持计算精度的同时将计算开销降至最低。通过对包含四个不同蛋白质-配体系统的基准进行相对结合自由能（RBFE）计算，评估了CAR方法的性能。结果表明，与传统FEP方法相比，速度显著加快了8倍以上，同时保持了高精度。结合CAR方法，采用组合结构FEP方法和单步FEP方法分别获得的总体值为0.65和0.56。深入的案例研究进一步突出了CAR方法在加速收敛、改善预测相关性和降低计算成本方面的卓越性能。CAR方法的进步使其成为一种高效的方法，增强了FEP在药物发现中的适用性。
