Convergence-Adaptive Roundtrip Method Enables Rapid and Accurate FEP Calculations,Journal of Chemical Theory and Computation

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Convergence-Adaptive Roundtrip Method Enables Rapid and Accurate FEP Calculations
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2024-09-05 , DOI: 10.1021/acs.jctc.4c00939
Yufen Yao ₁ , Runduo Liu ₁ , Wenchao Li ₁ , Wanyi Huang ₁ , Yijun Lai ₁ , Hai-Bin Luo _{2,

3} , Zhe Li ₁

Affiliation

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 R² 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.

中文翻译：

收敛自适应往返方法可实现快速准确的 FEP 计算

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有效配体至关重要。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，旨在解决 FEP 计算中的关键挑战，包括精度与效率的权衡、采样效率和收敛评估。通过采用动态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重要相空间的有效遍历，并消除多个并行模拟的需要，CAR方法提高了收敛性并最大限度地减少了计算开销保持计算精度。 CAR 方法的性能是通过对包含四种不同蛋白质-配体系统的基准进行相对结合自由能 (RBFE) 计算来评估的。结果表明，与传统 FEP 方法相比，速度显着提高 8 倍以上，同时保持高精度。使用组合结构FEP方法和单步FEP方法结合CAR方法分别获得0.65和0.56的总体R ²值。深入的案例研究进一步凸显了 CAR 方法在收敛加速、改进预测相关性和降低计算成本方面的优越性能。 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。

更新日期：2024-09-05

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