Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling.
Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling.
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DOI：
10.1038/s41467-023-44208-9
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发表时间：
2023-12-21
影响因子：
16.6
通讯作者：
Vilseck, Jonah Z.
中科院分区：
文献类型：
--
作者：
Robo, Michael T.;Hayes, Ryan L.;Ding, Xinqiang;Pulawski, Brian;Vilseck, Jonah Z.
相对结合能量计算已成为基于结构的药物设计中铅优化的整体计算工具，包括自由能扰动或热力学整合，通过将一个分子转换为另一种分子来计算相对的自由能差。由于需要独立执行许多成对扰动而导致的高运营成本。工作流程，我们提出了一种称为“偏置的gibbs”采样的方法，该方法使用动态偏见在多个配体类似物之间连续采样。对于五个基准系统的折衷精度，实验的同意是很高的1.0 kcal mol-1。观察到整个芳族环取代的100-200倍。基于结构的药物设计要加速。
相对结合能的计算对于基于结构的药物设计中的铅优化至关重要，但是经典方法在这里计算昂贵。
Relative binding free energy calculations have become an integral computational tool for lead optimization in structure-based drug design. Classical alchemical methods, including free energy perturbation or thermodynamic integration, compute relative free energy differences by transforming one molecule into another. However, these methods have high operational costs due to the need to perform many pairwise perturbations independently. To reduce costs and accelerate molecular design workflows, we present a method called λ-dynamics with bias-updated Gibbs sampling. This method uses dynamic biases to continuously sample between multiple ligand analogues collectively within a single simulation. We show that many relative binding free energies can be determined quickly with this approach without compromising accuracy. For five benchmark systems, agreement to experiment is high, with root mean square errors near or below 1.0 kcal mol−1. Free energy results are consistent with other computational approaches and within statistical noise of both methods (0.4 kcal mol−1 or less). Notably, large efficiency gains over thermodynamic integration of 18–66-fold for small perturbations and 100–200-fold for whole aromatic ring substitutions are observed. The rapid determination of relative binding free energies will enable larger chemical spaces to be more readily explored and structure-based drug design to be accelerated. Calculations of relative binding free energy are crucial for lead optimization in structure-based drug design, but classical methods are computationally expensive. Here, the authors describe a more efficient method for calculating the free energy that is as accurate as thermodynamic integration.
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