平衡和不平衡组合方法用于准确,精确和可重复的绝对约束的自由能量计算
Agastya P Bhati1, Shunzhou Wan1, Peter V Coveney1,2,3
1Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom.
Journal of chemical theory and computation
|December 16, 2024
概括
综合化学绝对结合自由能量 (ABFE) 计算为蛋白质-连接体复合体提供了准确的预测. 在大规模的药物发现中,由于其速度,成本效益和简单性,建议使用平衡方法.
科学领域:
- 计算化学是一种计算化学.
- 分子建模分子建模
- 药物发现 药物发现
背景情况:
- 蛋白质-连接体复合体的自由能量计算在药物发现中至关重要.
- 集合方法提高了预测的准确性,精度和可重复性.
- 使用炼化方法进行绝对结合的自由能量 (ABFE) 预测一直是常规应用的挑战.
研究的目的:
- 将整体化学 ABFE 方法应用于 219 个配体-蛋白质复合体的大数据集.
- 为了比较平衡和不平衡方法的大规模ABFE预测的性能.
- 提供对这些方法的批判性评估,并推一个最佳的协议.
主要方法:
- 合并化学绝对结合自由能量 (ABFE) 的计算.
- 应用到219个蛋白质连接体复合物的数据集.
- 平衡和不平衡的化学方法的比较.
主要成果:
- 获得了具有高准确度 (<1 kcal/mol) 的统计学上稳健的 ABFE 预测.
- 与非平衡方法相比,平衡方法显示出更高的准确性,精度,速度和成本效益.
- 发现自由能量分布是不正常的,这对分析有影响.
结论:
- 整体化学ABFE方法,特别是平衡方法,适用于大规模的药物发现应用.
- 建议为最佳的ABFE计算制定一个最终的协议.
- 超级计算可以在几个小时内使用拟议的协议进行数千次ABFE计算.
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