通过自动路径搜索微调G蛋白合受体的激活特异性
概括
我们开发了一个高效的模拟协议来预测分子相互作用,并设计选择性药物激动剂. 这种方法加速了对基-1-酸盐受体 (S1PRs) 新型化合物的发现.
科学领域:
- 计算化学是一种计算化学.
- 分子动力学分子动力学
- 药物发现 药物发现
背景情况:
- 基于物理学的模拟提供了对生物分子功能的原子洞察力,但受到低效率的限制,特别是在设计选择性激动剂时.
- 模拟众多的蛋白质 - 配体组合是计算密集且耗时的.
研究的目的:
- 开发一种自动化,无输入的协议,用于在复杂的分子系统中高效地搜索最小自由能量路径 (MFEP).
- 将该协议应用于由各种配体激活基-1-酸盐受体 (S1PRs),并指导选择性激动剂的设计.
主要方法:
- 开发了一个自动化,无输入的协议,以识别高维配置空间中的最小自由能量路径 (MFEP).
- 图形处理单元 (GPU) 服务器被用于高效的计算,在14天内完成计算.
- 该协议被应用于模拟任意连接体激活氨酸-1-酸盐受体 (S1PRs).
主要成果:
- 模拟协议在MFEP和实验生物发光共振能量转移 (BRET) 测量G蛋白解离的预测自由能量分布之间取得了显著的一致性.
- 关键残留物 (S1PR3中的F263 / I284) 被确定为决定激动剂偏好的关键,残留物交换实验验证实了它们的作用.
- 该协议指导了新型S1PR1选择性激动剂的in silico设计,这些激动剂经过实验证实可以在纳米分子度下激活S1PR1,但对S1PR3/5.5的活性显著降低.
结论:
- 开发的模拟协议显著提高了分子模拟的效率,以了解生物分子功能和药物设计.
- 这种方法可以快速设计和优化G蛋白结合受体 (GPCRs) 的选择性激动剂,如S1PRs.
- 这些发现突显了计算方法在加速发现具有提高选择性和有效性的向治疗方法方面的潜力.
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