阿尔法Fold2结构指导潜在的连接体发现
Jiankun Lyu1,2, Nicholas Kapolka3, Ryan Gumpper3
1Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA.
概括
AlphaFold2模型通过准确预测配体结合部位,显示出药物发现的前景. 这项研究表明它们在潜在的对抗关键受体的新药候选者的有效性.
科学领域:
- 计算生物学
- 结构生物学
- 药物发现
背景情况:
- 在回顾性联体识别研究中,AlphaFold2 (AF2) 模型具有广泛的实用性,但成功率可变.
- 基于结构的药物设计依赖于精确的蛋白质标结构以实现有效的连接.
研究的目的:
- 预期评估未精炼的AlphaFold2模型对σ2和2A (5-HT2A) 受体的联体识别的有用性.
- 将AF2模型的性能与大型复合库对接中的实验结构进行比较.
主要方法:
- 大型化合物库与σ2和5-HT2A受体的未精炼AF2模型的潜在分子对接.
- 与AF2模型相比,通过对接获得的命中率和连体亲属性与实验结构的比较.
- 通过AF2对接确定一个强大的5-HT2A连接体的冷电子显微镜结构.
主要成果:
- 使用实验和AF2衍生结构实现了高和可比的命中率和亲和度.
- 与实验结构相比,即使有不同的正体残留形状,也可以成功对接AF2模型.
- 对顶部配体的冷电磁分析显示了与AF2预测一致的残留住宿.
结论:
- 不精细的AlphaFold2模型可以准确地预测连体结合,即使与实验结构的构造差异不同.
- AF2模型代表了低能量,适合基于结构的药物设计和配体发现的相关构造.
- 这些发现扩大了AF2模型在药物设计中的适用性,补充了传统的基于结构的方法.
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