使用AlphaFold和实验结构来预测GPCR复合体的结构和结合亲缘关系,通过诱导适合对接和自由能量扰动
Dilek Coskun1, Muyun Lihan1, João P G L M Rodrigues1
1Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States.
Journal of chemical theory and computation
|December 15, 2023
概括
这项研究表明,通过诱导适合对接来改进的AlphaFold模型可以可靠地用于自由能量扰动 (FEP) 计算. 这种方法使得准确的前性药物设计,当实验结构是不可用的.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 自由能量扰动 (FEP) 对于评估潜在的药物化合物至关重要.
- 在未来应用之前,需要对FEP模型进行追溯验证.
- 作为FEP的起点,AlphaFold模型的实用性尚未得到充分探索,特别是对于缺乏实验结构的目标.
研究的目的:
- 评估使用AlphaFold模型的可行性,通过诱导适合对接来改进,用于自由能量扰动 (FEP) 计算.
- 在没有实验结构数据的情况下,验证药物发现的计算工作流.
- 为了探索FEP模型的性能,使用不同数量的连接体数据.
主要方法:
- 用分子动力学 (IFD-MD) 诱导的适合对接被用来用接体来改进AlphaFold模型.
- IFD-MD方法在实验性G蛋白结合受体 (GPCR) 结构的数据集上得到了验证.
- 在精制的AlphaFold模型上进行了FEP计算,用于体静止素受体家族 (SSTRs).
主要成果:
- 在实验性GPCR的姿势预测中,IFD-MD实现了高准确度 (95%的成功率).
- 对SSTR2,SSTR4和SSTR5精制的AlphaFold模型的FEP计算产生了大约1kcal/mol的根平均平方误差 (RMSE).
- 该研究确定了模型验证的挑战,使用有限或分类的配体数据.
结论:
- 当AlphaFold模型与IFD-MD完善后,为FEP计算提供了一个可行的起点.
- 这种综合方法为缺乏实验结构的目标提供了前性药物设计的便利.
- 这些发现强调了数据可用性对于FEP模型验证的重要性.
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