通过AlphaFold2-RAVE授权AlphaFold2进行蛋白质构成选择性药物发现
Xinyu Gu1,2, Akashnathan Aranganathan1,3, Pratyush Tiwary1,4,2
1Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA.
ArXiv
|April 25, 2024
概括
这项研究介绍了AF2RAVE-Glide,这是药物设计的新框架. 它准确地预测了小分子与转移稳定的蛋白质结构的结合,改善了药物发现的虚拟查.
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:
- 小分子药物设计需要共结晶的配体-蛋白质结构.
- AlphaFold2在预测apoprotein结构方面表现出色,但忽略了连接体和全结构.
- 针对转移稳定的蛋白质构造对于选择性药物设计至关重要,但这些很难预测.
研究的目的:
- 开发一个计算框架,用于预测小分子与转移稳定的蛋白质构成的结合.
- 为了使基于结构的药物设计使用序列启动模型.
- 评估框架在虚拟查和药物发现方面的有效性.
主要方法:
- 集成AlphaFold2 (AF2) 与全原子增强采样分子动力学 (RAVE) 和诱导合适对接 (Glide).
- 开发AF2RAVE-Glide工作流程,用于建模小分子结合.
- 对三种蛋白激酶及其I型和II型抑制剂的应用,重点关注DFG-out状态.
主要成果:
- AF2RAVE-Glide成功地取样了包括DFG-out状态在内的转移稳定的激酶构造.
- 该框架使已知II型激酶抑制剂的对接成为可能,成功率超过50%.
- 在预测小分子与具有挑战性的蛋白质标的结合方面表现出高准确性.
结论:
- 该AF2RAVE-Glide协议是有效的计算建模的小分子结合转移稳定的蛋白质构造.
- 这种方法增强了虚拟查,并促进了基于结构的药物发现.
- 该协议显示了在各种激酶和蛋白质中更广泛应用的前景.
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