结合结构和共同进化的信息,揭示了全位.
Giuseppina La Sala1, Christopher Pfleger2, Helena Käck3
1Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca Gothenburg Sweden giuseppina.lasala@astrazeneca.com andrey.frolov@astrazeneca.com.
Chemical science
|June 30, 2023
概括
预测蛋白质中的全位对药物发现至关重要. 一个新的计算模型整合了多种数据类型,以准确识别隐藏的全囊,帮助制药研究.
科学领域:
- 生物化学和结构生物学
- 计算机化药物发现技术
- 药理学 药理学是指药理学的学科.
背景情况:
- 体调节在生物分子中至关重要,使得体位预测成为制药研究中的一个关键挑战.
- 计算方法已经在表征全结合方面取得了进展,但识别全位仍然很困难.
研究的目的:
- 开发和验证一种基于结构的新型计算模型,用于识别蛋白质中潜在隐藏的全位.
- 通过提供可靠的方法来精确确定可用药物的全囊,以加强药物发现工作.
主要方法:
- 整合了局部结合部位信息,共进化的数据和动态菌信息.
- 基于结构的三参数模型的开发,用于预测全位.
- 模型对五种已知的全性蛋白质的验证:LFA-1,p38-α,GR,MAT2A和BCKDK.
主要成果:
- 该模型成功地将所有已知的全囊在测试蛋白质的前三名中排名.
- 在MAT2A中发现了一个新的可用药物的部位,并通过X射线结晶学和表面等离子体共振 (SPR) 确认了这一点.
- 通过生物化学分析和X射线结晶学验证了BCKDK中以前未知的可药性全位.
结论:
- 开发的三参数模型有效地识别了蛋白质结构中隐藏的全位.
- 这种计算方法具有显著的潜力,可以通过定位新的可用药物的全囊来加速药物发现.
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