G蛋白结合受体-连接体姿势和功能类预测
Gregory L Szwabowski1, Makenzie Griffing1, Elijah J Mugabe1
1Department of Chemistry, University of Memphis, Memphis, TN 38152, USA.
International journal of molecular sciences
|July 13, 2024
概括
预测与G蛋白结合受体 (GPCRs) 的药物相互作用对于药物发现至关重要. 这项研究发现,虽然连接体相互作用指纹提供了轻微的好处,但随机森林分类器准确地预测了GPCRs的连接体功能.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
- 生物化学 生化学
背景情况:
- G蛋白结合受体 (GPCR) 是重要的跨膜蛋白,也是常见的药物点.
- 虚拟查 (VS) 广泛用于针对GPCRs的药物发现计划.
- 提高预测GPCR分子结合和功能的准确性可以加速药物发现.
研究的目的:
- 评估连接体相互作用指纹的优势,而不是在对接时用于结合部位选择的自动化方法.
- 用随机森林分类器来确定连接体相互作用指纹是否可以预测候选分子的功能状态 (激动剂,抗剂,逆激动剂).
主要方法:
- 进行了经典的对接模拟.
- 对干相互作用指纹进行了评估,以评估它们在结合部位选择和姿势采样中的实用性.
- 一个随机的森林分类器被训练和测试,使用连接体-受体复杂数据来预测连接体功能.
- 分类器的性能被评估在一个外部测试集的GPR31和TAAR2连接体.
主要成果:
- 干相互作用指纹在采样准确姿势中提供了适度的优势,但在得分后在排名最高的姿势中没有实质性的好处.
- 随机森林分类器有效预测了连接体功能,将激动剂,对抗剂和逆激动剂分类为活性.
- 二元分类器在外部测试集上实现了预测活性集内的实际资产的82.6%的成功率.
结论:
- 干相互作用指纹对于生成高质量的干受体复合体并非必不可少,用于GPCR药物发现.
- 使用连接体相互作用指纹的随机森林分类是预测GPCR连接体功能状态的高效方法.
- 这种预测能力可以显著加速识别针对GPCR的新药候选药物.
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