3DDPDs:描述蛋白质动态,用于蛋白质化学测量生物活性预测. 对于 (突变的) G 蛋白结合受体的一个例子
Marina Gorostiola González1,2, Remco L van den Broek1, Thomas G M Braun1
1Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
新的3D动态蛋白质描述器 (3DDPDs) 捕获蛋白质运动,以改善药物发现模型. 这些动态特征增强了生物活性预测,在关键任务中表现优于传统的静态方法.
科学领域:
- 计算化学和化学信息学
- 结构生物学和生物物理学
- 药理学和药物发现
背景情况:
- 蛋白化学计 (PCM) 建模使用化学和蛋白质特征来预测药物的生物活性.
- 当前的蛋白质描述器往往忽略了蛋白质动态,这对于功能和连接体相互作用至关重要.
- G蛋白结合受体 (GPCRs) 是关键的药物标,其功能是由构造变化调节的.
研究的目的:
- 开发新的3D动态蛋白质描述器 (3DDPDs),将蛋白质动态纳入PCM模型.
- 通过使用GPCR分子动力学数据,评估3DDPD与传统静态描述器的性能.
- 探索3DDPDs在捕捉突变蛋白的动态波动中的实用性.
主要方法:
- 根据分子动力学 (MD) 轨迹设计的残留特异性 (rs) 和蛋白质特异性 (ps) 3DDPD.
- 计算了坐标和部分电荷的分布,然后进行了维度缩小.
- 与PCM回归和分类任务中的最先进的非动态描述符对比的基准3DDPD.
主要成果:
- 在时间分裂回归任务中,3DDPD显著超过非动态描述符.
- 在随机分割和分类任务中观察到类似的性能.
- 将3DDPD与非动态描述符相结合并没有改善预测性能.
- 3DDPDs有效地捕获了突变GPCRs中的动态波动.
结论:
- 通过3DDPDs将蛋白质动态纳入,可以增强用于生物活性预测的机器学习.
- 3DDPDs为计算药物发现提供了一个有希望的方法,特别是对于像GPCRs这样的动态目标.
- 这种方法为药物设计开辟了新的途径,包括瘤学.
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