波恩折叠:预测影响蛋白质折叠率的替代物
Yang Yang1,2, Zhang Chong1, Mauno Vihinen3
1School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
International journal of molecular sciences
|August 26, 2023
概括
我们开发了PON-Fold,这是一种机器学习工具,用于预测氨基酸变化如何影响二态折叠蛋白质中的蛋白质折叠率. PON-Fold的性能优于现有的方法,有助于蛋白质工程和药物设计.
科学领域:
- 蛋白质生物物理学 蛋白质生物物理学
- 计算生物学是一种计算生物学.
- 机器学习是机器学习.
背景情况:
- 蛋白质折叠对功能至关重要,其速度对氨基酸序列变化敏感.
- 预测突变对蛋白质折叠率的影响对于理解蛋白质稳定性和设计新型蛋白质至关重要.
研究的目的:
- 开发一种基于机器学习的新方法,用于预测氨基酸替代对双态折叠蛋白质折叠速率的影响.
- 创建一个可以准确评估突变对蛋白质折叠动力学影响的工具.
主要方法:
- 收集了一组实验确定蛋白质变体折叠率的数据集.
- 训练了一个梯度增强算法,使用1161个特征来开发两个预测器:一个三类分类器和一个回归预测器.
- 通过盲目测试评估预测器性能,并将结果与现有方法进行比较,将开发的方法命名为PON-Fold.
主要成果:
- 三类分类器在盲测试中实现了分别从0.324到0.419和0.256到0.451的特异性和灵敏性.
- 回归预测器显示皮尔森相关系数为0.525,平均绝对误差为0.581,平均平方误差为0.603.
- 与之前展示的工具相比,PON-Fold在所有测量指标上都表现出优异的表现.
- 预测对蛋白质构造敏感,对不同形式 (开放/关闭,apo/holo) 观察到显著影响.
结论:
- PON-Fold是一种新且有效的机器学习工具,用于预测二态蛋白中氨基酸替代的折叠率效应.
- 蛋白质结构结构的选择显著影响预测结果,突出了结构上下文的重要性.
- 与现有方法相比,PON-Fold提供了更高的性能,并且可以在研究和蛋白质工程中免费使用.
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