利用神经网络来纠正FoldX的自由能量估计
Jonathan E Barnes1, L América Chi2, F Marty Ytreberg3,1
1Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, USA.
bioRxiv : the preprint server for biology
|October 10, 2024
概括
这项研究通过在FoldX计算输出上训练神经网络来增强蛋白质突变效应预测. 改进的模型显著提高了预测蛋白质折叠和结合亲缘关系的准确性,有助于药物设计和疾病研究.
科学领域:
- 计算生物学和生物物理学
- 蛋白质的工程和设计.
- 生物信息学和机器学习
背景情况:
- 蛋白质突变可以通过改变折叠或生物分子相互作用来引起功能障碍和疾病.
- 准确预测突变效应对于药物设计和了解疾病机制至关重要.
- 量化突变效应的实验方法是准确的,但耗时和昂贵;计算方法更快,但不那么准确.
研究的目的:
- 使用神经网络提高蛋白质突变效应的计算预测的准确性.
- 改善对蛋白质折叠稳定性和结合亲和力的预测,特别是对于抗体-抗原系统.
- 开发一种具有成本效益和快速的方法来评估蛋白质突变的影响.
主要方法:
- 在大型数据集 (SKEMPIv2用于绑定,ProTherm4用于折叠) 上使用实验数据进行了FoldX计算.
- 从FoldX输出中提取特征,包括预测的自由能量变化.
- 开发并优化了一个神经网络,以预测FoldX估计和实验数据之间的差异,生成一个校正因子.
主要成果:
- 皮尔森相关性对突变效应预测的显著改善.
- 影响折叠的单个突变的相关性从0.3提高到0.66.
- 结合性亲和力预测相关性从0.37增加到0.61 (单一突变),从0.52增加到0.81 (双重突变);表观相关性从0.19提高到0.59.
结论:
- 神经网络对FoldX输出进行校正,可以在最小的计算开销下大幅提高预测准确度.
- 在双重突变上训练的模型对更高阶突变表现更好,这表明在FoldX.X中互动能量和表现效应未得到充分利用.
- 这种方法为自由能量预测管道提供了宝贵的增强,并有可能预测抗体逃生突变.
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