FuncPhos-STR:基于AlphaFold蛋白质结构和动态的功能酸盐预测的集成深度神经网络
Guangyu Zhang1, Cai Zhang1, Mingyue Cai2
1School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
International journal of biological macromolecules
|March 29, 2024
概括
FuncPhos-STR是一种新的深度学习工具,通过整合结构,进化和相互作用数据来预测人类酸盐的功能. 本资源有助于理解蛋白质调节,并识别新的治疗点.
科学领域:
- 生物化学 生物化学
- 生物信息学是一种生物信息学.
- 结构生物学 结构生物学
背景情况:
- 酸化对生物调节至关重要,但对大多数已识别的酸盐赋予功能是具有挑战性的.
- 了解酸盐的功能对于破译细胞机制和疾病途径至关重要.
研究的目的:
- 开发FuncPhos-STR,这是一个深度学习框架,用于预测人类酸盐的功能和可视化它们的结构上下文.
- 为研究蛋白质酸化的研究人员创建一个可访问的在线资源.
主要方法:
- 来自AlphaFold蛋白质结构的综合结构和动态数据与序列演变和蛋白质-蛋白质相互作用 (PPI) 信息.
- 在FuncPhos-SEQ框架的基础上开发了一个深度学习模型 (FuncPhos-STR).
- 使用AUC评估模型性能,达到0.855.5的最佳值.
主要成果:
- FuncPhos-STR有效地整合了多维特征 (结构,动态,进化,PPI) 以提高酸盐功能的预测.
- 结构特征,特别是口袋区域内的矿,与更高的功能评分相关,表明它们具有监管作用.
- 与现有方法相比,该模型显示出更高的性能.
结论:
- FuncPhos-STR加速了未表征的酸盐的功能注释.
- 该框架有助于阐明由酸盐介导的全性调节机制.
- 在线资源为科学界提供了一种有价值的工具,以探索酸盐的功能.
相关概念视频
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01:02Phosphorylation
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