深度学习模型的翻译后修改调节液体-液体相分离液体
Xiaokun Hong1, Jiyang Lv2, Zhengxin Li2
1College of Biological Science and Engineering, Fuzhou University, Fuzhou, Fujian, China.
Communications chemistry
|December 8, 2025
概括
研究人员创建了一个数据库PTMPhaSe和一个深度学习预测器PhosLLPS,以了解蛋白质翻译后修饰 (PTMs) 如何调节液态-液态相分离 (LLPS). 这有助于研究生物过程和疾病中的LLPS.
科学领域:
- 生物化学和分子生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 液体-液体相分离 (LLPS) 对于形成无膜有机体至关重要,影响生物功能和疾病致病.
- 蛋白质后翻译修饰 (PTMs) 显著调节LLPS,但专门的资源和预测工具很少.
研究的目的:
- 为监管LLPS的PTM建立一个全面的数据库 (PTMPhaSe) 和一个预测模型 (PhosLLPS).
- 加强对PTM在LLPS动态中的作用的理解,并促进针对LLPS相关疾病的药物发现.
主要方法:
- 构建PTMPhaSe数据库,使用手工策划的PTM和LLPS实验证据.
- 基于图形神经网络的深度学习模型 (PhosLLPS) 的开发,用于预测LLPS所涉及的功能酸化部位.
- 应用PhosLLPS在人类蛋白质组中进行大规模预测.
主要成果:
- PTMPhaSe数据库提供了LLPS中PTM监管的精心策划的存储库.
- 与基线模型和现有方法相比,PhosLLPS在识别功能酸化位点方面表现优异 (AUC=0.9116).
- 对于整个人类蛋白质组,产生了PhosLLPS预测,提供了对调控酸化位点的见解.
结论:
- 开发的数据库和预测器弥合了PTM监管和LLPS之间的差距.
- 这些资源对于推进LLPS分子机制的研究和支持与异常LLPS相关的疾病治疗方法的开发有价值.
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