基于蛋白质语言模型的液态相分离蛋白质的预测
Wenbin Li1, Xusheng Deng1, Chunlin Xiang1
1State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Engineering Research Centre of Molecular Diagnostics of the Ministry of Education, National Institute for Data Science in Health and Medicine Engineering, School of Life Sciences, Faculty of Medicine and Life Sciences, Shenzhen Research Institute of Xiamen University, Xiamen University, No. 4221, Xiang'an South Road, Xiamen, Fujian 361102, China.
Briefings in bioinformatics
|December 17, 2025
概括
我们开发了一种新的计算方法,有效地识别参与液态相分离 (LLPS) 的蛋白质,这对于细胞功能和疾病研究至关重要. 这个工具有助于理解生物分子凝聚物及其在健康中的作用.
科学领域:
- 生物化学 生物化学
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 液-液相分离 (LLPS) 形成了细胞过程中必不可少的无膜冷凝物.
- 识别驱动LLPS的蛋白质是了解细胞组织和疾病发病的关键.
- 由于复杂的特征提取,现有的预测方法面临挑战.
研究的目的:
- 开发一种新的,高效的计算框架,用于识别经历LLPS的蛋白质.
- 在LLPS蛋白质预测中克服当前特征提取方法的局限性.
主要方法:
- 集成ProtT5蛋白语言模型用于序列表示.
- 整合了一个KmerConv模块用于本地序列模式分析.
- 应用多头注意力机制用于全球序列信息提取.
主要成果:
- 这种新的框架在各种数据集中展示了高预测性能.
- 该方法在不同物种中显示出有效的概括能力.
- 实现了对LLPS相关蛋白质的强大和高效的系统识别.
结论:
- 拟议的计算框架为LLPS蛋白质识别提供了强大的和高效的解决方案.
- 这一进步促进了对生物分子聚合及其疾病影响的更深入研究.
- 该工具支持对LLPS蛋白在细胞组织中的功能进行系统分析.
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