蛋白质序列和结构的双语语言模型
Michael Heinzinger1, Konstantin Weissenow1, Joaquin Gomez Sanchez1
1School of Computation, Information, and Technology (CIT), Department of Informatics, Bioinformatics & Computational Biology, TUM (Technical University of Munich), 85748 Garching/Munich, Germany.
NAR genomics and bioinformatics
|December 5, 2024
概括
我们开发了 ProstT5,一种新型的蛋白质语言模型 (pLM),集成了 3D 结构和 1D 序列数据. 这种方法增强了与结构相关的预测,并加速了蛋白质结构数据库的分析.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 在基因组学中的机器学习.
背景情况:
- 蛋白质语言模型 (pLMs) 已经推进了蛋白质序列的分析.
- AlphaFold2彻底改变了蛋白质结构预测,产生了大量的3D结构数据.
- 了解蛋白质需要整合它们的1D序列和3D结构信息.
研究的目的:
- 开发一个统一的模型,同时分析蛋白质序列和结构数据.
- 利用pLM来建模蛋白质作为序列和3D机器的双重性质.
- 创建一种新的方法,用于在3D结构表示和氨基酸序列之间进行翻译.
主要方法:
- 将蛋白质结构编码为符号序列,使用Foldseek.ek的3Di字母表.
- 在AlphaFoldDB的非冗余数据集上微调现有的pLM (ProtT5).
- 开发蛋白质"结构序列"T5 (ProstT5) 模型用于联合结构序列分析.
主要成果:
- ProstT5在与结构相关的预测任务上表现得更好.
- 在导出3D结构表示 (3Di) 中实现了三次数加速.
- 建立了将plm与大规模蛋白质结构数据集成的概念验证.
结论:
- ProstT5有效地模拟了蛋白质序列和结构,弥合了pLMs和结构生物信息学之间的差距.
- 这种方法对未来的应用至关重要,它可以在大型元基因组数据库中进行结构敏感的搜索.
- ProstT5通过将结构预测集成到基于序列的分析中,为后AlphaFold2时代开辟了新的研究途径.
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