ProtTeX:使用大型语言模型对蛋白质进行结构上下文推理和编辑
Zicheng Ma1,2, Chuanliu Fan3, Zhicong Wang3
1Changping Laboratory, Beijing 102200, China.
Journal of chemical information and modeling
|June 25, 2025
概括
ProtTeX 将蛋白质序列,结构和文本集成到一个统一的代币空间中. 这使得大型语言模型 (LLM) 能够进行结构感知蛋白质分析和生成,大大提高了预测准确性.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质科学中的人工智能
背景情况:
- 大型语言模型 (LLM) 在分子科学中脱而出,主要是使用基于序列的小分子标记化.
- 蛋白质科学的挑战往往是结构依赖的,但目前的LLM缺乏结构意识的标记化,限制了他们的能力.
- 由于缺乏结构信息,现有的LLM在全面的生物分子理解和多模式生成方面扎.
研究的目的:
- 介绍ProtTeX,一种用于将蛋白质序列,结构和文本标记化为统一离散空间的新框架.
- 通过Next-Token预测实现多模式蛋白质推理和生成的联合LLM培训.
- 授权一般的LLM处理蛋白质结构,使用它们进行推理,并通过文本生成/操纵结构.
主要方法:
- 开发了ProtTeX以令牌化蛋白质序列,结构和相关的文本信息.
- 实现了一个统一的离散空间,用于多模式蛋白质数据表示.
- 利用下一个令牌预测模式进行联合LLM培训,使结构意识处理成为可能.
主要成果:
- ProtTeX显著提高了蛋白质功能预测的准确性,比最先进的领域专家模型提高了两倍.
- 证明了高质量的蛋白质结构生成和可定制的蛋白质设计能力.
- 展示了仅使用解码器的LLM,使用标准管道,以解决与ProtTeX有关的各种蛋白质相关任务的能力.
结论:
- ProtTeX弥合了LLM和结构依赖的蛋白质科学挑战之间的差距.
- 该框架使用标准的LLM架构促进了先进的多模式蛋白质推理和生成.
- 在将LLM应用到复杂的蛋白质设计和分析问题上,ProtTeX代表了重大进步.
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