使用全球生成模型探索蛋白质序列空间
Sergio Romero-Romero1, Sebastian Lindner2, Noelia Ferruz3
1Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany.
Cold Spring Harbor perspectives in biology
|October 17, 2023
概括
大型语言模型正在通过生成新型人工蛋白来彻底改变蛋白质设计. 本综述涵盖了生成模型,包括变压器和非变压器架构,以及它们在定向进化中的应用.
科学领域:
- 生物技术和生物信息学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 图像和语言处理的大规模架构已经推进了计算机视觉和自然语言处理 (NLP).
- 像ChatGPT和GPT-4这样的语言模型的突破显示出在语言任务中的特殊能力.
- 这些进步推动了蛋白质研究的快速发展和前所未有的表现.
研究的目的:
- 提供蛋白质生成模型的概述.
- 审查新型人工蛋白质设计的语言模型.
- 在指导进化中探索非变压器架构和应用.
主要方法:
- 关于蛋白质生成模型的最新文献的综述.
- 对应蛋白质序列生成的语言模型的分析.
- 检查蛋白质设计中的非变压器架构.
- 在定向进化策略中的应用的研究.
主要成果:
- 语言模型越来越多地用于设计新型的人工蛋白质.
- 在未经探索的蛋白质空间中生成序列方面取得了重大进展.
- 正在使用各种架构,包括变压器和非变压器.
- 生成模型在增强定向进化方法方面显示出前景.
结论:
- 生成模型,特别是那些基于语言模型架构的模型,正在改变蛋白质设计.
- 该领域正在迅速发展,新的方法实现了高性能.
- 未来的应用预计在合成生物学和蛋白质工程等领域.
- 对各种架构和应用的持续研究将进一步扩大蛋白质生成模型的潜力.
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