蛋白质序列结构图的尚未探索的区域被一个折叠语言模型图书馆的大小揭示了
Arjuna M Subramanian1, Zachary A Martinez1, Alec L Lourenço1
1Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA.
bioRxiv : the preprint server for biology
|January 8, 2024
概括
蛋白质语言模型 (pLMs) 使用一种名为"折叠调整"的新方法探索超越自然进化的新型蛋白质序列. 这种方法产生稳定,功能性蛋白质与独特的序列合成生物学应用.
科学领域:
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质的工程.
- 合成生物学 合成生物学
背景情况:
- 蛋白质序列空间的广性限制了对蛋白质序列结构图的理解.
- 探索远离自然例子的序列是当前实验方法 (如定向进化) 的挑战.
研究的目的:
- 开发一种新的方法",折叠调节",以探索超越自然进化的蛋白质序列空间.
- 使用蛋白质语言模型 (pLMs) 来生成具有所需结构约束的新型蛋白质序列.
主要方法:
- 利用蛋白质语言模型 (pLMs) 进行序列生成和自我反,探索未知的序列空间.
- 从SCOP数据库中开发一个超过700个自然折叠的折叠调整的pLM库.
- 应用折叠调整对高优先级的目标,包括GPCRs,GTPases和受体/结合域.
主要成果:
- 折叠调整的plm产生了具有超出可检测同质性的新型序列"规则"的候选蛋白质.
- 产生的蛋白质表现出微妙的结构变化,模仿自然多样化.
- 实验验证证证实,折叠调整的蛋白质变体表达,稳定折叠,并在体外和体内运行.
结论:
- 折叠调整使我们能够在规模上探索蛋白质序列结构关系,而不依赖于进化背景.
- 这种方法有望推进新型合成生物系统的设计,用于健康和催化.
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