用语言模型模拟5亿年的进化
Thomas Hayes1, Roshan Rao1, Halil Akin1
1EvolutionaryScale, PBC, New York, NY, USA.
概括
大型语言模型现在可以通过从进化数据中学习来生成新的功能性蛋白质. 这一突破使得我们能够创造出不同于自然界的蛋白质,
科学领域:
- 生物技术
- 计算生物学
- 蛋白质工程
背景情况:
- 生物系统和蛋白质功能是经过数十亿年的进化编码的.
- 自然蛋白质代表了一个庞大的,但不完全理解的生物序列空间.
研究的目的:
- 证明在进化数据上训练的大规模语言模型可以产生新的功能性蛋白质.
- 介绍ESM3,一种蛋白质序列,结构和功能的多模式生成语言模型.
- 探索ESM3在产生具有所需特性的蛋白质的能力,例如光.
主要方法:
- 在大规模的进化蛋白数据上训练一种多式生成语言模型 (ESM3).
- 使用复杂的提示,结合序列,结构和功能模式来生成蛋白质.
- 合成和表征生成的蛋白质,包括光蛋白.
- 评估已知的蛋白质的序列标识以量化进化距离.
主要成果:
- ESM3成功地产生了与已知的蛋白家族相隔的功能蛋白.
- 产生了一种新的明亮光蛋白,其序列与已知的光蛋白相同58%.
- 产生的蛋白质的进化距离估计相当于5亿年.
结论:
- 大型语言模型是探索和扩展蛋白质序列空间的强大工具.
- ESM3可以产生功能性蛋白质,这些蛋白质代表着重要的进化新性.
- 这种方法有可能加速蛋白质工程和发现新的生物功能.
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