Simulating 500 million years of evolution with a language model

IF 44.7 1区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Science Pub Date : 2025-01-16 DOI:10.1126/science.ads0018

Thomas Hayes, Roshan Rao, Halil Akin, Nicholas J. Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q. Tran, Jonathan Deaton, Marius Wiggert, Rohil Badkundri, Irhum Shafkat, Jun Gong, Alexander Derry, Raul S. Molina, Neil Thomas, Yousuf A. Khan, Chetan Mishra, Carolyn Kim, Liam J. Bartie, Matthew Nemeth, Patrick D. Hsu, Tom Sercu, Salvatore Candido, Alexander Rives

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引用次数: 0

Abstract

More than 3 billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here, we show that language models trained at scale on evolutionary data can generate functional proteins that are far away from known proteins. We present ESM3, a frontier multimodal generative language model that reasons over the sequence, structure, and function of proteins. ESM3 can follow complex prompts combining its modalities and is highly responsive to alignment to improve its fidelity. We have prompted ESM3 to generate fluorescent proteins. Among the generations that we synthesized, we found a bright fluorescent protein at a far distance (58% sequence identity) from known fluorescent proteins, which we estimate is equivalent to simulating 500 million years of evolution.

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用语言模型模拟5亿年的进化

30多亿年的进化已经产生了一种编码在天然蛋白质空间中的生物图像。在这里，我们展示了在进化数据上进行大规模训练的语言模型可以生成与已知蛋白质相距甚远的功能蛋白质。我们提出了ESM3，一个前沿的多模态生成语言模型，可以对蛋白质的序列、结构和功能进行推理。ESM3可以遵循复杂的提示结合其模式，并高度响应校准，以提高其保真度。我们已经促使ESM3产生荧光蛋白。在我们合成的世代中，我们发现了一个与已知荧光蛋白距离很远的明亮荧光蛋白（58%的序列同源性），我们估计这相当于模拟了5亿年的进化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Science 综合性期刊-综合性期刊

CiteScore

61.10

自引率

0.90%

发文量

审稿时长

2.1 months

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