用语言模型对原子级蛋白质结构的进化规模预测
Zeming Lin1,2, Halil Akin1, Roshan Rao1
1FAIR, Meta AI, New York, NY, USA.
概括
大型语言模型现在可以直接从主要序列中推断出原子级蛋白质结构. 这一突破加速了结构预测,使得ESM能够创建超过6.17亿个蛋白质结构的元基因图谱.
科学领域:
- 计算生物学
- 人工智能
- 结构生物学
背景情况:
- 机器学习的进步利用多个序列对齐的进化信息来预测蛋白质结构.
- 目前的方法通常需要大量的计算资源和时间.
研究的目的:
- 使用大型语言模型从初级序列中直接推断出完整的原子级蛋白质结构.
- 在高分辨率结构预测中实现数量级加速.
- 为了实现大规模的基因组蛋白质结构特征.
主要方法:
- 使用一个扩展到150亿参数的大型语言模型.
- 在蛋白质序列上训练模型以学习其表示中的原子分辨率结构信息.
- 应用该模型来预测大基因组蛋白序列的结构.
主要成果:
- 蛋白质结构的原子分辨率图像出现在扩展语言模型的学习表现中.
- 在高分辨率蛋白质结构预测中实现了数量级加速.
- 成功构建ESM转基因图谱,预测了超过6.17亿个转基因蛋白序列的结构.
- 确定了超过2.25亿个具有高可靠性结构预测的蛋白质序列.
结论:
- 大型语言模型提供了一种强大而高效的方法,用于从初级序列直接预测蛋白质结构.
- 这种方法显著加速了这一过程,使得生物数据的前所未有的大规模结构性表征成为可能.
- 欧洲基因组图谱为探索自然蛋白质的多样性提供了宝贵的资源.
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