ProGen2:探索蛋白质语言模型的边界
Erik Nijkamp1, Jeffrey A Ruffolo2, Eli N Weinstein3
1Salesforce Research, Palo Alto, CA, USA.
Cell systems
|November 1, 2023
概括
我们开发了ProGen2,大型蛋白质语言模型训练了超过10亿个蛋白质序列. 这些模型通过改进序列生成和健身预测,在蛋白质设计中推进人工智能.
科学领域:
- 计算生物学 计算生物学
- 人工智能在生物学中的应用
- 蛋白质工程是指蛋白质工程.
背景情况:
- 基于注意力的模型在蛋白质序列分类和人工智能驱动的蛋白质设计的生成方面表现出色.
- 了解大规模模型和数据对蛋白质模型开发的影响至关重要.
研究的目的:
- 介绍ProGen2,一个大型蛋白质语言模型套件.
- 研究模型大小和各种序列数据在蛋白质建模中的作用.
- 在蛋白质序列任务中建立最先进的性能.
主要方法:
- 开发的ProGen2模型达到6.4B参数.
- 在超过10亿个蛋白质序列 (基因组,元基因组,免疫谱) 的各种数据集上训练模型.
- 对序列分布,新型序列生成和蛋白质适应性预测的评估性能.
主要成果:
- 在关键任务中,ProGen2模型实现了最先进的性能.
- 在捕捉进化序列分布方面表现出卓越的能力.
- 成功生成了新的,可行的蛋白质序列,并且在没有微调的情况下预测了蛋白质适应性.
结论:
- 大型模型尺寸和广泛的蛋白质序列数据是有效蛋白质建模的关键.
- 数据分布是蛋白质序列模型性能的一个关键因素.
- 开源的ProGen2模型和代码有助于在蛋白质工程中广泛采用.
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