从分子到基因组规模的序列建模和设计
Eric Nguyen1,2, Michael Poli3,4, Matthew G Durrant1
1Arc Institute, Palo Alto, CA, USA.
概括
一个新的基因组基础模型,解码DNA,RNA和蛋白质. 这种人工智能模型预测生物功能和设计遗传系统,推进合成生物学和基因组理解.
科学领域:
- 基因组学
- 生物信息学
- 合成生物学
背景情况:
- 基因组编码DNA,RNA和对生物体功能至关重要的蛋白质.
- 基础模型在语言和视觉方面取得了成功, 但基因组应用正在出现.
研究的目的:
- 介绍一下Evo,一个长文本基因组基础模型.
- 为了探索DNA序列的缩放规律.
- 在分子和基因组尺度上展示Evo的预测和生成能力.
主要方法:
- 培训了数以百万计的原生物和菌体基因组.
- 开发了长文本基因组数据的前沿架构.
- 评估了Evo的零射击预测和序列生成能力.
主要成果:
- 通过对DNA,RNA和蛋白质进行概括,实现了竞争性的零射击功能预测.
- 埃沃成功生成了功能性的CRISPR-Cas和转子系统 (蛋白质-RNA和蛋白质-DNA编码设计).
- 埃沃学会了突变对身体健康的影响, 并产生了具有现实架构的大规模基因组序列.
结论:
- 这代表了基因组基础模型的重大进步.
- 该模型在预测生物功能和设计遗传系统方面展示了新的能力.
- 在多个尺度上对生物系统的理解和控制有潜力.
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