RNA language models predict mutations that improve RNA function.
RNA language models predict mutations that improve RNA function.
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RNA 语言模型预测可改善 RNA 功能的突变。
DOI：
10.1101/2024.04.05.588317
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Doudna,Jenni
中科院分区：
文献类型：
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作者：
Shulgina,Yekaterina;Trinidad,MarenaI;Langeberg,ConnerJ;Nisonoff,Hunter;Chithrananda,Seyone;Skopintsev,Petr;Nissley,AmosJ;Patel,Jaymin;Boger,RonS;Shi,Honglue;Yoon,PeterH;Doherty,ErinE;Pande,Tara;Iyer,AdityaM;Doudna,Jenni
结构化 RNA 是从基因表达到催化等许多重要生物过程的核心。由于缺乏与有机体表型相关的高质量参考数据（可以告知 RNA 功能），RNA 结构预测尚不可能。我们推出了 GARNET（Gtdb Acquired RNA with Environmental Thermals），这是一个基于基因组分类数据库 (GTDB) 的用于 RNA 结构和功能分析的新数据库。 GARNET 将 RNA 序列与 GTDB 参考生物体的实验和预测最佳生长温度联系起来。使用 GARNET，我们开发了序列和结构感知的 RNA 生成模型，其中重叠的三联体标记化为类似 GPT 的模型提供了最佳编码。利用 GARNET 中的超嗜热 RNA 和这些 RNA 生成模型，我们确定了核糖体 RNA 中的突变，这些突变赋予大肠杆菌核糖体更高的热稳定性。这里介绍的 GTDB 衍生数据和深度学习模型为理解 RNA 序列、结构和功能之间的联系奠定了基础。
Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence-and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.