深度设计合成RNA家族序列，这一成果由日本京都大学Hirohide Saito和早稻田大学Michiaki Hamada研究组合作经过不懈努力而取得。相关论文发表在2024年1月18日出版的《自然—方法学》杂志上。

研究人员建立了RNA家族序列生成器（RfamGen），这是一种深度生成模型，通过明确结合比对和已知的二级结构信息以数据高效的方式设计RNA家族序列。RfamGen可以在功能丰富的连续表示中通过采样点，来生成新颖且功能性强的RNA家族序列。研究利用不同的RNA家族实验证明了RfamGen的多功能性。

此外，研究还通过大规模平行定量检测证实了RfamGen在设计功能性核酶方面的高成功率。值得注意的是，RfamGen生成的人工序列比天然序列具有更高的活性。总之，RfamGen显著提高了设计功能性核糖核酸的能力，为合成生物学中核糖核酸的生物合成开辟了新前景。

据悉，RNA工程具有推动生物技术和医学创新的巨大潜力。尽管其重要性不言而喻，但自动设计RNA的多功能平台仍然缺乏。

附：英文原文

Title: Deep generative design of RNA family sequences

Author: Sumi, Shunsuke, Hamada, Michiaki, Saito, Hirohide

Issue&Volume: 2024-01-18

Abstract: RNA engineering has immense potential to drive innovation in biotechnology and medicine. Despite its importance, a versatile platform for the automated design of functional RNA is still lacking. Here, we propose RNA family sequence generator (RfamGen), a deep generative model that designs RNA family sequences in a data-efficient manner by explicitly incorporating alignment and consensus secondary structure information. RfamGen can generate novel and functional RNA family sequences by sampling points from a semantically rich and continuous representation. We have experimentally demonstrated the versatility of RfamGen using diverse RNA families. Furthermore, we confirmed the high success rate of RfamGen in designing functional ribozymes through a quantitative massively parallel assay. Notably, RfamGen successfully generates artificial sequences with higher activity than natural sequences. Overall, RfamGen significantly improves our ability to design functional RNA and opens up new potential for generative RNA engineering in synthetic biology.

DOI: 10.1038/s41592-023-02148-8

Source: https://www.nature.com/articles/s41592-023-02148-8