香港中文大学李煜等研究人员合作使用基于语言模型的深度学习方法，进行准确的RNA 3D结构预测。该研究于2024年11月21日在线发表于国际一流学术期刊《自然—方法学》。

研究人员提出了RhoFold+，一种基于RNA语言模型的深度学习方法，可以从序列中准确预测单链RNA的3D结构。通过集成对约2370万个RNA序列进行预训练的RNA语言模型，并利用技术解决数据短缺问题，RhoFold+为RNA 3D结构预测提供了一个完全自动化的端到端管线。

对RNA-Puzzle和CASP15天然RNA靶点的回顾性评估表明，RhoFold+优于包括人类专家组在内的现有方法。通过跨家庭和跨类型评估以及时间审查基准，进一步验证了其有效性和普遍性。此外，RhoFold+能够预测RNA二级结构和螺旋间角，研究人员提供了经验验证的特征，并扩大了其在RNA结构和功能研究中的适用性。

据了解，准确预测RNA的3D结构仍然是一个未解决的挑战。确定RNA的3D结构对于理解其功能和为RNA靶向药物开发和合成生物学设计提供信息至关重要。RNA的结构灵活性导致实验确定数据的稀缺性，使计算预测工作复杂化。

附：英文原文

Title: Accurate RNA 3D structure prediction using a language model-based deep learning approach

Author: Shen, Tao, Hu, Zhihang, Sun, Siqi, Liu, Di, Wong, Felix, Wang, Jiuming, Chen, Jiayang, Wang, Yixuan, Hong, Liang, Xiao, Jin, Zheng, Liangzhen, Krishnamoorthi, Tejas, King, Irwin, Wang, Sheng, Yin, Peng, Collins, James J., Li, Yu

Issue&Volume: 2024-11-21

Abstract: Accurate prediction of RNA three-dimensional (3D) structures remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The structural flexibility of RNA, which leads to the scarcity of experimentally determined data, complicates computational prediction efforts. Here we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. By integrating an RNA language model pretrained on ~23.7 million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction. Retrospective evaluations on RNA-Puzzles and CASP15 natural RNA targets demonstrate the superiority of RhoFold+ over existing methods, including human expert groups. Its efficacy and generalizability are further validated through cross-family and cross-type assessments, as well as time-censored benchmarks. Additionally, RhoFold+ predicts RNA secondary structures and interhelical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies.

DOI: 10.1038/s41592-024-02487-0

Source:https://www.nature.com/articles/s41592-024-02487-0