1. Accurate RNA 3D structure prediction using a language model-based deep learning approach.
- Author
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Shen T, Hu Z, Sun S, Liu D, Wong F, Wang J, Chen J, Wang Y, Hong L, Xiao J, Zheng L, Krishnamoorthi T, King I, Wang S, Yin P, Collins JJ, and Li Y
- 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., Competing Interests: Competing interests: S.W. and L.Z. are the co-founders of Zelixir Biotech Co. Ltd. P.Y. is a co-founder, equity holder, board member and consultant of Ultivue, Inc., Spear Bio, Inc. and Digital Biology, Inc. J.J.C. is the founding scientific advisory board chair of Integrated Biosciences. F.W. is a co-founder of Integrated Biosciences. The other authors declare no competing interests., (© 2024. The Author(s).)
- Published
- 2024
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