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

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…

• BG
  Bowling Green State University
• RG
  Research Grants Council, University Grants Committee

  Award: 24204023
• FU
  Fudan University
• IA
  Innovation and Technology Commission
• CU
  Chinese University of Hong Kong
• IA
  Innovation and Technology Fund

  Award: GHP/065/21SZ
• NI
  National Institutes of Health
• NI
  National Institute of Allergy and Infectious Diseases