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

By Tao Shen, Zhihang Hu, Fred Zhangzhi Peng, Jiayang Chen, Peng Xiong, Liang Hong, Liangzhen Zheng, Yixuan Wang, Irwin King, Sheng Wang and 2 more. 2022

213 citations recorded by the source corpus.

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. RhoFold+ is an end-to-end language model-based deep learning method to predict RNA three-dimensional structures of single-chain RNAs from sequences.

Subfields: Molecular Biology, Large Language Models, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Peng Xiong
2. Pushmeet Kohli
3. Alexander Pritzel
4. Yaoqi Zhou
5. K. Chen
6. Sheng Wang
7. Eirini Arvaniti
8. Michael Figurnov

In Large Language Models

1. Anna Potapenko
2. Eirini Arvaniti
3. Demis Hassabis

In Computational Biology & Bioinformatics

1. Peng Xiong
2. Pushmeet Kohli
3. Kathryn Tunyasuvunakool
4. Eirini Arvaniti
5. Ellen D. Zhong

Citations in the corpus

• Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024, background): “We did not reach the performance of the best human-expert-aided CASP15 submission AIchemy_RNA2 39 (Fig.”
• RNA tertiary structure modeling with BRiQ potential in CASP15 (2023, methodology): “…protein structures, a variety of approaches have been developed for predicting the RNA 3D structure, 7 including homology modeling, 8,9 fragment assembly, 10 – 16 and de novo prediction with coarse grained models 17 – 20 as well as deep learning methods, 21 – 26 following the success of AlphaFold2.”