We present an RNA language model-based deep learning pipeline for accurate and rapid de novo RNA 3D structure prediction, demonstrating strong accuracy in modeling single-stranded RNAs and excellent generalization across RNA families and types while also being capable of capturing local features such as interhelical angles and secondary structures.
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References
Zhang, J. et al. Advances and opportunities in RNA structure experimental determination and computational modeling. Nat. Methods 19, 1193–1207 (2022). This paper summarizes traditional experimental and computational technologies developed to predict and evaluate RNA secondary and tertiary structures.
Boniecki, M. J. et al. SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Res. 44, e63 (2016). This paper introduces a traditional computational method for RNA folding and predicting 3D structures that is based on a knowledge-based scoring function and energy minimization techniques.
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). This paper introduced AlphaFold2, a deep learning model to predict protein structure accurately.
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023). This paper proposed a protein language model trained on a large scale and a structure prediction model using only a single sequence.
Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. 41, 1099–1106 (2023). This paper introduces a language model that can generate protein sequences with a predictable function across large protein families.
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This is a summary of: Shen, T. et al. Accurate RNA 3D structure prediction using a language model-based deep learning approach. Nat. Methods https://doi.org/10.1038/s41592-024-02487-0 (2024).
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Large language modeling and deep learning shed light on RNA structure prediction. Nat Methods 21, 2237–2238 (2024). https://doi.org/10.1038/s41592-024-02488-z
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DOI: https://doi.org/10.1038/s41592-024-02488-z
