We developed a machine learning model, RoseTTAFoldNA, that can predict the structures of protein–DNA and protein–RNA complexes. Our model is capable of predicting accurate structures of protein families for which structural information is unknown.
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References
Lambert, S. et al. The human transcription factors. Cell 172, 650–665 (2018). This paper gives an overview of human transcription factors, including some understudied families.
Joyce, A. et al. Structure-based modeling of protein: DNA specificity. Brief. Funct. Genomics 14, 39–49 (2021). This paper reviews structure-based predictions of DNA binding specificity.
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). This paper reports AlphaFold2, a neural network trained to predict protein structure with a high degree of accuracy.
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021). This paper presents RoseTTAFold, an alternative neural network architecture to AlphaFold that also predicts protein structure to a high degree of accuracy.
Castro-Mondragon, J. A. et al. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 50, D165–D173 (2022). This paper describes the curation of JASPER, a database of known protein–DNA interactions.
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This is a summary of: Baek, M. et al. Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA. Nat. Methods https://doi.org/10.1038/s41592-023-02086-5 (2023).
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Using machine learning to predict the structure of proteins that bind to DNA and RNA. Nat Methods 21, 22–23 (2024). https://doi.org/10.1038/s41592-023-02088-3
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DOI: https://doi.org/10.1038/s41592-023-02088-3
