Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA

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Improving de novo protein binder design with deep learning (2022, unknown): “An updated version of RoseTTAFold (RF2 22,23 ) was also found to predict all monomer structures with binder C ɑ accuracy between 0.2 Å − 0.8Å, except for TrkA which was predicted with 1.8 Å accuracy (Supplementary Fig.”
Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022, unknown): “For instance, DRfold had a higher TM-score(0.435)than RhoFold (0.420) and RoseTTAFoldRNA (0.428), but lower than DeepFoldRNA (0.485) and trRosettaRNA (0.474); the differences between them were not statistically signi ﬁ cant except for DeepFoldRNA which had a p -value= 1.66E − 02 against DRfold.”
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization (2023, methodology): “…and to employ it as a module within novel algorithms, including ones for protein complex prediction (Baek 2021), peptide-protein interactions (Tsaban et al. 2022), structure ranking (Roney and Ovchinnikov 2022), and more ( e.g. , Baltzis et al. 2022, Bryant et al. 2022, Wayment-Steele et al. 2022).”
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models (2022, unknown): “RF has recently been extended to nucleic and protein-nucleic acid complexes 13 which should enable diffusive modeling of protein-nucleic acid assemblies.”