RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers

By Zhichao Miao, Ryszard W. Adamiak, Maciej Antczak, Michal J. Boniecki, Janusz M. Bujnicki, Shi-Jie Chen, C. Cheng, Yi Cheng, Fang-Chieh Chou, Rhiju Das and 40 more. 2020

141 citations recorded by the source corpus.

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.

Subfields: Molecular Biology

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Citations in the corpus

Assessment of three-dimensional RNA structure prediction in CASP15 (2023, neutral): “11 In 2021, it became clear that accelerations in RNA structure determination 12 would allow enough targets for a single CASP season.”
RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints (2023, methodology): “4 Based on this experience, we have developed a generalized workflow (Figure 1) that includes a set of modeling tools as well as human intervention procedures.”
Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022, unknown): “Meanwhile, we found that the average values of TM-score/RMSD on the CASP15 targets (=0.288/21.60Å , or =0.302/20.34 Å after excluding the super-long 720 NTs target R1138) are largely consistent with the benchmark testresults on the targets with a sequence identity cutoff of 60% to the training datas”
De Novo RNA Tertiary Structure Prediction at Atomic Resolution Using Geometric Potentials from Deep Learning (2022, methodology): “…method for RNA, as a control we also list the distance/orientation parameters taken from the predicted models by two state-of-the-art modeling methods: SimRNA 4 and Rosetta FARFAR2 5 , which have been among the most accurate automatic modeling approaches in previous RNA-Puzzles experiments 6,7,13 .”