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

Zhichiao MiaoRyszard W AdamiakMaciej AntczakMichał J BonieckiJanusz M BujnickiShi-Jie ChenClarence Yu ChengYi ChengFang-Chieh ChouRachel J HageyNikolay V DokholyanFeng DingCaleb GeniesseYangwei JiangAstha JoshiAndrey KrokhotinMarcin MagnusOlivier MailhotFrancois MajorThomas H MannPaweł PiątkowskiRadoslaw PlutaMariusz PopendaJoanna SarzyńskaLizhen SunMarta SzachniukSiqi TianJian WangJun WangAndrew M WatkinsJakub WiedemannYi XiaoXiaojun XuJoseph D YesselmanDong ZhangYi ZhangZhenzhen ZhangChenhan ZhaoPeinan ZhaoYuanzhe ZhouTomasz ZokAdriana ŻyłaAiming RenRobert T BateyBarbara L GoldenLin HuangDavid M J LilleyYijin LiuDinshaw J PatelEric Westhof
Published in: RNA (New York, N.Y.) (2020)
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.
Keyphrases
  • nucleic acid
  • machine learning
  • endothelial cells
  • deep learning
  • climate change
  • clinical evaluation