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RNA 谜题第四轮:四种核酶和两种适体的 3D 结构预测。

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

机构信息

Translational Research Institute of Brain and Brain-Like Intelligence and Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200081, China.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, CB10 1SD, United Kingdom.

出版信息

RNA. 2020 Aug;26(8):982-995. doi: 10.1261/rna.075341.120. Epub 2020 May 5.

Abstract

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.

摘要

RNA 结构预测拼图是一项集体努力,致力于推进和改进 RNA 三维结构预测。在晶体结构发表之前,晶体学家们同意由不同的小组预测 RNA 结构。我们现在报告了六个 RNA 序列的三维结构预测:四个核酸酶核酶和两个核糖开关。描述和分析了用于比较模型和晶体结构的系统方案。在这六个谜题中,我们讨论了:(i)自动网络服务器和人类专家之间的比较;(ii)同轴堆叠的预测;(iii)结构细节和配体结合的预测;(iv)新预测方法的开发;以及(v)潜在的改进。我们表明,正确预测同轴堆叠和三级接触对于 RNA 结构的预测至关重要,而配体结合模式只能以低分辨率预测,并且同时准确预测 RNA 结构和配体结合仍然遥不可及。所有预测的模型都可用于进一步开发力场参数以及改进比较和评估工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9bf/7373991/f7a43d5805ad/982f01.jpg

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