five

Comparative analysis of hairpin ribozyme structures and interference data

收藏
PubMed Central2002-03-15 更新2026-05-16 收录
下载链接:
https://pmc.ncbi.nlm.nih.gov/articles/PMC101345/
下载链接
链接失效反馈
官方服务:
资源简介:
Great strides in understanding the molecular underpinnings of RNA catalysis have been achieved with advances in RNA structure determination by NMR spectroscopy and X-ray crystallography. Despite these successes the functional relevance of a given structure can only be assessed upon comparison with biochemical studies performed on functioning RNA molecules. The hairpin ribozyme presents an excellent case study for such a comparison. The active site is comprised of two stems each with an internal loop that forms a series of non-canonical base pairs. These loops dock into each other to create an active site for catalysis. Recently, three independent structures have been determined for this catalytic RNA, including two NMR structures of the isolated loop A and loop B stems and a high-resolution crystal structure of both loops in a docked conformation. These structures differ significantly both in their tertiary fold and the nature of the non-canonical base pairs formed within each loop. Several of the chemical groups required to achieve a functioning hairpin ribozyme have been determined by nucleotide analog interference mapping (NAIM). Here we compare the three hairpin structures with previously published NAIM data to assess the convergence between the structural and functional data. While there is significant disparity between the interference data and the individual NMR loop structures, there is almost complete congruity with the X-ray structure. The only significant differences cluster around an occluded pocket adjacent to the scissile phosphate. These local differences may suggest a role for these atoms in the transition state, either directly in chemistry or via a local structural rearrangement.
提供机构:
Oxford University Press
创建时间:
2002-03-15
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作