Sieving RNA 3D Structures with SHAPE and Evaluating Mechanisms Driving Sequence-Dependent Reactivity Bias

被引:5
|
作者
Hurst, Travis [1 ,2 ]
Chen, Shi-Jie [1 ,2 ]
机构
[1] Univ Missouri, Dept Phys, Dept Biochem, Columbia, MO 65211 USA
[2] Univ Missouri, Inst Data Sci & Informat, Columbia, MO 65211 USA
来源
JOURNAL OF PHYSICAL CHEMISTRY B | 2021年 / 125卷 / 04期
基金
美国国家科学基金会;
关键词
37;
D O I
10.1021/acs.jpcb.0c11365
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) chemical probing provides local RNA flexibility information at single-nucleotide resolution. In general, SHAPE is thought of as a secondary structure (2D) technology, but we find evidence that robust tertiary structure (3D) information is contained in SHAPE data. Here, we report a new model that achieves a higher correlation between SHAPE data and native RNA 3D structures than the previous 3D structure-SHAPE relationship model. Furthermore, we demonstrate that the new model improves our ability to discern between SHAPE-compatible and -incompatible structures on model decoys. After identifying sequence-dependent bias in SHAPE experiments, we propose a mechanism driving sequence-dependent bias in SHAPE experiments, using replica-exchange umbrella sampling simulations to confirm that the SHAPE sequence bias is largely explained by the stability of the unreacted SHAPE reagent in the binding pocket. Taken together, this work represents multiple practical advances in our mechanistic and predictive understanding of SHAPE technology.
引用
收藏
页码:1156 / 1166
页数:11
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