Department of Biomedical Informatics, Stanford University, Stanford, CA 94305, USA.
Biochemistry. 2012 Sep 11;51(36):7037-9. doi: 10.1021/bi3008802. Epub 2012 Aug 29.
For decades, dimethyl sulfate (DMS) mapping has informed manual modeling of RNA structure in vitro and in vivo. Here, we incorporate DMS data into automated secondary structure inference using an energy minimization framework developed for 2'-OH acylation (SHAPE) mapping. On six noncoding RNAs with crystallographic models, DMS-guided modeling achieves overall false negative and false discovery rates of 9.5% and 11.6%, respectively, comparable to or better than those of SHAPE-guided modeling, and bootstrapping provides straightforward confidence estimates. Integrating DMS-SHAPE data and including 1-cyclohexyl(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate (CMCT) reactivities provide small additional improvements. These results establish DMS mapping, an already routine technique, as a quantitative tool for unbiased RNA secondary structure modeling.
几十年来，二甲基硫酸盐（DMS）图谱一直为体外和体内 RNA 结构的手动建模提供信息。在这里，我们将 DMS 数据纳入到一个能量最小化框架中，该框架是为 2'-OH 酰化（SHAPE）图谱开发的，用于自动二级结构推断。在具有晶体结构模型的六个非编码 RNA 上，DMS 引导建模的总体假阴性和假发现率分别为 9.5%和 11.6%，与 SHAPE 引导建模相当或更好，并且自举提供了简单的置信度估计。整合 DMS-SHAPE 数据并包括 1-环己基(2-吗啉乙基)碳化二亚胺甲氧基对甲苯磺酸盐（CMCT）反应性提供了很小的额外改进。这些结果将 DMS 图谱（一种已经常规使用的技术）确立为一种用于无偏 RNA 二级结构建模的定量工具。
