Probing Viral Genomic Structure: Alternative Viewpoints and Alternative Structures for Satellite Tobacco Mosaic Virus RNA
Probing Viral Genomic Structure: Alternative Viewpoints and Alternative Structures for Satellite Tobacco Mosaic Virus RNA
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DOI：
10.1021/bi501051k
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发表时间：
2014-11-04
期刊：
影响因子：
2.9
通讯作者：
Schroeder, Susan J.
中科院分区：
文献类型：
--
作者：
Schroeder, Susan J.
病毒RNA结构预测是开发抗病毒疾病药物的重要工具。这项工作讨论了预测衣壳病毒 RNA 的不同方法，并强调卫星烟草花叶病毒 (STMV) RNA 作为具有出色晶体学数据的模型系统。争论的基本重要问题包括病毒组装的热力学与动力学控制，以及一级结构中的准物种对单个结构或结构集合的 RNA 二级结构预测的可能后果。现在有多种计算工具和化学试剂可用于改进病毒 RNA 结构预测。衣壳化 STMV RNA 的两种不同预测结构源于三个主要领域的差异：研究衣壳化病毒 RNA 的不同方法和理念、对不同 RNA 基序的重视以及计算方法和化学试剂的技术差异。使用传统化学探测和 SHAPE 试剂进行的实验在 STMV RNA 以及其他 RNA 蛋白组装体（例如 HIV 的 5'UTR 和核糖体）的化学、结果和解释方面进行了比较。对病毒 RNA 结构预测挑战的讨论将带来新的实验并改进对病毒 RNA 的未来预测。
Viral RNA structure prediction is a valuable tool for development of drugs against viral disease. This work discusses different approaches to predicting encapsidated viral RNA and highlights satellite tobacco mosaic virus (STMV) RNA as a model system with excellent crystallography data. Fundamentally important issues for debate include thermodynamic versus kinetic control of virus assembly and the possible consequences of quasi-species in the primary structure on RNA secondary structure prediction of a single structure or an ensemble of structures. Multiple computational tools and chemical reagents are now available for improved viral RNA structure prediction. Two different predicted structures for encapsidated STMV RNA result from differences in three main areas: a different approach and philosophy to studying encapsidated viral RNA, an emphasis on different RNA motifs, and technical differences in computational methods and chemical reagents. The experiments with traditional chemical probing and SHAPE reagents are compared in terms of chemistry, results, and interpretation for STMV RNA as well as other RNA protein assemblies, such as the 5'UTR of HIV and the ribosome. This discussion of the challenges of viral RNA structure prediction will lead to new experiments and improved future predictions for viral RNA.