Computational prediction and biochemical characterization of novel RNA aptamers to Rift Valley fever virus nucleocapsid protein.
Computational prediction and biochemical characterization of novel RNA aptamers to Rift Valley fever virus nucleocapsid protein.
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DOI：
10.1016/j.compbiolchem.2015.06.005
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发表时间：
2015-10
影响因子：
3.1
通讯作者：
Lodmell JS
中科院分区：
文献类型：
--
作者：
Ellenbecker M;St Goddard J;Sundet A;Lanchy JM;Raiford D;Lodmell JS
裂谷热病毒（RVFV）是一种在撒哈拉以南非洲和阿拉伯半岛流行的人畜病原体，有可能传播到世界其他地区。虽然目前还没有被证明有效和安全的治疗RVFV感染，一个潜在的治疗靶点是病毒编码的核衣壳蛋白（N）。在感染过程中，N与病毒RNA结合，干扰这种相互作用可以抑制病毒复制。为了深入了解N如何特异性识别病毒RNA，我们设计了一种算法，该算法使用距离矩阵和多维缩放来比较已知N结合RNA或适体的预测二级结构，这些RNA或适体在之前的体外进化实验中分离和表征。这些适体没有表现出明显的序列或预测的结构相似性，所以我们采用生物信息学的方法，提出新的适体的基础上分析和聚类的二级结构。我们筛选和评分的预测二级结构的新的随机产生的RNA序列在计算机上，并选择了几个这些推定的N-结合RNA的二级结构相似的已知的N-结合RNA。我们发现，总体而言，计算机生成的RNA序列在体外与N结合良好。此外，在用RVFV感染之前将这些RNA引入细胞中抑制了细胞培养物中的病毒复制。这项概念验证研究展示了生物信息学的预测能力和生物化学的经验能力如何共同利用来发现，合成和测试与RVFV N蛋白紧密结合的新RNA序列。这种方法很容易推广到其他应用。
Rift Valley fever virus (RVFV) is a potent human and livestock pathogen endemic to sub-Saharan Africa and the Arabian Peninsula that has potential to spread to other parts of the world. Although there is no proven effective and safe treatment for RVFV infections, a potential therapeutic target is the virally encoded nucleocapsid protein (N). During the course of infection, N binds to viral RNA, and perturbation of this interaction can inhibit viral replication. To gain insight into how N recognizes viral RNA specifically, we designed an algorithm that uses a distance matrix and multidimensional scaling to compare the predicted secondary structures of known N-binding RNAs, or aptamers, that were isolated and characterized in previous in vitro evolution experiment. These aptamers did not exhibit overt sequence or predicted structure similarity, so we employed bioinformatic methods to propose novel aptamers based on analysis and clustering of secondary structures. We screened and scored the predicted secondary structures of novel randomly generated RNA sequences in silico and selected several of these putative N-binding RNAs whose secondary structures were similar to those of known N-binding RNAs. We found that overall the in silico generated RNA sequences bound well to N in vitro. Furthermore, introduction of these RNAs into cells prior to infection with RVFV inhibited viral replication in cell culture. This proof of concept study demonstrates how the predictive power of bioinformatics and the empirical power of biochemistry can be jointly harnessed to discover, synthesize, and test new RNA sequences that bind tightly to RVFV N protein. The approach would be easily generalizable to other applications.