Driving factors in amiloride recognition of HIV RNA targets
Driving factors in amiloride recognition of HIV RNA targets
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DOI：
10.1039/c9ob01702j
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发表时间：
2019-11-14
影响因子：
3.2
通讯作者：
Hargrove, Amanda E.
中科院分区：
文献类型：
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作者：
Patwardhan, Neeraj N.;Cai, Zhengguo;Hargrove, Amanda E.
非编码RNA是越来越有希望的药物靶标，但由于缺乏揭示选择性小分子：RNA相互作用驱动因素的方法，特别是考虑到高分辨率结构表征的困难，配体设计受到阻碍。鉴于HIV rna的靶向历史、已知的结构-功能关系以及对更有效治疗的未满足需求，它们是开发方法的优秀模型系统。在此，我们报告了一种结合合成多样化、针对多种RNA靶点的分析和预测化学信息学分析的策略，以确定小分子对不同HIV RNA靶点的选择性和亲和力的驱动因素。使用这种策略，我们发现了针对多个靶标的改进配体和针对ESSV的第一个配体，ESSV是一种对复制至关重要的外显子剪接沉默子。计算分析揭示了未来设计的指导原则和小分子预测化学信息学模型：RNA结合。这些方法有望促进选择性靶向致病rna的研究进展。
Noncoding RNAs are increasingly promising drug targets yet ligand design is hindered by a paucity of methods that reveal driving factors in selective small molecule : RNA interactions, particularly given the difficulties of high-resolution structural characterization. HIV RNAs are excellent model systems for method development given their targeting history, known structure-function relationships, and the unmet need for more effective treatments. Herein we report a strategy combining synthetic diversification, profiling against multiple RNA targets, and predictive cheminformatic analysis to identify driving factors for selectivity and affinity of small molecules for distinct HIV RNA targets. Using this strategy, we discovered improved ligands for multiple targets and the first ligands for ESSV, an exonic splicing silencer critical to replication. Computational analysis revealed guiding principles for future designs and a predictive cheminformatics model of small molecule : RNA binding. These methods are expected to facilitate progress toward selective targeting of disease-causing RNAs.