PDF(Pubmed)
Abstract：
The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics simulations and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identifies all the experimentally resolved pockets and ranks them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.
摘要：
我们对RNA结构和动态特性的理解仍然有限，这阻碍了用小分子对RNA的合理靶向。用于结合位点鉴定的大多数计算机模拟工具依赖于静态结构,因此不能面对由RNA分子的动态性质带来的挑战。这里,我们介绍沙曼,一种识别RNA结构集合中潜在小分子结合位点的计算技术。SHAMAN能够通过原子分子动力学模拟探索RNA的构象景观，同时借助探针和增强采样技术以有效的方式识别RNA口袋。在我们的基准组成中，结构化的核糖开关以及小型，灵活的病毒RNA，SHAMAN成功地识别了所有实验解决的口袋，并将它们列为最喜欢的探针热点。总的来说,SHAMAN为未来靶向小分子RNA的药物设计工作奠定了坚实的基础。有效应对该领域的长期挑战。
