Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, 1901 East Road, Houston Texas 77054, USA.
J Chem Inf Model. 2012 Oct 22;52(10):2741-53. doi: 10.1021/ci300320t. Epub 2012 Sep 21.
Substantial progress in RNA biology highlights the importance of RNAs (e.g., microRNAs) in diseases and the potential of targeting RNAs for drug discovery. However, the lack of RNA-specific modeling techniques demands the development of new tools for RNA-targeted rational drug design. Herein, we implemented integrated approaches of accurate RNA modeling and virtual screening for RNA inhibitor discovery with the most comprehensive evaluation to date of five docking and 11 scoring methods. For the first time, statistical analysis was heavily employed to assess the significance of our predictions. We found that GOLD:GOLD Fitness and rDock:rDock_solv could accurately predict the RNA ligand poses, and ASP rescoring further improved the ranking of ligand binding poses. Due to the weak correlations (R(2) < 0.3) of existing scoring with experimental binding affinities, we implemented two new RNA-specific scoring functions, iMDLScore1 and iMDLScore2, and obtained better correlations with R(2) = 0.70 and 0.79, respectively. We also proposed a multistep virtual screening approach and demonstrated that rDock:rDock_solv together with iMDLScore2 rescoring obtained the best enrichment on the flexible RNA targets, whereas GOLD:GOLD Fitness combined with rDock_solv rescoring outperformed other methods for rigid RNAs. This study provided practical strategies for RNA modeling and offered new insights into RNA-small molecule interactions for drug discovery.
在 RNA 生物学领域取得了重大进展，突显了 RNA（例如 microRNAs）在疾病中的重要性，以及针对 RNA 进行药物发现的潜力。然而，缺乏 RNA 特异性建模技术要求开发新的工具，用于针对 RNA 的合理药物设计。在此，我们实施了综合的 RNA 精确建模和虚拟筛选方法，对迄今为止的五种对接和十一种评分方法进行了最全面的评估。首次，我们大量使用统计分析来评估我们的预测的重要性。我们发现，GOLD:GOLD Fitness 和 rDock:rDock_solv 可以准确预测 RNA 配体的构象，而 ASP 重新评分进一步提高了配体结合构象的排名。由于现有评分与实验结合亲和力之间的相关性较弱（R(2) < 0.3），我们实施了两个新的 RNA 特异性评分函数，iMDLScore1 和 iMDLScore2，它们分别与 R(2) = 0.70 和 0.79 具有更好的相关性。我们还提出了一种多步虚拟筛选方法，并证明 rDock:rDock_solv 与 iMDLScore2 重新评分相结合，可以更好地富集柔性 RNA 靶标，而 GOLD:GOLD Fitness 与 rDock_solv 重新评分相结合则优于其他方法，用于刚性 RNA。这项研究为 RNA 建模提供了实用策略，并为药物发现中的 RNA-小分子相互作用提供了新的见解。
