Zhou Yuanzhe, Jiang Yangwei, Chen Shi-Jie
Department of Physics and Astronomy, University of Missouri-Columbia, Columbia, Missouri 65211-7010, United States.
Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri-Columbia, Columbia, Missouri 65211-7010, United States.
J Chem Theory Comput. 2024 Aug 16. doi: 10.1021/acs.jctc.4c00681.
The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.
对RNA靶向药物日益增长的兴趣凸显了对RNA分子与小分子化合物之间相互作用进行计算建模的必要性。拥有一个可靠的RNA-配体相互作用评分函数对于有效的计算药物筛选至关重要。一个理想的评分函数不仅应该预测配体结合的天然构象，还应该对不同配体的结合亲和力进行排序。然而，现有的评分函数主要设计用于预测给定RNA-配体对的天然结合模式，并且尚未针对虚拟筛选目的进行全面评估。在本文中，我们介绍了SPRank，这是一种通过加权迭代方法开发的机器学习和基于知识的评分函数的组合，专门设计用于应对结合模式预测和虚拟筛选挑战。我们的方法结合了第三方对接软件，如rDock和AutoDock Vina，以针对RNA结构集合对柔性配体进行采样，捕捉RNA和配体的构象灵活性。通过严格测试，与在包含122、42、55和71个核酸-配体复合物的四个测试集上测试的评分函数相比，SPRank表现出更好的性能。此外，SPRank在针对HIV-1 TAR集合的虚拟筛选测试中表现出更好的性能，这突出了其在药物发现中的优势。这些结果强调了SPRank作为一种潜在的有前途的RNA靶向药物设计工具的优势。SPRank的源代码和数据集可在https://github.com/Vfold-RNA/SPRank上免费获取。
