Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China.
Phys Chem Chem Phys. 2024 Mar 27;26(13):10323-10335. doi: 10.1039/d3cp04366e.
Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants () and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GB model with higher interior dielectric constant ( = 12, 16 or 20) yields the best correlation ( = -0.513), which outperforms the best correlation ( = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.
核糖核酸（RNA）-配体相互作用在广泛的生物学过程中起着至关重要的作用，从蛋白质生物合成到细胞繁殖。这种认识促使人们更广泛地接受 RNA 作为药物靶点的可行候选物。深入了解 RNA-配体相互作用的原子尺度对于揭示复杂的分子机制并进一步促进基于 RNA 的药物发现具有至关重要的意义。计算方法，特别是分子对接，提供了一种预测 RNA 与小分子相互作用的有效方法。然而，这些预测的准确性和可靠性在很大程度上取决于评分函数（SFs）的性能。与用于 RNA-配体对接的大多数 SF 不同，端点结合自由能计算方法，如分子力学/广义 Born 表面积（MM/GBSA）和分子力学/泊松 Boltzmann 表面积（MM/PBSA），是理论上更严格的方法。然而，评估它们在预测 RNA-配体系统中的结合亲和力和结合构象方面的有效性仍然是未知的。本研究首次报道了不同溶剂模型、内部介电常数（）和力场在 29 个 RNA-配体复合物结合亲和力预测中的 MM/PBSA 和 MM/GBSA 的性能。MM/GBSA 是基于具有 YIL 力场的显式溶剂中的短（5 ns）分子动力学（MD）模拟；具有较高内部介电常数（= 12、16 或 20）的 GB 模型产生最佳相关性（= -0.513），优于各种对接程序提供的最佳相关性（= -0.317，rDock）。然后，基于 56 个 RNA-配体复合物评估了 MM/GBSA 从诱饵中识别近天然结合构象的能力。然而，显然 MM/GBSA 在准确预测 RNA-配体系统的结合构象方面存在局限性，特别是与 rDock 和 PLANTS 等明显高效的对接程序相比。MM/GBSA 重新评分的最佳前 1 成功率为 39.3%，低于对接程序（50%，PLNATS）的最佳结果。本研究代表了对 MM/PBSA 和 MM/GBSA 用于 RNA-配体系统的首次评估，预计将为成功应用于 RNA 靶点提供有价值的见解。
