Department of Chemistry, Duke University, 124 Science Drive, Durham, North Carolina 27708, United States.
Social Science Research Institute, 140 Science Drive, Durham, North Carolina 27708, United States.
J Med Chem. 2022 May 26;65(10):7262-7277. doi: 10.1021/acs.jmedchem.2c00254. Epub 2022 May 6.
The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSARs). Herein, we develop QSAR models that quantitatively predict both thermodynamic- and kinetic-based binding parameters of small molecules and the HIV-1 transactivation response (TAR) RNA model system. Small molecules bearing diverse scaffolds were screened against TAR using surface plasmon resonance. Multiple linear regression (MLR) combined with feature selection afforded robust models that allowed direct interpretation of the properties critical for both binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performance was confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform.
RNA 结构元件的多样性及其在人类疾病中的作用已被证实，这使得 RNA 成为一个有吸引力的治疗靶点。然而，药物发现和开发的进展受到确定高分辨率 RNA 结构的挑战以及对小分子识别 RNA 的参数的理解有限的阻碍，包括缺乏经过验证的定量构效关系（QSAR）。在此，我们开发了 QSAR 模型，可定量预测小分子的热力学和动力学结合参数以及 HIV-1 转录激活反应（TAR）RNA 模型系统。使用表面等离子体共振（SPR）对带有不同骨架的小分子进行 TAR 筛选。多元线性回归（MLR）结合特征选择提供了稳健的模型，可直接解释对结合强度和动力学速率常数都至关重要的性质。通过用新分子进行验证，并通过与集合树方法进行比较来确认其准确性能，证明了该平台的普遍适用性。
