Lewis Stephanie N, Garcia Zulma, Hontecillas Raquel, Bassaganya-Riera Josep, Bevan David R
Genetics, Bioinformatics, and Computational Biology Program, Virginia Tech, Blacksburg, VA, USA,
J Comput Aided Mol Des. 2015 May;29(5):421-39. doi: 10.1007/s10822-015-9831-x. Epub 2015 Jan 24.
Peroxisome proliferator-activated receptor-gamma (PPARγ) is a nuclear hormone receptor involved in regulating various metabolic and immune processes. The PPAR family of receptors possesses a large binding cavity that imparts promiscuity of ligand binding not common to other nuclear receptors. This feature increases the challenge of using computational methods to identify PPAR ligands that will dock favorably into a structural model. Utilizing both ligand- and structure-based pharmacophore methods, we sought to improve agonist prediction by grouping ligands according to pharmacophore features, and pairing models derived from these features with receptor structures for docking. For 22 of the 33 receptor structures evaluated we observed an increase in true positive rate (TPR) when screening was restricted to compounds sharing molecular features found in rosiglitazone. A combination of structure models used for docking resulted in a higher TPR (40 %) when compared to docking with a single structure model (<20 %). Prediction was also improved when specific protein-ligand interactions between the docked ligands and structure models were given greater weight than the calculated free energy of binding. A large-scale screen of compounds using a marketed drug database verified the predictive ability of the selected structure models. This study highlights the steps necessary to improve screening for PPARγ ligands using multiple structure models, ligand-based pharmacophore data, evaluation of protein-ligand interactions, and comparison of docking datasets. The unique combination of methods presented here holds potential for more efficient screening of compounds with unknown affinity for PPARγ that could serve as candidates for therapeutic development.
过氧化物酶体增殖物激活受体γ（PPARγ）是一种核激素受体，参与调节各种代谢和免疫过程。PPAR受体家族具有一个大的结合腔，赋予了与其他核受体不同的配体结合多特异性。这一特性增加了使用计算方法识别能良好对接至结构模型的PPAR配体的挑战。利用基于配体和基于结构的药效团方法，我们试图通过根据药效团特征对配体进行分组，并将从这些特征衍生的模型与受体结构进行对接配对来改进激动剂预测。在评估的33个受体结构中的22个中，当筛选仅限于具有罗格列酮中发现的分子特征的化合物时，我们观察到真阳性率（TPR）有所增加。与使用单一结构模型进行对接（<20%）相比，用于对接的结构模型组合导致更高的TPR（40%）。当对接配体与结构模型之间的特定蛋白质-配体相互作用比计算出的结合自由能赋予更大权重时，预测也得到了改善。使用市售药物数据库对化合物进行大规模筛选验证了所选结构模型的预测能力。本研究强调了使用多个结构模型、基于配体的药效团数据、评估蛋白质-配体相互作用以及比较对接数据集来改进PPARγ配体筛选所需的步骤。本文提出的独特方法组合具有更高效筛选对PPARγ具有未知亲和力的化合物的潜力，这些化合物可作为治疗开发的候选物。
