Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital in Olomouc, Hnevotinska 5, 77900 Olomouc, Czech Republic.
A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kremlyovskaya Str. 18, 420008 Kazan, Russia.
Int J Mol Sci. 2019 Nov 20;20(23):5834. doi: 10.3390/ijms20235834.
Pharmacophore models are widely used for the identification of promising primary hits in compound large libraries. Recent studies have demonstrated that pharmacophores retrieved from protein-ligand molecular dynamic trajectories outperform pharmacophores retrieved from a single crystal complex structure. However, the number of retrieved pharmacophores can be enormous, thus, making it computationally inefficient to use all of them for virtual screening. In this study, we proposed selection of distinct representative pharmacophores by the removal of pharmacophores with identical three-dimensional (3D) pharmacophore hashes. We also proposed a new conformer coverage approach in order to rank compounds using all representative pharmacophores. Our results for four cyclin-dependent kinase 2 (CDK2) complexes with different ligands demonstrated that the proposed selection and ranking approaches outperformed the previously described common hits approach. We also demonstrated that ranking, based on averaged predicted scores obtained from different complexes, can outperform ranking based on scores from an individual complex. All developments were implemented in open-source software pharmd.
药效团模型广泛用于鉴定化合物大库中的有前途的原始命中。最近的研究表明，从蛋白质-配体分子动力学轨迹中检索到的药效团优于从单个晶体复合物结构中检索到的药效团。然而，检索到的药效团数量可能非常庞大，因此，使用所有这些药效团进行虚拟筛选在计算上效率低下。在这项研究中，我们提出通过去除具有相同三维（3D）药效团哈希值的药效团来选择独特的代表性药效团。我们还提出了一种新的构象覆盖方法，以便使用所有代表性药效团对化合物进行排名。我们对四个具有不同配体的细胞周期蛋白依赖性激酶 2 (CDK2) 复合物的结果表明，所提出的选择和排名方法优于先前描述的常见命中方法。我们还表明，基于从不同复合物获得的平均预测分数进行的排名可以优于基于单个复合物的分数进行的排名。所有开发均在开源软件 pharmd 中实现。
