Kumar Sivakumar Prasanth, Jha Prakash Chandra
School of Chemical Sciences, Central University of Gujarat, Gandhinagar - 382030, Gujarat, India.
Comb Chem High Throughput Screen. 2018;21(1):26-40. doi: 10.2174/1386207321666180102114917.
Numerous caspase-3 drug discovery projects were found to have relied on single receptor as the template to recognize most promising small molecule candidates using docking approach. Alternatively, some researchers were contingent upon ligand-based alignment to build up an empirical relationship between ligand functional groups and caspase-3 inhibitory activity quantitatively. To connect both caspase-3 receptor details and its inhibitors chemical functionalities, this study was undertaken to develop receptor- and ligand-pharmacophore models based on different conformational schemes.
A multi-pharmacophore modeling strategy is carried out based on three conformational schemes of pharmacophore hypothesis generation to screen caspase-3 inhibitors from database. The schemes include (i) flexible (conformations unrestricted or flexible during pharmacophore mapping), (ii) dock (conformations obtained using FlexX docking method) and (iii) crystal (extracted from multiple caspase-3-ligand complexes from PDB repository) conformations of query ligands. The pharmacophore models developed using these conformational schemes were then used to identify probable caspase-3 inhibitors from ZINC database.
We noticed better sensitivity with good specificity measures returned by candidate pharmacophore hypotheses across each conformation type and recognized crucial pharmacophore features that enable caspase-3 binding. Pharmacophore modeling based on flexible conformational scheme indicated that the crystal structure 3KJF (AAAADH) is the best receptor structure to perform receptor-based pharmacophore screening of caspase-3 inhibitors. When multiple crystal structures were included, the hypothesis (HAAA) is more generalized. Superimposition of multiple co-crystal ligands from various caspase-3 PDB entries in crystallographic binding mode revealed similar hypothesis (HAAA). Further, FlexX-guided dock conformations of validation dataset showed that the crystal structure 1RE1 is the best-suited for dock-based pharmacophore models. Database screening using these pharmacophore hypotheses identified N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4- yl]benzohydrazide as the probable caspase-3 inhibitors.
N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4-yl]benzohydrazide may be tested for caspase-3 inhibition. We believe that potential caspase-3 inhibitors can be recognized efficiently by adapting multi-pharmacophore models in database screening.
众多的半胱天冬酶 - 3药物发现项目依赖单一受体作为模板,使用对接方法识别最有前景的小分子候选物。另外,一些研究人员依靠基于配体的比对来定量建立配体官能团与半胱天冬酶 - 3抑制活性之间的经验关系。为了将半胱天冬酶 - 3受体细节与其抑制剂的化学功能联系起来,本研究基于不同的构象方案开发受体和配体药效团模型。
基于药效团假设生成的三种构象方案开展多药效团建模策略,从数据库中筛选半胱天冬酶 - 3抑制剂。这些方案包括:(i)灵活(在药效团映射期间构象不受限制或灵活)、(ii)对接(使用FlexX对接方法获得的构象)和(iii)晶体(从PDB库中的多个半胱天冬酶 - 3 - 配体复合物中提取)查询配体的构象。然后使用这些构象方案开发的药效团模型从ZINC数据库中识别可能的半胱天冬酶 - 3抑制剂。
我们注意到,在每种构象类型中,候选药效团假设返回了具有良好特异性指标的更高灵敏度,并识别出了使半胱天冬酶 - 3结合的关键药效团特征。基于灵活构象方案的药效团建模表明,晶体结构3KJF(AAAADH)是对半胱天冬酶 - 3抑制剂进行基于受体的药效团筛选的最佳受体结构。当纳入多个晶体结构时,假设(HAAA)更具通用性。在晶体学结合模式下,来自各种半胱天冬酶 - 3 PDB条目的多个共晶体配体的叠加显示出相似的假设(HAAA)。此外,验证数据集的FlexX引导对接构象表明,晶体结构1RE1最适合基于对接的药效团模型。使用这些药效团假设进行数据库筛选,确定N'-[6 - (苯并咪唑 - 1 - 基) - 5 - 硝基 - 嘧啶 - 4 - 基] - 4 - 甲基苯磺酰肼和2 - 硝基 - N'-[5 - 硝基 - 6 - [N'-(对甲苯磺酰基)肼基] - 嘧啶 - 4 - 基]苯甲酰肼为可能的半胱天冬酶 - 3抑制剂。
可测试N'-[6 - (苯并咪唑 - 1 - 基) - 5 - 硝基 - 嘧啶 - 4 - 基] - 4 - 甲基苯磺酰肼和2 - 硝基 - N'-[5 - 硝基 - 6 - [N'-(对甲苯磺酰基)肼基] - 嘧啶 - 4 - 基]苯甲酰肼对半胱天冬酶 - 3的抑制作用。我们相信,通过在数据库筛选中采用多药效团模型,可以有效地识别潜在的半胱天冬酶 - 3抑制剂。