Homology modeling and 3D-QSAR study of benzhydrylpiperazine δ opioid receptor agonists
Homology modeling and 3D-QSAR study of benzhydrylpiperazine δ opioid receptor agonists
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DOI：
10.1016/j.compbiolchem.2019.107109
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发表时间：
2019-12-01
影响因子：
3.1
通讯作者：
Chang, Kwen-Jen
中科院分区：
文献类型：
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作者：
Pan, Chenling;Meng, Hao;Chang, Kwen-Jen
采用三维定量构效关系（3D-QSAR）和同源模建/分子对接（homologous modeling/molecular docking）方法对一系列二苯甲基哌嗪δ阿片受体激动剂的结合亲和力进行了评价。以活性最高的化合物BW 373 U86为模板，对46个化合物进行了配体CoMFA和CoMSIA 3D-QSAR分析。CoMFA的q（2）值为0.508，r（2）值为0.964; CoMSIA的q（2）值为0.530，r（2）值为0.927。在R-pred（2）值分别为0.720和0.814的测试集上验证了两个模型的预测能力。在这种情况下，CoMSIA模型似乎更有效。以已知的μ阿片受体活性晶体结构为模板，利用Swiss-Model建立了8阿片受体活性形式的同源模型，并利用纳秒级分子动力学模拟对其进行了优化。将活性最高的化合物BW 373 U86对接到8阿片受体的活性位点，然后使用最低能量结合位姿来鉴定结合残基，例如s Gln 105、Lys 108、Leu 125、Asp 128、Tyr 129、Leu 200、Met 132、Met 199、Lys 214、Trp 274、Ile 277、Ile 304和Tyr 308。对接和3D-QSAR结果表明，氢键和疏水相互作用在配体-受体相互作用中起主要作用。我们的研究结果表明，基于结构的同源建模/分子对接和基于配体的3D-QSAR方法相结合的方法可能是有用的，在设计新的阿片受体激动剂。
The binding affinity of a series of benzhydrylpiperazine delta opioid receptor agonists were pooled and evaluated by using 3D-QSAR and homology modeling/molecular docking methods. Ligand-based CoMFA and CoMSIA 3D-QSAR analyses with 46 compounds were performed on benzhydrylpiperazine analogues by taking the most active compound BW373U86 as the template. The models were generated successfully with q(2) value of 0.508 and r(2) value of 0.964 for CoMFA, and q(2) value of 0.530 and r(2) value of 0.927 for CoMSIA. The predictive capabilities of the two models were validated on the test set with R-pred(2) value of 0.720 and 0.814, respectively. The CoMSIA model appeared to work better in this case. A homology model of active form of 8 opioid receptor was established by Swiss-Model using a reported crystal structure of active mu opioid receptor as a template, and was further optimized using nanosecond scale molecular dynamics simulation. The most active compound BW373U86 was docked to the active site of 8 opioid receptor and the lowest energy binding pose was then used to identify binding residues such as s Gln105, Lys108, Leu125, Asp128, Tyr129, Leu200, Met132, Met199, Lys214, Trp274, Ile277, Ile304 and Tyr308. The docking and 3D-QSAR results showed that hydrogen bond and hydrophobic interactions played major roles in ligand-receptor interactions. Our results highlight that an approach combining structure-based homology modeling/molecular docking and ligand-based 3D-QSAR methods could be useful in designing of new opioid receptor agonists.