Çifci Gülşah, Aviyente Viktorya, Akten E Demet
Department of Chemistry, Boğaziçi University, 34342 Bebek, Istanbul.
Department of Information Technologies, Kadir Has University 34083, Cibali, Istanbul.
Mol Inform. 2012 Jul;31(6-7):459-71. doi: 10.1002/minf.201100141. Epub 2012 Jul 11.
In this study, pharmacophore modelling was carried out for novel PhosphodiesteraseIV (PDEIV) inhibitors. A pharmacophore-based virtual screening, which resulted in 1959 hit compounds was performed with six chemical databases. The pharmacophore screening was proven to be successful in discriminating active and inactive inhibitors using a set of compounds with known activity obtained from ChEMBL database. Furthermore, the Lipinski's rule of five was applied for physicochemical filtering of the hit molecules and this yielded 1840 compounds. Three docking software tools, AutoDock 4.0, AutoDock Vina, and Gold v5.1 were used for the docking process. All 1840 compounds and the known selective inhibitor, rolipram, were docked into the active site of the target protein. A total of 234 compounds with all three scoring values higher than those of rolipram were determined with the three docking tools. The interaction maps of 14 potent inhibitors complexed with PDEIV B and D isoforms have been analyzed and seven key residues (Asn 395, Gln 443, Tyr 233, Ile 410, Phe 446, Asp 392, Thr 407) were found to interact with more than 80 % of the potent inhibitors. For each one of the 234 hit compounds, using the bound conformation with the highest AutoDock score, the interacting residues were determined. 117 out of 234 compounds are found to interact with at least five of the seven key residues and these were selected for further evaluation. The conformation with the highest AutoDock score for each 117 compounds were rescored using the DSX scoring function. This yielded a total of 101 compounds with better score values than the natural ligand rolipram. For ADME/TOX calculations, the FAF-Drugs2 server was used and 32 out of 101 compounds were found to be non-toxic.
在本研究中,对新型磷酸二酯酶IV(PDEIV)抑制剂进行了药效团建模。利用六个化学数据库进行了基于药效团的虚拟筛选,得到了1959个命中化合物。通过使用从ChEMBL数据库获得的一组具有已知活性的化合物,药效团筛选被证明能够成功区分活性和非活性抑制剂。此外,应用Lipinski的五规则对命中分子进行物理化学筛选,得到了1840个化合物。使用三种对接软件工具AutoDock 4.0、AutoDock Vina和Gold v5.1进行对接过程。将所有1840个化合物和已知的选择性抑制剂咯利普兰对接至靶蛋白的活性位点。使用这三种对接工具确定了总共234个化合物,其所有三个评分值均高于咯利普兰。分析了与PDEIV B和D亚型复合的14种强效抑制剂的相互作用图谱,发现七个关键残基(Asn 395、Gln 443、Tyr 233、Ile 410、Phe 446、Asp 392、Thr 407)与80%以上的强效抑制剂相互作用。对于234个命中化合物中的每一个,使用具有最高AutoDock评分的结合构象,确定相互作用的残基。发现234个化合物中有117个与七个关键残基中的至少五个相互作用,并选择这些化合物进行进一步评估。使用DSX评分函数对117个化合物中每个化合物具有最高AutoDock评分的构象进行重新评分。这产生了总共101个化合物,其评分值优于天然配体咯利普兰。对于ADME/TOX计算,使用了FAF-Drugs2服务器,发现101个化合物中有32个无毒。