Kulkarni S S, Kulkarni V M
Department of Chemical Technology, Pharmaceutical Division, University of Mumbai, Matunga, Mumbai 400 019, India.
J Med Chem. 1999 Feb 11;42(3):373-80. doi: 10.1021/jm9708442.
A three-dimensional quantitative structure-activity relationship (QSAR) study using the comparative molecular field analysis (CoMFA) method was performed on a series of interleukin 1-beta converting enzyme (ICE) inhibitors. The compounds studied have been reported to be time-dependent inhibitors of ICE. This study was performed using 49 compounds, in which the CoMFA models were developed using a training set of 39 compounds. All the compounds were modeled using the X-ray crystal structure of tetrapeptide aldehyde inhibitor/ICE complex. The inhibitor compounds were considered both as neutral species and as P1 carboxylate ionized species. Superimpositions were performed using two alignment rules, namely, an alignment of the structures based on RMS fitting of the backbone heavy atoms of each structure to compound 2 and an alignment based on SYBYL QSAR rigid body field fit of the steric and electrostatic fields of the molecules to the fields of compound 2. Use of LUMO energies or ClogP as additional descriptors in the QSAR table did not improve the significance of the CoMFA models. Steric and electrostatic fields of the inhibitors were found to be the relevant descriptors for structure-activity relationships. The predictive ability of the CoMFA model was evaluated by using a test set of 10 compounds (r2pred as high as 0.859). Further comparison of the coefficient contour maps with the steric and electrostatic properties of the receptor show a high level of compatibility.
使用比较分子场分析(CoMFA)方法对一系列白细胞介素1-β转化酶(ICE)抑制剂进行了三维定量构效关系(QSAR)研究。所研究的化合物据报道是ICE的时间依赖性抑制剂。本研究使用了49种化合物,其中CoMFA模型是使用39种化合物的训练集开发的。所有化合物均使用四肽醛抑制剂/ICE复合物的X射线晶体结构进行建模。抑制剂化合物既被视为中性物种,也被视为P1羧酸盐离子化物种。使用两种比对规则进行叠加,即基于每个结构的主链重原子与化合物2的RMS拟合对结构进行比对,以及基于分子的空间和静电场与化合物2的场的SYBYL QSAR刚体场拟合进行比对。在QSAR表中使用最低未占分子轨道(LUMO)能量或辛醇/水分配系数(ClogP)作为额外描述符并未提高CoMFA模型的显著性。发现抑制剂的空间和静电场是构效关系的相关描述符。通过使用10种化合物的测试集评估CoMFA模型的预测能力(预测决定系数r2pred高达0.859)。系数等高线图与受体的空间和静电性质的进一步比较显示出高度的兼容性。