Wang Zhanhui, Kai Zhenpeng, Beier Ross C, Shen Jianzhong, Yang Xinling
College of Veterinary Medicine, China Agricultural University, Beijing 100094, China.
College of Science, China Agricultural University, Beijing 100094, China.
Int J Mol Sci. 2012;13(5):6334-6351. doi: 10.3390/ijms13056334. Epub 2012 May 23.
A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs binding a monoclonal antibody (MAb(SMR)) produced against sulfamerazine was carried out by Distance Comparison (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA). The affinities of the MAb(SMR), expressed as Log(10)IC(50), for 17 sulfonamide analogs were determined by competitive fluorescence polarization immunoassay (FPIA). The results demonstrated that the proposed pharmacophore model containing two hydrogen-bond acceptors, two hydrogen-bond donors and two hydrophobic centers characterized the structural features of the sulfonamides necessary for MAb(SMR) binding. Removal of two outliers from the initial set of 17 sulfonamide analogs improved the predictability of the models. The 3D-QSAR models of 15 sulfonamides based on CoMFA and CoMSIA resulted in q(2) (cv) values of 0.600 and 0.523, and r(2) values of 0.995 and 0.994, respectively, which indicates that both methods have significant predictive capability. Connolly surface analysis, which mainly focused on steric force fields, was performed to complement the results from CoMFA and CoMSIA. This novel study combining FPIA with pharmacophore modeling demonstrates that multidisciplinary research is useful for investigating antigen-antibody interactions and also may provide information required for the design of new haptens.
通过距离比较法（DISCOtech）、比较分子场分析（CoMFA）和比较分子相似性指数分析（CoMSIA），构建了针对磺胺甲基嘧啶产生的单克隆抗体（MAb(SMR)）的磺胺类类似物的三维定量构效关系（3D-QSAR）模型。通过竞争性荧光偏振免疫测定法（FPIA）测定了MAb(SMR)对17种磺胺类类似物的亲和力，以Log(10)IC(50)表示。结果表明，所提出的药效团模型包含两个氢键受体、两个氢键供体和两个疏水中心，表征了MAb(SMR)结合所需的磺胺类药物的结构特征。从最初的17种磺胺类类似物中去除两个离群值提高了模型的预测能力。基于CoMFA和CoMSIA的15种磺胺类药物的3D-QSAR模型的交叉验证q(2)（cv）值分别为0.600和0.523，r(2)值分别为0.995和0.994，这表明两种方法都具有显著的预测能力。进行了主要关注空间力场的康诺利表面分析，以补充CoMFA和CoMSIA的结果。这项将FPIA与药效团建模相结合的新研究表明，多学科研究有助于研究抗原-抗体相互作用，也可能为新半抗原的设计提供所需信息。
