Structure-activity relationships for a collection of structurally diverse inhibitors of purine nucleoside phosphorylase

Values of inhibition constants, Ki, and concentrations required for 50% inhibition, IC50, for a collection of structurally diverse competitive inhibitors of calf spleen purine nucleoside phosphorylase have been determined employing inosine as substrate. These values have been employed to create predictive quantitative structure-activity relationships (QSAR) which link structure to values of Ki and IC50. These QSAR models have substantial power to predict values and the associated uncertainties for Ki and IC50 for unknown, structurally diverse inhibitors of purine nucleoside phosphorylase.     

基于分子对接和QSAR方法预测B-Raf II型抑制剂活性

B-Raf激酶在促分裂素原活化蛋白激酶(MAPK)信号转导通路中起着重要作用,已被确定为癌症治疗非常有吸引力的靶标.新型高效B-Raf抑制剂的开发成为癌症治疗的一个热门研究领域.本文以结构多样的B-Raf II型抑制剂为研究对象,联合应用分子对接和定量构效关系(QSAR)模型研究其定量构效关系去探讨抑制活性的起源.两个主题作为研究重点:生物活性构象和描述符.首先对分子对接方法(Glide、Gold、LigandFit、Cdocker和Libdock)进行准确性评价,后将研究的对象分子对接到B-Raf活性位点并获得生物活性构象.基于准确的对接结果,计算得到16个打分评价函数和21个能量描述符,以此构建定量构效关系模型. QSAR结果表明模型具有高度精确的拟合和强的预测能力(模型M1: r² = 0.852, r_(CV)² = 0.790, r_pre² = 0.864;模型M2: r² = 0.738, r_(CV)² = 0.812, r_pre² = 0.8605).同时探讨了对抑制活性有重要影响的描述符,结果表明打分评价函数(G_Score, -ECD, Dock_Score, PMF)与能量描述符(S(hb_ext), DE(int), Emodel)对抑制活性影响非常大.通过虚拟筛选和QSAR模型理论预测,一些新的具有潜在抑制活性的化合物作为B-Raf II型抑制剂被获得.上述信息对于进一步设计新颖高效的B-Raf II型抑制剂提供了有用的指导.     

Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) modelling using comparative molecular similarity indices analysis (CoMSIA) was applied to a series of 406 structurally diverse dihydrofolate reductase (DHFR) inhibitors from Pneumocystis carinii (pc) and rat liver (rl). X-ray crystal structures of three inhibitors bound to pcDHFR were used for defining the alignment rule. For pcDHFR, a QSAR model containing 6 components was selected using leave-10%-out cross-validation (n= 240, q2 = 0.65), while a 4-component model was selected for rlDHFR (n= 237, q2 = 0.63); both include steric, electrostatic and hydrophobic contributions. The models were validated using a large test set, designed to maximise its diversity and to verify the predictive accuracy of models for extrapolation. The pcDHFR model has r2 = 0.60 and mean absolute error (MAE) = 0.57 for the test set after removing 4 outliers, and the rlDHFR model has r2 = 0.60 and MAE = 0.69 after removing 4 test set outliers. In addition, classification models predicting selectivity for pcDHFR over rlDHFR were developed using soft independent modelling by class analogy (SIMCA), with a selectivity ratio of 2 (IC50,rlDHFR/ IC50,pcDHFR) used for delimiting classes. A 5-component model including steric and electrostatic contributions has cross-validated and test set classification rates of 0.67 and 0.68 for selective inhibitors, and 0.85 and 0.72 for unselective inhibitors. The predictive accuracy of models, together with the identification of important contributions in QSAR and classification models, offer the possibility of designing potent selective inhibitors and estimating their activity prior to synthesis.