拮抗状态下α1A, α1B和α1D-肾上腺素能受体的分子模拟研究

采用同源建模法对α1A-,α1B-和α1D-AR的三维结构进行了模拟,并采用分子力学、分子动力学方法对所得同源模型进行优化,然后分别采用训练集拮抗剂对接的方法得到拮抗状态下的α1A-,α1B-和α1D-AR三维结构模型.得到的模型再采用FRED对接软件对测试集中的18个化合物进行对接并打分,再将所得打分结果与其活性进行线性回归,其回归结果具有良好的拟合效果,由此回归方程预测的活性与化合物实验值较吻合,说明我们建立的拮抗状态下的α1A-,α1B-和α1D-AR的三维同源模型具有一定的合理性,可作为化合物虚拟筛选模型,对新化合物进行对接虚拟筛选.     

拮抗状态下α1A,α1B和α1D-肾上腺素能受体的分子模拟研究

采用同源建模法对α1A-,α1B-和α1D-AR的三维结构进行了模拟,并采用分子力学、分子动力学方法对所得同源模型进行优化,然后分别采用训练集拮抗剂对接的方法得到拮抗状态下的α1A-,α1B-和α1D-AR三维结构模型.得到的模型再采用FRED对接软件对测试集中的18个化合物进行对接并打分,再将所得打分结果与其活性进行线性回归,其回归结果具有良好的拟合效果,由此回归方程预测的活性与化合物实验值较吻合,说明我们建立的拮抗状态下的α1A-,α1B-和α1D-AR的三维同源模型具有一定的合理性,可作为化合物虚拟筛选模型,对新化合物进行对接虚拟筛选.     

毒蕈碱型M1受体的同源模建和分子对接

采用同源模建方法对M1受体的三维结构进行了模拟, 将得到的模型分别与M受体完全激动剂乙酰胆碱和M1受体选择性激动剂占诺美林进行分子对接, 形成非特异性激动和特异性激动的受体-配体复合物. 用分子动力学模拟方法分别将未与小分子对接的M1受体、M1受体-乙酰胆碱复合物、M1受体-占诺美林复合物置于磷脂双膜中模拟10 ns. 将模拟后的蛋白质结构与包含活性分子的测试库对接并将结果打分, 以top5%富集因子(EF)作为评价依据, 用占诺美林优化后的M1受体模型的EF为8.0, 用乙酰胆碱优化后M1受体模型的EF为6.5, 非复合物的EF为1.5. 说明M1受体选择性激动剂复合物进行分子动力学模拟后得到的三维结构模型比较合理, 可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选, 为找到新的选择性M1受体激动剂奠定了基础.     

毒蕈碱型M1受体的同源模建和分子对接

采用同源模建方法对M1受体的三维结构进行了模拟,将得到的模型分别与M受体完全激动剂乙酰胆碱和M1受体选择性激动剂占诺美林进行分子对接,形成非特异性激动和特异性激动的受体-配体复合物.用分子动力学模拟方法分别将未与小分子对接的M1受体、M1受体-乙酰且H碱复合物、M1受体-占诺美林复合物置于磷脂双膜中模拟10 ns.将模拟后的蛋白质结构与包含活性分子的测试库对接并将结果打分,以top5%富集因子(EF)作为评价依据,用占诺美林优化后的M1受体模型的EF为8.0,用乙酰胆碱优化后M1受体模型的EF为6.5,非复合物的EF为1.5.说明M1受体选择性激动剂复合物进行分子动力学模拟后得到的三维结构模型比较合理,可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选,为找到新的选择性M1受体激动剂奠定了基础.     

拮抗状态下α1A1，α1B和α1D-肾上腺素能受体的分子模拟研究

采用同源建模法对α1A1，α1B和α1D-的三维结构进行了模拟，并采用分子力学、分子动力学方法对所得同源模型进行优化，然后分别采用训练集拮抗剂对接的方法得到拮抗状态下的α1A1，α1B和α1D-三维结构模型．得到的模型再采用FRED对接软件对测试集中的18个化合物进行对接并打分，再将所得打分结果与其活性进行线性回归，其回归结果具有良好的拟合效果，由此回归方程预测的活性与化合物实验值较吻合，说明我们建立的拮抗状态下的α1A1，α1B和α1D-的三维同源模型具有一定的合理性，可作为化合物虚拟筛选模型，对新化合物进行对接虚拟筛选．     

人类腺苷受体A3亚型拮抗剂的构效关系分析

构建人类腺苷受体A3亚型药效团模型和三维蛋白结构模型用于作用模式研究.以18个来源于文献具有腺苷受体A3亚型拮抗活性的化合物作为训练集,使用HypoGen方法构建药效团模型.通过同源模建和分子动力学模拟构建了人类腺苷受体A3亚型的三维蛋白模型,并利用PROCHECK方法评估该模型的合理性,对所得的结构使用分子对接程序进行作用模式分析,药效团模型和同源模建结果相互匹配较好.使用新药效团模型对MDL药物数据库(MDDR)中包含的约120000个化合物进行虚拟筛选,得到了8个候选化合物,用于进一步的生物学评价和活性测定.本工作对于人类腺苷受体A3亚型拮抗剂的设计和抗哮喘药物的研发具有一定的理论指导和应用价值.     

A_1腺苷受体的同源模建及其结构验证

采用同源模建的方法构建了A1腺苷受体的三维结构,并与拮抗剂分子DPCPX对接,将得到的复合物结构进行5 ns的分子动力学模拟,以最后2 ns的平均结构和平衡后抽取的11帧构象共12个蛋白结构为研究对象,用包含52个活性分子和1000个诱饵分子的测试库,分别通过DOCK、VINA和GOLD三种对接软件进行评价,最终得出合理的蛋白质模型.根据top10%的富集因子(EF)和ROC曲线下面积(AU-ROC)的计算结果,我们认为GOLD是最适合A1腺苷受体的对接软件,而12个蛋白质结构中F5和Favg的三维结构模型比较合理,可以作为进一步大规模虚拟筛选的模型.     

基于分子对接的苯丙素甙（PPGs）类化合物的虚拟筛选和合理设计

采用虚拟化合物生成法对抗肿瘤的苯丙素甙(PPGs)类化合物进行了配体受体对 接研究。以三种不同的骨架结构为基础分别生成了五十个虚拟苯丙素甙(PPGs)类化 合物，并将它们与端粒DNA受体进行分子对接，分析已知结构的对接结果，通过虚 拟筛选的方法得到了一批与受体相互作用能较高并且复合物能量较低的新的有潜力 的活性化合物。该方法可以弥补分子对接研究中，只能计算药物与受体的相互作用 ，无法有效设计新化合物的不足。这种方法在基于结构的药物分子设计中具有重要 的意义。     

PPAR激动剂的定向设计、虚拟筛选及合成

过氧化物酶体增殖因子活化受体(PPAR)是核受体超家族的一员.基于受体结构的药物分子设计与组合化学策略相结合,构建了过氧化物酶体增殖因子活化受体(PPAR)激动剂的虚拟化合物库.将已知小分子配体(GW409544)与PPAR晶体复合物进行剥离,得到受体的活性构象,并利用此活性受体分子与虚拟库中小分子进行对接和虚拟筛选,得到理论上结合较强的化合物,并对这些化合物进行合成,共合成9个新化合物.活性筛选结果显示化合物对PPAR具有一定的亲和力,其中有三个化合物显示出对PPARα,PPARγ的双重激动作用,从而指导新活性化合物的设计和合成.     

基于他克林的乙酰胆碱酯酶抑制剂的3D-QSAR研究及虚拟筛选

本文通过对58个他克林派生物乙酰胆碱酯酶抑制剂分子进行建模分析,研究其结构与活性的关系,并通过虚拟筛选方法获得一系列潜在AChE抑制剂双位点分子。首先将一系列他克林二联体化合物与AChE晶体结构对接,获得化合物的活性构象,以此进行建模分析,建立结构与活性之间的三维定量构效关系。所得模型CoMFA、CoMSIA、TopomerCoMFA的交叉验证系数分别为0.510、0.702、0.571,非交叉验证系数为0.998、0.988、0.794,测试集r_(pred)~2为0.750、0.742、0.766,所得模型具有良好的预测性,由此可以为设计高活性的新分子提供理论基础。然后,使用Topomer search对ZINC数据库中的125909分子进行虚拟筛选,得到891个具有潜在AChE抑制活性的分子。最后,对这891个分子进行分子对接,观察分子与晶体结构的结合情况,筛选得到66个具有高选择性的双位点AChE抑制剂分子。     

Molecular dynamics simulations of the ligand-induced chemical information transfer in the 5-HT(1A) receptor

Comparative molecular dynamics simulations of the 5-HT(1A) receptor in its empty as well as agonist- (i.e. active) and antagonist-bound (i.e. nonactive) forms have been carried out. The agonists 5-HT and (R)-8-OH-DPAT as well as the antagonist WAY100635 have been employed. The results of this study strengthen the hypothesis that the receptor portions close to the E/DRY/W motif, with prominence to the cytosolic extensions of helices 3 and 6, are particularly susceptible to undergo structural modification in response to agonist binding. Despite the differences in the structural/dynamics behavior of the two agonists when docked into the 5-HT(1A) receptor, they both exert a destabilization of the intrahelical and interhelical interactions found in the empty and antagonist-bound receptor forms between the arginine of the E/DRY sequence and both D133(3.49) and E340(6.30). For both agonists, the chemical information transfer from the extracellular to the cytosolic domains is mediated by a cluster of aromatic amino acids in helix 6, following the ligand interaction with selected amino acids in the extracellular half of the receptor, such as D116(3.32), S199(5.42), Y195(5.38), and F361(6.51). A significant reduction in the bend at P360(6.50), as compared to the empty and the antagonist-bound receptor forms, is one of the features of the agonist-bound forms that is related to the breakage of the interhelical salt bridge between the E/DRY arginine and E340(6.30). Another structural feature, shared by the agonist-bound receptor forms and not by the empty and antagonist-bound forms, is the detachment of helices 2 and 4, as marked by the movement of W161(4.50) away from helix 2, toward the membrane space.     

Prediction of ligand binding affinity using a multiple-conformations-multiple-protonation scheme: application to estrogen receptor α

A fast method that can predict the binding affinities of chemicals to a target protein with a high degree of accuracy will be very useful in drug design and regulatory science. We have been developing a scoring function for affinity prediction, which can be applied to extensive protein systems, and also trying to generate a prediction scheme that specializes in each target protein, with as high a predictive power as possible. In this study, we have constructed a prediction scheme with target-specific scores for estimating ligand-binding affinities to human estrogen receptor α (ERα), considering the major conformational change between agonist- and antagonist-bound forms and the change in protonation states of histidine at the ligand-binding site. The generated scheme calibrated with fewer training compounds (23 for the agonist-bound form, 17 for the antagonist-bound form) demonstrated good predictive power (a predictive r(2) of 0.83 for 154 validation compounds); this was also true for compounds with frameworks that were quite different from those of the training compounds. Our prediction scheme will be useful in drug development targeting ERα and in primary screening of endocrine disruptors, and provides a successful method of affinity prediction considering the major conformational changes in a protein.     

Pharmacophore Mode lingand Virtual Screening to Design the Potential Influenza Virus Endonuclease Inhibitors

Influenza virus endonuclease is an attractive target for antiviral therapy in the treatment of influenza infection. The purpos e of this study is to design a novel antiviral agent with improved biological activities against the influenza virus endonuclease. In this study, chemical feature‐based 3D pharmacophore models were developed from 41 known influenza virus endonuclease inhibitors. The best quantitative pharmacohore model (Hypo 1), which consists of two hydrogen‐bond acceptors and two hydrophobic features, yields the highest correlation coefficient (R = 0.886). Hypo 1 was further validated by the cross validation method and the test set compounds. The application of this model for predicting the activities of 11 known influenza virus endonuclease inhibitors in the test set shows great success. The correlation coefficient of 0.942 and a cross validation of 95;% confidence level prove that this model is reliable in identifying structurally diverse compounds for influenza virus endonuclease inhibition. The most active compound (compound 1) from the training set was docked into the active site of the influenza virus endonuclease as an additional verification that the pharmacophore model is accurate. The docked conformation showed important hydrogen bond interactions between the compound and two amino acids, Lys 134 and Lys 137. After validation, this model was used to screen the NCI chemical database to identify new influenza virus endonuclease inhibitors. Our study shows that the to pranking compound out of the 10 newly identified compounds using fit value ranking has an estimated activity of 0.049 μM. These newly identified lead compounds can be further experimentally validated using in vitro techniques.     

A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A,a selective kappa opioid receptor agonist

Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based “agonist-bound” hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental pK _i values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

Structure-based QSAR analysis of a set of 4-hydroxy-5,6-dihydropyrones as inhibitors of HIV-1 protease: an application of the receptor-dependent (RD) 4D-QSAR formalism

Receptor-dependent (RD) 4D-QSAR models were constructed for a set of 39 4-hydroxy-5,6-dihydropyrone analogue HIV-1 protease inhibitors. The receptor model used in this QSAR analysis was derived from the HIV-1 protease (PDB ID ) crystal structure. The bound ligand in the active site of the enzyme, also a 4-hydroxy-5,6-dihydropyrone analogue, was used as the reference ligand for docking the data set compounds. The optimized RD 4D-QSAR models are not only statistically significant (r(2) = 0.86, q(2) = 0.80 for four- and greater-term models) but also possess reasonable predictivity based on test set predictions. The proposed "active" conformations of the docked analogues in the active site of the enzyme are consistent in overall molecular shape with those suggested from crystallographic studies. Moreover, the RD 4D-QSAR models also "capture" the existence of specific induced-fit interactions between the enzyme active site and each specific inhibitor. Hydrophobic interactions, steric shape requirements, and hydrogen bonding of the 4-hydroxy-5,6-dihydropyrone analogues with the HIV-1 protease binding site model dominate the RD 4D-QSAR models in a manner again consistent with experimental conclusions. Some possible hypotheses for the development of new lead HIV-1 protease inhibitors can be inferred from the RD 4D-QSAR models.     

3D-QSAR and molecular modeling of HIV-1 integrase inhibitors

Three-dimensional quantitative structure-activity relationship (3D QSAR) methods were applied on a series of inhibitors of HIV-1 integrase with respect to their inhibition of 3-processing and 3-end joining steps in vitro.The training set consisted of 27 compounds belonging to the class of thiazolothiazepines. The predictive ability of each model was evaluated using test set I consisting of four thiazolothiazepines and test set II comprised of seven compounds belonging to an entirely different structural class of coumarins. Maximum Common Substructure (MCS) based method was used to align the molecules and this was compared with other known methods of alignment. Two methods of 3D QSAR: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were analyzed in terms of their predictive abilities. CoMSIA produced significantly better results for all correlations. The results indicate a strong correlation between the inhibitory activity of these compounds and the steric and electrostatic fields around them. CoMSIA models with considerable internal as well as external predictive ability were obtained. A poor correlation obtained with hydrophobic field indicates that the binding of thiazolothiazepines to HIV-1 integrase is mainly enthalpic in nature. Further the most active compound of the series was docked into the active site using the crystal structure of integrase. The binding site was formed by the amino acid residues 64-67, 116, 148, 151-152, 155-156, and 159. The comparison of coefficient contour maps with the steric and electrostatic properties of the receptor shows high level of compatibility.     

Phosphoinositide-3-kinases (PI3Ks): combined comparative modeling and 3D-QSAR to rationalize the inhibition of p110alpha

The p110alpha isoform of the class IA PI3Ks was recently genetically validated as a promising target for anticancer therapy. However, up to now, only one compound (PIK75 = 1) has been reported as a very potent and selective inhibitor of this isoform. The lack of a 3D structure for this enzyme has clearly hindered the discovery of new p110alpha selective compounds. In view of this, we combined target-based (homology modeling) and ligand-based (3D-QSAR) approaches in an attempt to define an integrated interaction model for p110alpha inhibition. Twenty-five analogues of 1 were docked within the putative p110alpha binding site, and the molecular alignment generated was subsequently used to derive QSAR models based on scoring function, free energy of binding, CoMFA. and CoMSIA. The predictive power of these models was then analyzed using a challenging test set of 5 compounds. CoMSIA, and particularly CoMFA, models were found to outperform the other methods, predicting accurately the potency of 100% of the compounds in the test set, thereby validating our p110alpha homology model for use in further drug development.