苯酰胺类组蛋白去乙酰化酶抑制剂的分子对接及三维定量构效关系研究

利用surflex-dock模块对59个苯酰胺类抑制剂和组蛋白去乙酰化酶进行了对接研究,分析了配体和受体的相互作用模式,所得结论与文献报道的实验结果符合。并利用比较分子力场分析方法(comparative molecular field anal-ysis,CoMFA)对此类抑制剂分子进行了三维定量构效关系研究,所建模型交叉验证相关系数q~2=0.640,非交叉验证相关系数r~2=0.932,有较好的预测能力。CoMFA得到的立体场和静电场的等值线图可用于指导新型药物的设计与合成。     

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses of choline acetyltransferase inhibitors

As a basis for predicting structural features that may lead to the design of more potent and selective inhibitors of choline acetyltransferase (ChAT), the three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were carried out on a series of trans-1-methyl-4-(1-naphthylvinyl)pyridinium (MNVP+) analogs, which are known ChAT inhibitors. 3D-QSAR studies were carried out using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Since these inhibitors have extremely shallow potential energy minimum energy wells and low barriers to rotation, two dihedral angles unique to these inhibitors were systematically modified to reflect the energetically preferred conformations as determined by force field calculations. An optimum alignment rule was devised based on the conformations obtained from the molecular mechanics studies, using a common substructure alignment method. The studies involve a set of 21 compounds and experimentally determined molar IC50 values were used as the dependent variable in the analysis. The 3D-QSAR models have conventional r2-values of 0.953 and 0.954 for CoMFA and CoMSIA, respectively; similarly, cross-validated coefficient q2-values of 0.755 and 0.834 for CoMFA and CoMSIA, respectively, were obtained. On the basis of these predictive r2-values the model was tested using previously determined IC50 values. CoMSIA 3D-QSAR yielded better results than CoMFA.     

Efficient parallel algorithms for solvent accessible surface areaof proteins

We present faster sequential and parallel algorithms for computing the solvent accessible surface area (ASA) of protein molecules. The ASA is computed by finding the exposed surface areas of the spheres obtained by increasing the van der Waals radii of the atoms with the van der Waals radius of the solvent. Using domain specific knowledge, we show that the number of sphere intersections is only O(n), where n is the number of atoms in the protein molecule. For computing sphere intersections, we present hash-based algorithms that run in O(n) expected sequential time and O(n/p) expected parallel time and sort-based algorithms that run in worst-case O(n log n) sequential time and O(n log n/p) parallel time. These are significant improvements over previously known algorithms which take O(n²) time sequentially and O(n²/p) time in parallel. We present a Monte Carlo algorithm for computing the solvent accessible surface area. The basic idea is to generate points uniformly at random on the surface of spheres obtained by increasing the van der Waals radii of the atoms with the van der Waals radius of the solvent molecule and to test the points for accessibility. We also provide error bounds as a function of the sample size. Experimental verification of the algorithms is carried out using an IBM SP-2     

黄酮类化合物的3D-QSAR研究

从NCI数据库中,筛选出67个与矢车菊黄素类似的天然黄酮化合物.采用CoMFA方法研究其构效关系,构建CoMFA模型,其模型相关系数为q2=0.599,r2=0.919,验证模型的预测能力和拟合能力较好.通过分子场等势图,可直观分子周围立体和静电特征对化合物活性的影响,为设计高活性黄酮衍生物提供理论依据.     

CoMFA analysis on a set of new noncompetitive GABAA receptor antagonists： fipronil and related analogs

Fipronil and related analogs, a set of new noncompetitive GABAA receptor antagonists, were investigated using comparative molecular field analysis （CoMFA） to explore their three-dimensional quantitative structure-activity relationships （3D-QSAR）. Considering the structural complexity of molecules of fipronil and related analogs, three different alignments were performed in this paper. CoMFA model for housefly receptor yield the leave-one-out and cross-validated correlation coefficient q^2 value of 0.511 and the conventional correlation coefficient r^2 value of 0.997. The new compounds with higher activity would be designed from this model. CoMFA model for rat receptor was not successful using all these three alignments, the reason of which maybe that some molecules adopt different conformations for rat receptor.     

两类促肾上腺皮质释放因子(CRF)抑制剂的CoMFA研究

Fipronil and related analogs, a set of new noncompetitive GABAA receptor antagonists, were investigated using comparative molecular field analysis (CoMFA) to explore their three-dimensional quantitative structure-activity relationships (3D-QSAR).Considering the structural complexity of molecules of fipronil and related analogs, three different alignments were performed in this paper. CoMFA model for housefly receptor yield the leave-one-out and cross-validated correlation coefficient q2 value of 0.511 and the conventional correlation coefficient r2 value of 0.997. The new compounds with higher activity would be designed from this model.CoMFA model for rat receptor was not successful using all these three alignments, the reason of which maybe that some molecules adopt different conformations for rat receptor.     

苯并呋喃类组胺H_3受体拮抗剂的构效关系研究

绢胺H3受体在体内参与调节很多神经递质的释放.人们预期,H3受体拮抗剂将临床应用于老年性痴呆症、抑郁症、精神分裂症等中枢性疾病.本文使用三维定量构效关系研究方法,包括比较分子场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA),研究芳基苯并呋喃类H3受体拮抗剂的分子结构与生物活性之间的定量关系.本文使用CoMSIA法所获建三维定量构效关系模型,其交叉验证系数q2为0.646,非交叉验证相关系数R2为0.920,表明模型预测能力较好,同时使用"留八法"证实模型的稳定和可靠.模型中各分子场的贡献为:立体场10.4%、静电场56.9%和疏水场32.7%.三维系数等势图和静电势图显示:母核3'和4'位上的取代基对活性影响较大,估计它们是配体与受体作用的位点.本研究结果可为设计和开发活性更高的该类拮抗剂提供理论参考.     

NMDA受体非竞争性拮抗剂的3D-QSAR研究

NMDA受体(N-methyl-D-aspartate receptor)是离子型谷氨酸受体(ionotropic glutamate receptors,iGluRs)的一亚型,对谷氨酸的神经兴奋毒性起关键性作用,因此对于NMDA受体拮抗剂的应用已引起广泛重视。本研究选用NMDA受体甘氨酸位点拮抗剂1,4-二氢喹喔啉-2,3-二酮衍生物(QXs)为研究对象,采用比较分子场分析法(CoMFA)建立34个NMDA受体拮抗剂的三维定量构效关系(3D-QSAR)模型。此CoMA模型的交叉验证相关系数(q~2)0.566,最佳主成分数(ONC)6,非交叉验证相关系数(r~2)0.969,标准方差(SEE)0.236,立体场和静电场贡献值分别为62.3%和37.7%,研究结果可用分子场等势图直观表示。分子场等势图结果表明,在1,4-二氢喹喔啉-2,3-二酮衍生物苯环2,3位,减少取代基体积或增加取代基的正电性,可以提高该类化合物的活性。所建模型的预测能力和拟合能力较好,不仅了解清楚NMDA受体非竞争性拮抗剂的结构特征,还为设计活性更高的受体拮抗剂提供理论依据。     

Exploring the P2 and P3 ligand binding features for hepatitis C virus NS3 protease using some 3D QSAR techniques

Several three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and catalyst pharmacophore feature building programs for a series of 26 truncated ketoacid inhibitors designed particularly for exploring the P2 and P3 binding pockets of HCV NS3 protease. The structures of these inhibitors were built from a structure template extracted from the crystal structure of HCV NS3 protease. The structures were aligned through docking each inhibitor into the NS3 active site using program GOLD. The best CoMSIA model was identified from the stepwise analysis results and the corresponding pharmacophore features derived were used for constructing a pharmacophore hypothesis by the catalyst program. Pharmacophore features obtained by CoMFA and CoMSIA are found to be in accord with each other and are both mapped onto the molecular 5K surface of NS3 active site. These pharmacophore features were also compared with those obtained by the catalyst program and mapped onto the same NS3 molecular surface. The pharmacophore building process was also performed for 20 boronic acid based NS3 inhibitors characterized by a long hydrophobic side chain attached at position P2. This latter pharmacophore hypothesis built by the catalyst program was also mapped onto the molecular surface of NS3 active site to define a second hydrophobic feature at position P2. The possibility of using the pharmacophore features mapped P2 and P3 binding pocket to design more potent depeptidized NS3 inhibitors was discussed.     

3D-QSAR and molecular docking studies of selective agonists for the thyroid hormone receptor beta

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) on a series of agonists of thyroid hormone receptor beta (TRbeta), which may lead to safe therapies for non-thyroid disorders while avoiding the cardiac side effects. The reasonable q(2) (cross-validated) values 0.600 and 0.616 and non-cross-validated r(2) values of 0.974 and 0.974 were obtained for CoMFA and CoMSIA models for the training set compounds, respectively. The predictive ability of two models was validated using a test set of 12 molecules which gave predictive correlation coefficients (r(pred)(2)) of 0.688 and 0.674, respectively. The Lamarckian Genetic Algorithm (LGA) of AutoDock 4.0 was employed to explore the binding mode of the compound at the active site of TRbeta. The results not only lead to a better understanding of interactions between these agonists and the thyroid hormone receptor beta but also can provide us some useful information about the influence of structures on the activity which will be very useful for designing some new agonist with desired activity.     

3D-QSAR and molecular docking studies on pyrazolopyrimidine derivatives as glycogen synthase kinase-3beta inhibitors

Glycogen synthase kinase-3 (GSK-3), a serine/threonine kinase, is a fascinating enzyme with diverse biological actions in intracellular signaling systems, making it an emerging target for diseases such as diabetes mellitus, cancer, chronic inflammation, bipolar disorders and Alzheimer's disease. It is important to inhibit GSK-3 selectively and the net effect of the GSK-3 inhibitors thus should be target specific, over other phylogenetically related kinases such as CDK-2. In the present work, we have carried out three-dimensional quantitative structure activity relationship (3D-QSAR) studies on novel class of pyrazolopyrimidine derivatives as GSK-3 inhibitors reported to have improved cellular activity. Docked conformation of the most active molecule in the series, which shows desirable interactions in the receptor, was taken as template for alignment of the molecules. Statistically significant CoMFA and CoMSIA models were generated using 49 molecules in training set. By applying leave-one-out (LOO) cross-validation study, r(cv)2 values of 0.53 and 0.48 for CoMFA and CoMSIA, respectively and non-cross-validated (r(ncv)2) values of 0.98 and 0.92 were obtained for CoMFA and CoMSIA models, respectively. The predictive ability of CoMFA and CoMSIA models was determined using a test set of 12 molecules which gave predictive correlation coefficients (r(pred)2) of 0.47 and 0.48, respectively, indicating good predictive power. Based upon the information derived from CoMFA and CoMSIA contour maps, we have identified some key features that explain the observed variance in the activity and have been used to design new pyrazolopyrimidine derivatives. The designed molecules showed better binding affinity in terms of estimated docking scores with respect to the already reported systems; hence suggesting that newly designed molecules can be more potent and selective towards GSK-3beta inhibition.     

范德华力作用下粗糙表面静态粘附的研究

通过在硅微接触表面上涂覆低表面能的憎水性OTS膜以除去接触面间的表面张力,把两表面均接地以除去接触面间的静电力,研究了仅有范德华力作用时硅微结构接触表面的粘附.根据实际粗糙表面凸峰自相似的高度分布,计算了发生粘附后,微观接触表面产生弹性和塑性变形的两种情况下的范德华粘附能,分析了表面形貌对其影响.     

Linear interaction energy models for beta-secretase (BACE) inhibitors: Role of van der Waals, electrostatic, and continuum-solvation terms

Computing the binding affinity of a protein–ligand complex is one of the most fundamental and difficult tasks in computer-aided drug design. Many approaches for computing binding affinities can be classified as linear interaction energy (LIE) models as they rely on some type of linear fit of computed interaction energies between ligand and protein. We have examined the computed interaction energies of a series of β-secretase (BACE) inhibitors in terms of van der Waals, coulombic, and continuum-solvation contributions to ligand binding. We have also systematically examined the effect of different protonation states of the protein and ligands. We find that the binding affinities are relatively insensitive to the protonation state of the protein when neutral ligands are considered. Inclusion of charged ligands leads to large deviations in the coulomb, solvation, and even van der Waals terms. The latter is due to increased repulsive van der Waals interactions in the complex due to the strong coulomb attraction found between oppositely charged functional groups in the protein and ligand. In general, we find that the best models are obtained when the protein is judiciously charged (e.g. Asp32⁻, Arg235⁺) and the potentially charged ligands are treated as neutral.     

Theoretical study of van der Waals dispersion pressures considering one-dimensional material distributions in the in-plane direction

The van der Waals dispersion pressures between a half-space consisting of a uniform material and a half-space with a one-dimensional material distribution in the in-plane direction have been theoretically derived. Two patterns of material distribution were considered: a periodic distribution of materials (Pattern 1) and a distribution of two materials with a single interface (Pattern 2). The van der Waals pressure for Pattern 1 was derived based on a Fourier series, while the van der Waals pressure for Pattern 2 was derived as elementary functions. Both of the van der Waals pressures derived consist of two terms: a conventional term between half-spaces made of uniform materials and a spatial fluctuation term due to the material distribution. The basic characteristics of these van der Waals pressures were quantitatively clarified. Furthermore, an approximate method for obtaining the van der Waals pressure of Pattern 1 from Pattern 2 was proposed.     

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.     

Insight into the structural requirements of proton pump inhibitors based on CoMFA and CoMSIA studies

In the present study, a series of 179 quinoline and quinazoline heterocyclic analogues exhibiting inhibitory activity against Gastric (H+/K+)-ATPase were investigated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods. Both the models exhibited good correlation between the calculated 3D-QSAR fields and the observed biological activity for the respective training set compounds. The most optimal CoMFA and CoMSIA models yielded significant leave-one-out cross-validation coefficient, q(2) of 0.777, 0.744 and conventional cross-validation coefficient, r(2) of 0.927, 0.914 respectively. The predictive ability of generated models was tested on a set of 52 compounds having broad range of activity. CoMFA and CoMSIA yielded predicted activities for test set compounds with r(pred)(2) of 0.893 and 0.917 respectively. These validation tests not only revealed the robustness of the models but also demonstrated that for our models r(pred)(2) based on the mean activity of test set compounds can accurately estimate external predictivity. The factors affecting activity were analyzed carefully according to standard coefficient contour maps of steric, electrostatic, hydrophobic, acceptor and donor fields derived from the CoMFA and CoMSIA. These contour plots identified several key features which explain the wide range of activities. The results obtained from models offer important structural insight into designing novel peptic-ulcer inhibitors prior to their synthesis.