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.     

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.     

3D-QSAR and molecular docking studies of 1,3,5-triazene-2,4-diamine derivatives against r-RNA: novel bacterial translation inhibitors

Aminoglycoside mimetics inhibit bacterial translation by interfering with the ribosomal decoding site. To elucidate the structural properties of these compounds important for antibacterial activity, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to a set of 56 aminoglycosides mimetics. The successful CoMFA model yielded the leave-one-out (LOO) cross-validated correlation coefficient (q(2)) of 0.708 and a non-cross-validated correlation coefficient (r(2)) of 0.967. CoMSIA model gave q(2)=0.556 and r(2)=0.935. The CoMFA and CoMSIA models were validated with 36 test set compounds and showed a good r(pred)(2) of 0.624 and 0.640, respectively. Contour maps of the two QSAR approaches show that electronic effects dominantly determine the binding affinities. These obtained results were agreed well with the experimental observations and docking studies. The results not only lead to a better understanding of structural requirements of bacterial translation inhibitors but also can help in the design of novel bacterial translation inhibitors.     

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.     

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.     

An investigation of structurally diverse carbamates for acetylcholinesterase (AChE) inhibition using 3D-QSAR analysis

In order to identify the essential structural features and physicochemical properties for acetylcholinesterase (AChE) inhibitory activity in some carbamate derivatives, the systematic QSAR (Quantitative Structure Activity Relationship) studies (CoMFA, advance CoMFA and CoMSIA) have been carried out on a series of (total 78 molecules) taking 52 and 26 molecules in training and test set, respectively. Statistically significant 3D-QSAR (three-dimensional Quantitative Structure Activity Relationship) models were developed on training set molecules using CoMFA and CoMSIA and validated against test set compounds. The highly predictive models (CoMFA q(2)=0.733, r(2)=0.967, predictive r(2)=0.732, CoMSIA q(2)=0.641, r(2)=0.936, predictive r(2)=0.812) well explained the variance in binding affinities both for the training and the test set compounds. The generated models suggest that steric, electrostatic and hydrophobic interactions play an important role in describing the variation in binding affinity. In particular the carbamoyl nitrogen should be more electropositive; substitutions on this nitrogen should have high steric bulk and hydrophobicity while the amino nitrogen should be electronegative in order to have better activity. These studies may provide important insights into structural variations leading to the development of novel AChE inhibitors which may be useful in the development of novel molecules for the treatment of Alzheimer's disease.     

N,N-二甲基-2-溴苯乙胺类衍生物活性与CoMFA、CoMSIA的研究

用比较分子力场分析(CoMFA)法和比较分子相似性指数分析(CoMSIA)法,建立N,N-二甲基-2-溴苯乙胺类化合物的3D-QSAR模型。CoMFA模型中,其交叉验证系数q2=0．792,传统的相关系数R2=0．955(R=0．978),相应立体场贡献为77．4％、静电场贡献为22．6％,优于文献的报导。CoMSIA研究中,其交叉验证系数q2=0．757,传统的相关系数R2=0．917 (R=0．958),其疏水场、立体场、静电场贡献依次为:42．9％、39．5％、17．6％。用两种模型分别预测检测集分子的活性,结果与实验值较吻合。说明所建的模型具有较好的预测能力。通过分析CoMFA分子场等值线图,可为优化N,N-二甲基-2-溴苯乙胺类衍生物的结构提供理论指导。     

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.     

Studies of tricyclic piperazine/piperidine furnished molecules as novel integrin αvβ3/αIIbβ3 dual antagonists using 3D-QSAR and molecular docking

The development of injectable integrin α(v)β(3)/α(IIb)β(3) dual antagonists attracts much attention of research for treating of acute ischemic diseases in recent years. In this work, based on a dataset composed of 102 tricyclic piperazine/piperidine furnished dual α(v)β(3) and α(IIb)β(3) antagonists, a variety of in silico modeling approaches including the comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), and molecular docking were applied to reveal the requisite 3D structural features impacting the biological activities. Our statistical results show that the ligand-based 3D-QSAR models for both the α(v)β(3) and α(IIb)β(3) studies exhibited satisfactory internal and external predictability, i.e., for the CoMFA models, results of Q(2)=0.48, R(ncv)(2)=0.87, R(pred)(2)=0.71 for α(v)β(3) and Q(2)=0.50, R(ncv)(2)=0.85, R(pred)(2)=0.72 for α(IIb)β(3) analysis were obtained, and for the CoMSIA ones, the outcomes of Q(2)=0.55, R(ncv)(2)=0.90, R(pred)(2)=0.72 for α(v)β(3) and Q(2)=0.52, R(ncv)(2)=0.88, R(pred)(2)=0.74 for α(IIb)β(3) were achieved respectively. In addition, through a comparison between 3D-QSAR contour maps and docking results, it is revealed that that the most crucial interactions occurring between the tricyclic piperazine/piperidine derivatives and α(v)β(3)/α(IIb)β(3) receptor ligand binding pocket are H-bonding, and the key amino acids impacting the interactions are Arg214, Asn215, Ser123, and Lys253 for α(v)β(3), but Arg214, Asn215, Ser123 and Tyr190 for α(IIb)β(3) receptors, respectively. Halogen-containing groups at position 15 and 16, benzene sulfonamide substituent at position 23, and the replacement of piperazine with 4-aminopiperidine of ring B may increase the α(v)β(3)/α(IIb)β(3) antagonistic activity. The potencies for antagonists to inhibit isolated α(v)β(3) and α(IIb)β(3) are linear correlated, indicating that similar interaction mechanisms may exist for the series of molecules. To our best knowledge this is the first report on 3D-QSAR modeling of these dual α(v)β(3)/α(IIb)β(3) antagonists. The results obtained should provide information for better understanding of the mechanism of antagonism and thus be helpful in design of novel potent dual α(v)β(3)/α(IIb)β(3) antagonists.     

Investigation of structural requirements of anticancer activity at the paclitaxel/tubulin binding site using CoMFA and CoMSIA

CoMFA and CoMSIA analysis were utilized in this investigation to define the important interacting regions in paclitaxel/tubulin binding site and to develop selective paclitaxel-like active compounds. The starting geometry of paclitaxel analogs was taken from the crystal structure of docetaxel. A total of 28 derivatives of paclitaxel were divided into two groups-a training set comprising of 19 compounds and a test set comprising of nine compounds. They were constructed and geometrically optimized using SYBYL v6.6. CoMFA studies provided a good predictability (q(2)=0.699, r(2)=0.991, PC=6, S.E.E.=0.343 and F=185.910). They showed the steric and electrostatic properties as the major interacting forces whilst the lipophilic property contribution was a minor factor for recognition forces of the binding site. These results were in agreement with the experimental data of the binding activities of these compounds. Five fields in CoMSIA analysis (steric, electrostatic, hydrophobic, hydrogen-bond acceptor and donor properties) were considered contributors in the ligand-receptor interactions. The results obtained from the CoMSIA studies were: q(2)=0.535, r(2)=0.983, PC=5, S.E.E.=0.452 and F=127.884. The data obtained from both CoMFA and CoMSIA studies were interpreted with respect to the paclitaxel/tubulin binding site. This intuitively suggested where the most significant anchoring points for binding affinity are located. This information could be used for the development of new compounds having paclitaxel-like activity with new chemical entities to overcome the existing pharmaceutical barriers and the economical problem associated with the synthesis of the paclitaxel analogs. These will boost the wide use of this useful class of compounds, i.e. in brain tumors as the most of the present active compounds have poor blood-brain barrier crossing ratios and also, various tubulin isotypes has shown resistance to taxanes and other antimitotic agents.     

Binding research on flavones as ligands of β-amyloid aggregates by fluorescence and their 3D-QSAR, docking studies

The K(d)s (dissociation constants) of 21 flavone derivatives have been obtained by fluorescence in vitro when binding with Aβ(1-40) (β-amyloid(1-40)) aggregates protein. Extensive 3D-QSAR (quantitative structure-activity relationship) studies were performed on the fluorescent flavones, which are excellent ligands of Aβ(1-40) aggregates protein. Comparative molecular similarity indices analysis (CoMSIA) technique was used to relate the binding affinities with the ligand structures, and the QSAR model was obtained using the CoMFA technique. The QSAR model was proved to statistically significant and have high predictive power: the CoMSIA model yielded the cross-validated q2 = 0.512 and the non-cross-validated r2 = 0.911. This model showed that electrostatic (22.5%) and H-bond interaction (acceptor 15.3%; donor 45.1%) properties played major roles in ligand binding process. The QSAR model was further graphically interpreted in terms of field contribution maps. In order to further investigate the specific binding site of the flavones in the Aβ(1-40) aggregates, preliminary docking studies were performed. According to the 3D-QSAR results, the possible binding site in the protein was proposed in order to direct the molecular docking studies. A good correlation (R2: 0.846) between the calculated binding energies and the experimental binding affinities (pK(d)s) suggests that the identified binding site is reliable. The 3D-QSAR model and the information of the ligand-protein interaction will be helpful in the selection of flavones to be structurally modified and labeled by a radio nuclide for imaging Aβ(1-40) aggregates in the AD (Alzheimer's disease) brain.     

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。     

Receptor-based QSAR studies of non-peptide human oxytocin receptor antagonists

In the present study, QSAR calculations were performed on the receptor-based alignment of 58 non-peptide human oxytocin receptor antagonists. With the aid of different scoring functions (AutoDock 3.05 built-in and X-Score 1.2) the evolved receptor-ligand complexes were characterized. By means of various datasets it was confirmed that the scoring functions were not capable to predict the biological activity correctly in compounds containing a rigid derivative in the variable region. To improve the pKi prediction 3D-QSAR calculation was performed. The regions related to the biological activity were determined by using cross-validated r2(q2)-guided region selection (q2-GRS) method. The predictive power of the CoMFA model [r(pred)2=0.89, q2(LMO, five groups)=0.695+/-0.034] allowed prediction of the biological activities of newly synthesized compounds and confirmed the receptor-based alignment.     

3D QSAR studies of 1,3,4-benzotriazepine derivatives as CCK2 receptor antagonists

A number of CCK(2) antagonists have been reported to play an important role in controlling gastric acid-related conditions, nervous system related disorders and certain types of cancer. To obtain the helpful information for designing potent antagonists with novel structures and to investigate the quantitative structure-activity relationship of a group of 62 different CCK(2) receptor antagonists with varying structures and potencies, CoMFA, CoMSIA, and HQSAR studies were carried out on a series of 1,3,4-benzotriazepine-based CCK(2) receptor antagonists. QSAR models were derived from a training set of 49 compounds. By applying leave-one-out (LOO) cross-validation study, cross-validated (r(cv)(2)) values of 0.673 and 0.608 and non-cross-validated (r(ncv)(2)) values of 0.966 and 0.969 were obtained for the CoMFA and CoMSIA models, respectively. The predictive ability of the CoMFA and CoMSIA models was determined using a test set of 13 compounds, which gave predictive correlation coefficients (r(pred)(2)) of 0.793 and 0.786, respectively. HQSAR was also carried out as a complementary study, and the best HQSAR model was generated using atoms, bonds, hydrogen atoms, and chirality as fragment distinction with fragment size (2-5) and six components showing r(cv)(2) and r(ncv)(2) values of 0.744 and 0.918, respectively. CoMFA steric and electrostatic, CoMSIA hydrophobic and hydrogen bond acceptor fields, and HQSAR atomic contribution maps were used to analyze the structural features of the datasets that govern their antagonistic potency.     

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.     

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.     

新非核苷喹诺酮类HIV-1逆转录酶抑制剂的CoMFA研究

目的：应用比较分子力场法(COMFA)研究一系列喹诺酮类对HIV-1逆转录酶抑制活性的三维定量构效关系,为进一步抗HIV药物设计提供理论依据。方法和结果：在研究的29个化合物中,用比较分子力场法得到一个CoMFA模型,交叉验证系数为q~2=0.556,具有较高的预测能力及合理性,非交叉验证模型相关系数分别为r~2=0.998,标准偏差SE=0.044,F= 401.038；结论：此模型对设计和预测高活性的喹诺酮类HIV-1逆转录酶抑制活性的化合物有一定可靠性。     

比较分子力场法研究5,6-二氢-(9H)-吡唑[3,4-c]-1,2,4-三唑[4,3-a]吡啶类抑制剂的定量构效关系

利用比较分子力场分析法(CoMFA),以5,6-二氢-(9H)-吡唑[3,4-c]-1,2,4一三唑[4,3-a]吡啶类抑制剂为研究对象,建立一组对嗜酸性粒细胞磷酸二酯酶有抑制活性的化合物及其三维定量构效关系(3D-QSAR)模型,探索化合物活性数据和三维结构参数之间的关系.模型的交叉验证相关系数q2=0.565,非交叉验证相关系数r2=0.867,标准偏差SE=0.362,F=49.782,立体场和静电场的贡献值分别为72.7%和27.3%.该模型的预测能力较好,能够增大取代基体积和降低取代基电负性,可以提高该类化合物的活性.