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。     

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.     

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.     

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.     

Structural investigation of PAP derivatives by CoMFA and CoMSIA reveals novel insight towards inhibition of Bcr-Abl oncoprotein

Molecular modeling by 3D-QSAR comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed on a series of phenylaminopyrimidine-based (PAP) Bcr-Abl inhibitors. The chemical structures of 63 PAP analogues were aligned using a template extracted from the crystal structure of STI571 bound to Abl kinase. Subsequently, the structures built were divided into training and test sets that include 53 and 10 compounds, respectively. Statistical results showed that the 3D-QSAR models generated from CoMSIA were superior to CoMFA (CoMSIA; q2=0.66, r2=0.94, N=3, F=139.09, r2pred=0.64 while CoMFA; q2=0.53, r2=0.73, N=3, F=43.53, r2pred=0.61). Based on the contour interpretation, the attachment of hydrophobic and bulky groups to the phenyl and pyrrolidine (D- and E-ring of NS-187, respectively) along with highly electronegative groups around the D-ring are important structural features for the design of second-generation Bcr-Abl inhibitors. The generated models are predictive based on reproducible values of the predicted compared with experimental activities in the test set. Further, the complementary analysis of contour maps to the Bcr-Abl binding site suggested the anchor points for binding affinity.     

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.     

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.     

Molecular modeling study of CP-690550 derivatives as JAK3 kinase inhibitors through combined 3D-QSAR,molecular docking,and dynamics simulation techniques

To develop more potent JAK3 kinase inhibitors, a series of CP-690550 derivatives were investigated using combined molecular modeling techniques, such as 3D-QSAR, molecular docking and molecular dynamics (MD). The leave-one-out correlation (q²) and non-cross-validated correlation coefficient (r²) of the best CoMFA model are 0.715 and 0.992, respectively. The q² and r² values of the best CoMSIA model are 0.739 and 0.995, respectively. The steric, electrostatic, and hydrophobic fields played important roles in determining the inhibitory activity of CP-690550 derivatives. Some new JAK3 kinase inhibitors were designed. Some of them have better inhibitory activity than the most potent Tofacitinib (CP-690550). Molecular docking was used to identify some key amino acid residues at the active site of JAK3 protein. 10 ns MD simulations were successfully performed to confirm the detailed binding mode and validate the rationality of docking results. The calculation of the binding free energies by MMPBSA method gives a good correlation with the predicted biological activity. To our knowledge, this is the first report on MD simulations and free energy calculations for this series of compounds. The combination results of this study will be valuable for the development of potent and novel JAK3 kinase inhibitors.     

家蝇GABA受体抑制剂的比较分子相似性指数分析模型研究

目的：寻找家蝇GABA受体抑制剂的化学结构与生物活性之间的关系，为设计合成新的更高活性的家蝇GA—BA受体抑制剂提供理论依据，从而为新农药的创制提供线索。方法：选择三类29个家蝇GABA受体抑制剂，在SGI工作站上，用SYBYM．9软件，进行比较分析相似性指数分析。结果：模型的传统相关系数r^2=0．929，交叉验证系数q^2=0．713，F(4，24)=78．392，标准偏差S=0．273。结论：在CoMSIA模型中，影响抑制剂活性的主要因素包括立体场，静电场和疏水场，对抑制剂的这些属性的合理设计可能增加抑制剂的生物活性.     

真蛸胺类杀虫剂药效团模型的确定

用比较分子场分析方法（ＣｏＭＦＡ）研究了２１种真蛸胺类杀虫剂的三维定量构效关系，确定了药效团模型，得到了具有较好预测能力的ＣｏＭＦＡ模型。研究表明，苯环与支链Ｎ原子及它们间的距离对于这些化合物的活性有重要影响，而且立体与静电场对其药效均起作用。     

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.     

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.     

氰基丙烯酸酯类化合物的3D-QSAR研究

3D-Quantitative Structure—Activity Relationships of a series of cyanoacrylate inhibitors which are known to act by blocking photosynthetic electron transport close to photosystem II reaction center in the thylakoid membranes of plant chloroplasts have been investigated using comparative molecular field analysis(CoMFA). The active conformations of the title compounds were obtained by constrained systematic search program. The resulting CoMFA model with considerable predictive ability shows that the contribution of steric effects for activities of cyanoacrylate inhibitors is more important than electrostatic effects.     

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.     

Structural and chemical basis for enhanced affinity and potency for a large series of estrogen receptor ligands: 2D and 3D QSAR studies

The estrogen receptor (ER) is an important drug target for the development of novel therapeutic agents for the treatment of breast cancer. Progress towards the design of more potent and selective ER modulators requires the optimization of multiple ligand-receptor interactions. Comparative molecular field analyses (CoMFA) and hologram quantitative structure-activity relationships (HQSAR) were conducted on a large set of ERalpha modulators. Two training sets containing either 127 or 69 compounds were used to generate QSAR models for in vitro binding affinity and potency, respectively. Significant correlation coefficients (affinity models, CoMFA, r(2)=0.93 and q(2)=0.79; HQSAR, r(2)=0.92 and q(2)=0.71; potency models, CoMFA, r(2)=0.94 and q(2)=0.72; HQSAR, r(2)=0.92 and q(2)=0.74) were obtained, indicating the potential of the models for untested compounds. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. The final QSAR models as well as the information gathered from 3D contour maps should be useful for the design of novel ERalpha modulators having improved affinity and potency.