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.     

Development of predictive in silico model for cyclosporine- and aureobasidin-based P-glycoprotein inhibitors employing receptor surface analysis

P-glycoprotein (Pgp) is implicated in multiple drug resistance (MDR) exhibited by several types of cancer against a multitude of anticancer chemotherapeutic agents. This problem prompted several research groups to search for effective P-gp inhibitors. Cyclosporine A (CsA), aureobasidin A (AbA) and related analogues were reported to possess potent inhibitory actions against Pgp. In this work we employed receptor surface analysis (RSA) to construct two satisfactory receptor surface models (RSMs) for cyclosporine- and aureobasidin-based Pgp inhibitors. These pseudoreceptors were combined to achieve satisfactory three-dimensional quantitative structure activity relationship (3D-QSAR) for 68 different cyclosporine and aureobasidin derivatives. Upon validation against an external set of 16 randomly selected Pgp inhibitors, the optimal 3D-QSAR was found to be self-consistent and predictive (r(LOO)(2)=0.673, r(PRESS)(2)=0.600). The resulting 3D-QSAR was employed to probe the structural factors that control the inhibitory activities of cyclosporine and aureobasidin analogues against Pgp.     

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.     

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.     

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.     

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.     

A CoMFA study of COX-2 inhibitors with receptor based alignment

A diverse set of 53 cyclooxygenase-2 (COX-2) inhibitors which were aligned in two different ways were subjected to CoMFA analysis. The first method of alignment of the molecules was based on the binding information sourced from the crystallographic study, from which CoMFA Model 1 was derived. The second mode of alignment was generated by docking the inhibitors in the binding pocket using the DOCK and AFFINITY suite of programs; this gave a second model. The CoMFA Model 2 was slightly better than Model 1 in terms of the statistical parameters r(2) and q(2). The two models could predict very well the activity of a test set of diverse molecules, with a predictive r(2) of 0.593 and 0.768, respectively. Besides the QSAR results, the docking studies give a deep insight into the H-bonding interactions between the inhibitors and residues in the active site of the enzyme, which can be exploited in designing better inhibitors. Useful ideas on activity improvement could be gleaned from these models.     

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.     

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.     

表皮生长因子受体酪氨酸激酶抑制剂的药效团研究

根据一系列表皮生长因子受体酪氨酸激酶抑制剂的三维定量构效关系研究,得到了该类抑制剂的药效团,研究结果与Novartis的药效团模型相当类似。药效团包括一个氢键受体,一个氢键给体,一个疏水区和一个带有氯或溴原子的苯环。该药效团对于研究表皮生长因子受体酪氨酸激酶抑制剂结构与活性的关系具有重要的意义。通过三维数据库搜索可能会得到新的先导化合物。     

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.