2D QSAR and similarity studies on cruzain inhibitors aimed at improving selectivity over cathepsin L

Hologram quantitative structure-activity relationships (HQSAR) were applied to a data set of 41 cruzain inhibitors. The best HQSAR model (Q(2)=0.77; R(2)=0.90) employing Surflex-Sim, as training and test sets generator, was obtained using atoms, bonds, and connections as fragment distinctions and 4-7 as fragment size. This model was then used to predict the potencies of 12 test set compounds, giving satisfactory predictive R(2) value of 0.88. The contribution maps obtained from the best HQSAR model are in agreement with the biological activities of the study compounds. The Trypanosoma cruzi cruzain shares high similarity with the mammalian homolog cathepsin L. The selectivity toward cruzain was checked by a database of 123 compounds, which corresponds to the 41 cruzain inhibitors used in the HQSAR model development plus 82 cathepsin L inhibitors. We screened these compounds by ROCS (Rapid Overlay of Chemical Structures), a Gaussian-shape volume overlap filter that can rapidly identify shapes that match the query molecule. Remarkably, ROCS was able to rank the first 37 hits as being only cruzain inhibitors. In addition, the area under the curve (AUC) obtained with ROCS was 0.96, indicating that the method was very efficient to distinguishing between cruzain and cathepsin L inhibitors.     

A型肉毒毒素抑制剂的分子全息定量构效关系研究

目的:建立A型肉毒毒素抑制剂的定量构效关系模型。方法:应用分子全息定量构效关系(HQSAR)技术,研究了14种A型肉毒毒素抑制剂的抑制活性与其二维分子结构之间的关系,讨论了碎片区分参数及碎片长度对模型质量的影响。结果:最佳全息条件下产生的模型相关系数r2为0.780,交叉验证相关系数q2LOO为0.583。所建模型具有良好的拟和效果和较高的预测能力,HQSAR模型贡献图显示抑制剂分子中的噻吩环及羟胺对活性有较大贡献。结论:本研究对新抑制剂的设计具有一定的指导作用。     

CoMFA and HQSAR studies on 6,7-dimethoxy-4-pyrrolidylquinazoline derivatives as phosphodiesterase10A inhibitors

Phosphodiesterase10A (PDE10A) is an important enzyme with diverse biological actions in intracellular signaling systems, making it an emerging target for diseases such as schizophrenia, Huntington's disease, and diabetes mellitus. The objective of the current 3D QSAR study is to uncover some of the structural parameters which govern PDE10A inhibitory activity over PDE3A/B. Thus, comparative molecular field analysis (CoMFA) and hologram quantitative structure-activity relationship (HQSAR) studies were carried out on recently reported 6,7-dimethoxy-4-pyrrolidylquinazoline derivatives as PDE10A inhibitors. The best CoMFA model using atom-fit alignment approach with the bound conformation of compound 21 as the template yielded the steric parameter as a major contributor (nearly 70%) to the observed variations in biological activity. The best CoMFA model produced statistically significant results, with the cross-validated (r(cv)(2)) and conventional correlation (r(ncv)(2)) coefficients being 0.557 and 0.991, respectively, for the 21 training set compounds. Validation of the model by external set of six compounds yielded a high (0.919) predictive value. The CoMFA models of PDE10A and PDE3A/B activity were compared in order to address the selectivity issue of these inhibitors. The best HQSAR model for PDE10A was obtained with an r(cv)(2) of 0.704 and r(ncv)(2) of 0.902 using atoms, bonds, connections, chirality, donor, and acceptor as fragment distinction and default fragment size of 4-7 with three components for the 21 compounds. The HQSAR model predicted the external test-set of compounds well since a good agreement between the experimental and predicted values was verified. Taken together, the present QSAR models were found to accurately predict the PDE10A inhibitory activity of the test-set compounds and to yield reliable clues for further optimization of the quinazoline derivatives in the dataset.     

Two- and three-dimensional quantitative structure-activity relationships for a series of purine nucleoside phosphorylase inhibitors

Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis, and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of 52 training set inhibitors of calf spleen purine nucleoside phosphorylase (PNP). Significant cross-validated correlation coefficients (CoMFA, q(2)=0.68; CoMSIA, q(2)=0.66; and HQSAR, q(2)=0.70) were obtained, indicating the potential of the models for untested compounds. The models were then used to predict the inhibitory potency of 16 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results. The final QSAR models along with the information gathered from 3D contour and 2D contribution maps should be useful for the design of novel inhibitors of PNP having improved potency.     

Hologram quantitative structure activity relationship studies on 5-HT6 antagonists

Predictive hologram quantitative structure activity relationship (HQSAR) models were developed for a series of arylsulfonamide compounds acting as specific 5-HT6 antagonists. A training set containing 48 compounds served to establish the model. The best HQSAR model was generated using atoms, bond, and connectivity as fragment distinction and 4-7 as fragment size showing cross-validated r2(q2) value of 0.702 and conventional r2 value of 0.971. The predictive ability of the model was validated by an external test set of 20 compounds giving satisfactory predictive r2 value of 0.678. The efficiency of HQSAR approach was further evidenced by the generation of predictive models for a training set containing 30 highly diverse, both specific and nonspecific 5-HT6 antagonists. The best HQSAR model for this training set was generated using atoms, bond, and connectivity as fragment distinction and 4-7 as fragment size showing cross-validated r2(q2) value of 0.693 and conventional r2 value of 0.923. This model was also validated by using an external test set of 10 compounds giving satisfactory predictive r2 value of 0.692. The contribution maps obtained from these models were used to explain the individual atomic contributions to the overall activity.     

HQSAR study of beta-ketoacyl-acyl carrier protein synthase III (FabH) inhibitors

The enzyme FabH catalyzes the initial step of fatty acid biosynthesis via a type II fatty acid synthase. The pivotal role of this essential enzyme combined with its unique structural features and ubiquitous occurrence in bacteria has made it an attractive new target for the development of antibacterial and antiparasitic compounds. Predictive hologram quantitative structure activity relationship (HQSAR) model was developed for a series of benzoylamino benzoic acid derivatives acting as FabH inhibitor. The best HQSAR model was generated using atoms and bond types as fragment distinction and 4-7 as fragment size showing cross-validated q2 value of 0.678 and conventional r2 value of 0.920. The predictive ability of the model was validated by an external test set of 6 compounds giving satisfactory predictive r2 value of 0.82. The contribution maps obtained from this model were used to explain the individual atomic contributions to the overall activity. It was confirmed from the contribution map that both ring A and ring C play a vital role for activity. Moreover hydroxyl substitution in the ortho position of ring A is favorable for better inhibitory activity. Therefore the information derived from the contribution map can be used to design potent FabH inhibitors.     

An explorative study on potent Gram-negative specific LpxC inhibitors: CoMFA,CoMSIA, HQSAR and molecular docking

Pathogenic Gram-negative bacteria are responsible for nearly half of the serious human infections. Hologram quantitative structure–activity relationships (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) were implemented on a group of 32 of potent Gram-negative LpxC inhibitors. The most effective HQSAR model was obtained using atoms, bonds, donor, and acceptor as fragment distinction. The cross-validated correlation coefficient (q²), non-cross-validated correlation coefficient (r²), and predictive correlation coefficient (r²_Pred) for test set of HQSAR model were 0.937, 0.993, and 0.892, respectively. The generated models were found to be statistically significant as the CoMFA model had (r²?=?0.967, q²?=?0.804, r²_Pred?=?0.827); the CoMSIA model had (r²?=?0.963, q²?=?0.752, r²_Pred?=?0.857). Molecular docking was employed to validate the results of the HQSAR, CoMFA, and CoMSIA models. Based on the obtained information, six new LpxC inhibitors have been designed.     

CoMFA of artemisinin derivatives: effect of location and size of lattice.

A CoMFA study of artemisinin derivatives with changes of the location and the number of lattice points was performed. The location of probe atoms in a large lattice has practically no effect on the cross-validated r(2) value (r(2)(cv)). The selection of only 18 probe atoms around the peroxide bond, considering the action mechanism of artemisinin, provided a less time-demanding and more reliable CoMFA model, which forecasts better than the large lattice model despite the lower r(2)(cv) value. Only 1 A displacement of the small lattice caused a reduction of cross-validated r(2) value of more than 50%, which indicates the lattice location played an important role in this small lattice model.     

QSAR studies on piperazinylalkylisoxazole analogues selectively acting on dopamine D3 receptor by HQSAR and CoMFA

QSAR studies for piperazinylalkylisoxazole analogues were conducted by hologram QSAR (HQSAR) and comparative molecular field analysis (CoMFA) to explain the binding affinities of 264 ligands acting on dopamine D(3) receptor. The HQSAR was assessed by r(2) value of 0.917 and cross validated q(2) value of 0.841. In the CoMFA, r(2) is 0.919 and cross validated q(2) is 0.727. The results provide the tools for predicting the affinity of related compounds and guiding the design of new ligands.     

Comparative molecular field analysis of flavonoid inhibitors of the PIM-1 kinase

The PIM-1 protein, the product of the pim-1 oncogene, is a serine/threonine kinase. Dysregulation of the PIM-1 kinase has been implicated in the development of human malignancies including lymphomas, leukemias, and prostate cancer. Comparative molecular field analysis (CoMFA) is a 3-D QSAR technique that has been widely used, with notable success, to correlate biological activity with the steric and electrostatic properties of ligands. We have used a set of 15 flavonoid inhibitors of the PIM-1 kinase, aligned de novo by common substructure, to generate a CoMFA model for the purpose of elucidating the steric and electrostatic properties involved in flavonoid binding to the PIM-1 kinase. Partial least squares correlation between observed and predicted inhibitor potency (expressed as -logIC50), using a non-cross-validated partial least squares analysis, generated a non-cross-validated q2=0.805 for the training set (n=15) of flavonoids. The CoMFA generated steric map indicated that the PIM-1-binding site was sterically hindered, leading to more efficient binding of planar molecules over (R) or (S) compounds. The electrostatic map identified that positive charges near the flavonoid atom C8 and negative charges near C4' increased flavonoid binding. The CoMFA model accurately predicted the potency of a test set of flavonoids (n=6), generating a correlation between observed and predicted potency of q2=0.825. CoMFA models generated from additional alignment rules, which were guided by co-crystal structure ligand orientations, did not improve the correlative value of the model. Superimposing the PIM-1 kinase crystal structure onto the CoMFA contours validated the steric and electrostatic maps, elucidating the amino acid residues that potentially contribute to the CoMFA fields. Thus we have generated the first predictive model that may be used for the rational design of small-molecule inhibitors of the PIM-1 kinase.     

Hologram quantitative structure-activity relationships for a series of farnesoid X receptor activators

The farnesoid X receptor (FXR) is an attractive drug target for the development of novel therapeutic agents for the treatment of dyslipidemia and cholestasis. Hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of potent FXR activators originated from natural product-like libraries. A training set containing 82 compounds served to establish the models. The best HQSAR model was generated using atoms, bonds, connections, chirality, and donor and acceptor as fragment distinction and fragment size default (4-7) with six components. The model was used to predict the potency of 20 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from HQSAR 2D contribution maps should be useful for the design of novel FXR ligands having improved potency.     

Particular interaction between pyrimethamine derivatives and quadruple mutant type dihydrofolate reductase of Plasmodium falciparum: CoMFA and quantum chemical calculations studies

Comparative molecular field analysis (CoMFA) was performed on twenty-three pyrimethamine (pyr) derivatives active against quadruple mutant type (Asn51Ile, Cys59Arg, Ser108Asn, Ile164Leu) dihydrofolate reductase of Plasmodium falcipaarum (PfDHFR). The represented CoMFA models were evaluated based on the various three different probe atoms, C(sp3) (+1), O(sp3) (-1) and H (+1), resulting in the best model with combined three types of probe atoms. The statistical results were r(2)(cv) = 0.702, S(press) = 0.608, r(2)(nv) = 0.980, s = 0.156, and r(2)(test-set) = 0.698 which can explain steric contribution of about 50%. In addition, an understanding of particular interaction energy between inhibitor and surrounding residues in the binding pocket was performed by using MP2/6-31G(d,p) quantum chemical calculations. The obtained results clearly demonstrate that Asn108 is the cause of pyr resistance with the highest repulsive interaction energy. Therefore, CoMFA and particular interaction energy analyses can be useful for identifying the structural features of potent pyr derivatives active against quadruple mutant type PfDHFR.     

CoMFA and CoMSIA studies of angiotensin (AT1) receptor antagonists

Two 3D-QSAR methods--CoMFA and CoMSIA--were applied to a set of 38 angiotensin receptor (AT1) antagonists. The conformation and alignment of molecules were obtained by a novel method - consensus dynamics. The representation of biological activity, partial charge formalism, absolute orientation of the molecules in the grid, and grid spacing were also studied for their effect on the CoMFA models. The models were thoroughly validated through trials using scrambled activities and bootstrapping. The best CoMFA model had a cross-validated correlation coefficient ( q2) of 0.632, which improved with "region focusing" to 0.680. This model had a "predictive" r2 of 0.436 on a test series that was unique and with little representation in the training set. Although the "predictive" r2 of the best CoMSIA model, which included steric, electrostatic, and hydrogen bond acceptor fields was higher than that of the best CoMFA model, the other statistical parameters like q2, r2, F value, and s were unsatisfactory. The contour maps generated using the best CoMFA model were used to identify the structural features important for biological activity in these compounds.     

Molecular docking and 3D-QSAR studies of Yersinia protein tyrosine phosphatase YopH inhibitors

Three-dimensional quantitative structure-activity relationship (QSAR) studies were conducted on two classes of recently explored compounds with known YopH inhibitory activities. Docking studies were employed to position the inhibitors into the YopH active site to determine the probable binding conformation. Good correlations between the predicated binding free energies and the inhibitory activities were found for two subsets of phosphate mimetics: alpha-ketocarboxylic acid and squaric acid (R2=0.70 and 0.68, respectively). The docking results also provided a reliable conformational alignment scheme for 3D-QSAR modeling. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed based on the docking conformations, giving q2 of 0.734 and 0.754 for CoMFA and CoMSIA models, respectively. The 3D-QSAR models were significantly improved after removal of an outlier (q2=0.829 for CoMFA and q2=0.837 for CoMSIA). The predictive ability of the models was validated using a set of compounds that were not included in the training set. Mapping the 3D-QSAR models to the active site of YopH provides new insight into the protein-inhibitor interactions for this enzyme. These results should be applicable to the prediction of the activities of new YopH inhibitors, as well as providing structural implications for designing potent and selective YopH inhibitors as antiplague agents.     

3D-QSAR analysis on benzazole derivatives as eukaryotic topoisomerase II inhibitors by using comparative molecular field analysis method

Selective topoisomerase II inhibitors have created a great deal of interest in recent years for the design of new antitumoral compounds. 3D-QSAR analysis has been performed on a series of previously synthesized benzoxazole, benzimidazole, and oxazolo(4,5-b)pyridine derivatives, which are screened as eukaryotic topoisomerase II inhibitors, using comparative molecular field analysis (CoMFA) with partial least squares fit to predict the steric and electrostatic molecular field interactions for the activity. The CoMFA study was carried out using a training set of 16 compounds. The predictive ability of the model was assessed using a test set of 7 compounds. The analyzed 3D-QSAR CoMFA model has demonstrated a good fit, having r(2) value of 0.997 and cross-validated coefficient q(2) value as 0.435 for the model. The obtained model reveals that the electronegatively charged substituents such as NO(2) or COOCH(3) group on position R and/or R(1) at the heterocyclic ring system and positively charged atom and/or atom groups located between the benzazole moiety and 2-substituted phenyl ring as a bridge element improve the activity. On the other hand, a bulky substituent, such as methoxy group, attached to the ortho position of 2-phenyl-5-nitro-benzoxazole (1) enhances the activity similar to compound 13, which is both a meta and para substituent of the phenyl group attached to the 2-position of benzimidazole ring system, fit into the favored steric region to improve the activity.     

3D-QSAR Study on dihydro-1,3,5-triazines and their spiro derivatives as DHFR inhibitors by comparative molecular field analysis (CoMFA)

A 3D-QSAR/CoMFA was performed for a series of triazine and its spiro derivative based DHFR inhibitors displaying IC(50) values ranging from 0.002 to 58.8 μM. Analyses resulted in a reliable computational model with the parameters of n=46, r(2)=0.986, q(2)=0.724, SE=0.164, F=275.889. It is shown that the steric and electrostatic properties predicted by CoMFA contours can be related to the DHFR inhibitory activity. The predictive ability of the resultant model was evaluated using a test set comprised of 18 molecules and the results show that the CoMFA model is able to correctly predict the poor inhibitory activities of the compounds in the testing set. This model is a significant guide to trace the features that really matter especially with respect to the design of novel compounds.     

Three-dimensional quantitative structure-activity relationship studies on novel series of benzotriazine based compounds acting as Src inhibitors using CoMFA and CoMSIA

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of benzotriazine derivatives, as Src inhibitors. Ligand molecular superimposition on the template structure was performed by database alignment method. The statistically significant model was established of 72 molecules, which were validated by a test set of six compounds. The CoMFA model yielded a q(2)=0.526, non cross-validated R(2) of 0.781, F value of 88.132, bootstrapped R(2) of 0.831, standard error of prediction=0.587, and standard error of estimate=0.351 while the CoMSIA model yielded the best predictive model with a q(2)=0.647, non cross-validated R(2) of 0.895, F value of 115.906, bootstrapped R(2) of 0.953, standard error of prediction=0.519, and standard error of estimate=0.178. The contour maps obtained from 3D-QSAR studies were appraised for activity trends for the molecules analyzed. Results indicate that small steric volumes in the hydrophobic region, electron-withdrawing groups next to the aryl linker region, and atoms close to the solvent accessible region increase the Src inhibitory activity of the compounds. In fact, adding substituents at positions 5, 6, and 8 of the benzotriazine nucleus were generated new compounds having a higher predicted activity. The data generated from the present study will further help to design novel, potent, and selective Src inhibitors as anticancer therapeutic agents.