Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201

A set of 102 peptides with affinity for the class I MHC HLA-A0201 molecule was subjected to three-dimensional quantitative structure-affinity relationship (3D QSAR) studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). A test set of 50 peptides was used to determine the predictive value of the models. The CoMFA models gave q(2) and r(2)pred below 0.5. The best CoMSIA model has q(2) = 0.542 and r(2)pred = 0.679, and includes hydrophobic, steric, and H-bond donor fields. The hydrophobic interactions play a dominant role in peptide-MHC molecule binding. CoMSIA coefficient contour maps were used to analyze the structural features of the peptides accounting for the affinity in terms of the three positively contributing physicochemical properties: local hydrophobicity, steric bulk and hydrogen-bond-donor ability.     

3D-QSAR analysis of anti-cancer agents by CoMFA and CoMSIA

Three-dimensional quantitative structure–activity relationships were performed for a series of isatin derivatives as anti-cancer agents using the CoMFA and CoMSIA methods. Statistically significant CoMFA ( $r_{\text{cv}}^{2} = 0.869,\;r_{\text{ncv}}^{2} = 0.962$ ) and CoMSIA ( $r_{\text{cv}}^{2} = 0.865,\;r_{\text{ncv}}^{2} = 0.959$ ) models were generated using the training set on the basis of the common substructure-based alignment. Further, the predictive ability of the CoMFA and CoMSIA models was determined using a test set of nine compounds. Based on the information derived from CoMFA and CoMSIA contour maps, we have identified some key features for increasing the activity of compounds and have been used to design new anti-cancer agents. The newly designed molecules in this series of compounds may be more potent anti-cancer agents.     

Design of novel anaplastic lymphoma kinase (ALK) inhibitors based on predictive 3D QSAR models using different alignment strategies

Anaplastic lymphoma kinase (ALK) is involved in many signaling mechanisms that lead to cell-cycle progression; overexpression of ALK has been found in many types of cancers. ALK is a recognized target for the development of small-molecule inhibitors for the treatment of cancer. In this study, a diverse set of 71 ALK inhibitors were aligned by three different methods (pharmacophore, docking-based, and rigid body (Distill) alignment) for the development of comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. The best 3D QSAR models were obtained, which used rigid body (Distill) alignment of test and training set molecules. CoMFA and CoMSIA models were found statistically significant with leave-one-out correlation coefficients (q ²) of 0.816 and 0.838, respectively; cross-validated coefficients ( $r_{\text{cv}}^{2}$ ) of 0.812 and 0.837, respectively; and conventional coefficients (r ²) of 0.969 and 0.966, respectively. QSAR models were validated by a test set of 14 compounds giving satisfactory prediction of correlation coefficients ( $r_{\text{pred}}^{2}$ ) of 0.910 and 0.904 for CoMFA and CoMSIA models, respectively. Based on the generated contour maps, we have designed 10 novel ALK inhibitors and predicted their activities. Finally, molecular docking study was performed for designed molecules. The designed compounds showed good potential to be used as ALK inhibitors.     

CoMFA and CoMSIA analyses of Pneumocystis carinii dihydrofolate reductase, Toxoplasma gondii dihydrofolate reductase, and rat liver dihydrofolate reductase

In a continuing effort to develop potent and selective dihydrofolate reductase (DHFR) inhibitors against opportunistic pathogens, we developed three-dimensional quantitative structure-activity relationship (3D QSAR) models for the inhibitory activity against Pneumocystis carinii (pc) DHFR, Toxoplasma gondii (tg) DHFR, and rat liver DHFR, using a data set of 179 structurally diverse compounds. To ensure a balanced distribution of more potent and less potent drugs in the training set, three different 90-compound training sets taken from the main data set were used, one for each enzyme, while the remaining 89 compounds in the main data set in each case were used as the test set. Three methods, namely, conventional CoMFA, all orientation search (AOS) CoMFA, and CoMSIA were applied to the training sets. While the AOS CoMFA models gave the best internal predictions (cross-validated r(2) values from the training sets), which are satisfactory, CoMSIA models gave the best external predictions (predictive r(2) values from the test sets). Both AOS CoMFA and CoMSIA analyses were used to construct stdev*coefficient contour maps which can be used to design new compounds in an interactive fashion.     

3D QSAR studies on cinnamaldehyde analogues as farnesyl protein transferase inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies on 59 cinnamaldehyde analogues as Farnesyl Protein Transferase (FPTase) inhibitors were investigated using comparative molecular field analysis (CoMFA) with the PLS region-focusing method. Forty-nine training set inhibitors were used for CoMFA with two different grid spacings, 2A and 1A. Ten compounds, which were not used in model generation, were used to validate the CoMFA models. After the PLS analysis, the best predictive CoMFA model showed that the cross-validated value (r2cv) and the non-cross validated conventional value (r2ncv) are 0.557 and 0.950, respectively. From the CoMFA contour maps, the steric and electrostatic properties of cinnamaldehyde analogues can be identified and verified.     

Inhibitory mode of 1,5-diarylpyrazole derivatives against cyclooxygenase-2 and cyclooxygenase-1: molecular docking and 3D QSAR analyses

The Lamarckian genetic algorithm of AutoDock 3.0 has been employed to dock 40 1,5-diarylpyrazole class compounds into the active sites of cyclooxygenase-2 (COX-2) and cyclooxygenase-1 (COX-1). The binding models were demonstrated in the aspects of inhibitor's conformation, subsite interaction, and hydrogen bonding. The data of geometrical parameters and RMSD values compared with the known inhibitor, SC-558 (43), show that these inhibitors interact respectively with COX-2 and COX-1 in a very similar way. The r(2) values of 0.648 for COX-2 and 0.752 for COX-1 indicate that the calculated binding free energies correlate well with the inhibitory activities. The structural and energetic differences in inhibitory potencies of 1,5-diarylpyrazoles were reasonably explored, and the COX-2/COX-1 selectivity was demonstrated by the three-dimensional (3D) interaction models of inhibitors complexing with these two enzymes. Using the binding conformations of 1,5-diarylpyrazoles, consistent and highly predictive 3D quantitative structure-activity relationship (QSAR) models were developed by performing comparative molecular field analyses (CoMFA) and comparative molecular similarity analyses (CoMSIA). The q(2) values are 0.635 and 0.641 for CoMFA and CoMSIA models, respectively. The predictive ability of these models was validated by SC-558 (43) and a set of 10 other compounds that were not included in the training set. Mapping these models back to the topology of the active site of COX-2 leads to a better understanding of vital diarylpyrazole compounds and COX-2 interactions. Structure-based investigations and the final 3D QSAR results provided possible guidelines and accurate activity predictions for novel inhibitor design.     

Structure-selectivity investigations of D2-like receptor ligands by CoMFA and CoMSIA guiding the discovery of D3 selective PET radioligands

Elucidation of the physiological role of the D3 receptor and its distribution in the brain using positron emission tomography (PET) is hampered by the lack of bioavailable subtype selective tracer ligands. To develop appropriate D3 radioligands, we designed an integrative procedure involving the elucidation of structural features determining D3 selectivity over both congeners D2 and D4 by comparative molecular analysis. Thus, we have successfully generated CoMFA and CoMSIA models based on the affinitiy differences of a series of 79 ligands representing a broad range of selectivities. These models yielded highly significant cross-validations (q2cv(D3/D2) = 0.86; q2cv(D3/D4) = 0.92) and excellent predictions of a 16-ligand test set (r2pred = 0.79-0.93). Exploiting this information, synthesis and receptor binding studies directed us to the fluorinated lead compounds 78 and 79, featuring subnanomolar D3 affinities and considerable selectivities over D2 and D4 and, subsequently, to the subtype selective PET tracers [18F]78 and [18F]79.     

Non-nucleoside inhibitors of NS5B polymerase binding to allosteric sites: 3D- QSAR and molecular docking studies

Infection caused by hepatitis C virus (HCV) is a significant world health problem for which novel therapies are in urgent demand. Nonstructural (NS5B) viral proteins have emerged as an attractive target for drug discovery efforts toward antiviral for hepatitis C virus. Toward this target several series of NS5B inhibitors that showed activity in the replicon assay have been reported. In this article, we gave a report of the NS5B allosteric sites and the corresponding non-nucleoside inhibitors, which belong to different chemical classes. Then using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods, 3-dimension quantitative structure-activity relationships (3D-QSAR) models have been built with more than two hundred benzimidazole/indole derivative inhibitors. These studies indicated that the QSAR models were statistically significant and had high predictabilities (CoMFA: q(2)=0.823, r(2)=0.942; CoMSIA: q(2)=0.817, r(2)=0.935). The flexible docking method, which was performed by the DOCK6.0 software, positioned all of the inhibitors into the allosteric site to determine the probable binding conformation. The CoMFA and CoMSIA models based on the docking conformations also yielded statistically significant and high predictive QSAR models (CoMFA: q(2)=0.509, r(2)=0.768; CoMSIA: q(2)=0.582, r(2)=0.854). Our models would offer help to better comprehend the structure-activity relationships existent for this class of compounds and also facilitate the design of new inhibitors with good chemical diversity.     

3D QSAR analyses-guided rational design of novel ligands for the (alpha4)2(beta2)3 nicotinic acetylcholine receptor

Three-dimensional quantitative structure-activity relationship methods, the comparative molecular field analysis (CoMFA) and the comparative molecular similarity indices analysis (CoMSIA), were applied using a training set of 45 ligands of the (alpha4)2(beta2)3 nicotinic acetylcholine receptor (nAChR). All compounds are related to (-)-epibatidine, (-)-cytisine, (+)-anatoxin-a, and (-)-ferruginine, and additionally, novel diazabicyclo[4.2.1]nonane- and quinuclidin-2-ene-based structures were included. Their biological data have been determined by utilizing the same experimental protocol. Statistically reliable models of good predictive power (CoMFA r2 = 0.928, q2 = 0.692, no. of components = 3; CoMSIA r2 = 0.899, q2 = 0.701, no. of components = 3) were achieved. The results obtained were graphically interpreted in terms of field contribution maps. Hence, physicochemical determinants of binding, such as steric and electrostatic and, for the first time, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor properties, were mapped back onto the molecular structures of a set of nAChR modulators. In particular, changes in the binding affinity of the modulators as a result of modifications in the aromatic ring systems could be rationalized by the steric, electrostatic, hydrophobic, and hydrogen bond acceptor properties. These results were used to guide the rational design of new nAChR ligands such as 48-52 and 54, which were subsequently synthesized for the first time and tested. Key steps of our synthetic approaches were successfully applied Stille and Suzuki cross-coupling reactions. Predictive r2 values of 0.614 and 0.660 for CoMFA and CoMSIA, respectively, obtained for 22 in part previously unknown ligands for the (alpha4)2(beta2)3 subtype, demonstrate the high quality of the 3D QSAR models.     

Quantitative structure-activity relationship of human neutrophil collagenase (MMP-8) inhibitors using comparative molecular field analysis and X-ray structure analysis.

A set of 90 novel 2-(arylsulfonyl)-1,2,3, 4-tetrahydroisoquinoline-3-carboxylates and -hydroxamates as inhibitors of the matrix metalloproteinase human neutrophil collagenase (MMP-8) was designed, synthesized, and investigated by 3D-QSAR techniques (CoMFA, CoMSIA) and X-ray structure analysis. Docking studies of a reference compound are based on crystal structures of MMP-8 complexed with peptidic inhibitors to propose a model of its bioactive conformation. This model was validated by a 1. 7 A X-ray structure of the catalytic domain of MMP-8. The 3D-QSAR models based on a superposition rule derived from these docking studies were validated using conventional and cross-validated r2 values using the leave-one-out method, repeated analyses using two randomly chosen cross-validation groups plus randomization of biological activities. This led to consistent and highly predictive 3D-QSAR models with good correlation coefficients for both CoMFA and CoMSIA, which were found to correspond to experimentally determined MMP-8 catalytic site topology in terms of steric, electrostatic, and hydrophobic complementarity. Subsets selected as smaller training sets using 2D fingerprints and maximum dissimilarity methods resulted in 3D-QSAR models with remarkable correlation coefficients and a high predictive power. This allowed to compensate the weaker zinc binding properties of carboxylates by introducing optimal fitting P1' residues. The final QSAR information agrees with all experimental data for the binding topology and thus provides clear guidelines and accurate activity predictions for novel MMP-8 inhibitors.     

Three-dimensional quantitative structure-activity relationships and activity predictions of human TRPV1 channel antagonists: comparative molecular field analysis and comparative molecular similarity index analysis of cinnamides

3D-QSAR models for human TRPV1 channel antagonists were developed based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA), using a training set of 61 cinnamide TRPV1 antagonists and tested on an independent test set of 47 antagonists. Molecular alignment procedure included weights for both internal energy and atom-to-atom matching against a reference or probe. Sensitivity of results on partial charge assignments was explored using multiple charge sets. AM1-BCC charge assignments gave better results for both CoMFA and CoMSIA models. For the best CoMFA model, the statistics are, r2 = 0.96, q2 = 0.58, n = 61 for the training set and r2 = 0.50, n = 47 for the test set. For the best CoMSIA model, the statistics are r2 = 0.95, q2 = 0.57, n = 61 for the training set and r2 = 0.48, n = 47 for the test set. These models are consistent with the proposed binding modes and interactions of known activators of the TRPV1 channel such as capsaicin, in a structural model of the TM3/4 helical region of TRPV1.     

3D QSAR studies on 5-(2-methylbenzimidazol-1-yl)-N-alkylthiophene-2-carboxamide derivatives as P. falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors

Malaria is currently one of the world’s most severe endemic diseases, responsible for majority of morbidity and mortality. A large number of drugs are available for its treatment; however, the development of resistance has become more widespread with most of the frontline drug therapies. Inhibitors of PfDHODH have proven efficacy for the treatment of malaria. 3D QSAR studies on some 5-(2-methylbenzimidazol-1-yl)-N-alkylthiophene-2-carboxamide derivatives as PfDHODH inhibitors were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The alignment strategy was used for these compounds by means of Distill function defined in SYBYL x 1.2. The best CoMFA and CoMSIA models obtained for the training set were statistically significant with q ² of 0.669 and 0.727, cross-validated coefficient (r ² _cv) of 0.603 and 0.698, and conventional coefficients (r ²) of 0.971 and 0.966, respectively. Both the models were validated by an external test set of five compounds giving satisfactory prediction (r ² _pred) of 0.799 and 0.815 for CoMFA and CoMSIA models, respectively. Further the robustness of the model was verified by bootstrapping analysis. Generated CoMFA and CoMSIA models provide useful information for the design of novel inhibitors with better PfDHODH inhibitory activity.