Comparative Molecular Similarity Indices Analysis Analyses Research Articles

Insights into the Structural Requirements of Potent Brassinosteroids as Vegetable Growth Promoters Using Second-Internode Elongation as Biological Activity: CoMFA and CoMSIA Studies.

In the present study, we have employed the ligand-based drug design technique, 3D-QSAR, through a comparative molecular field analysis (CoMFA) and a comparative molecular similarity indices analysis (CoMSIA) to determine the key factors for the plant growth promoting activity of brassinosteroids reported in literature, using the bean second-internode bioassay measured on two groups of compounds with different molar concentrations. This is the first 3D-QSAR study using the second internode elongation as biological activity. These results provide useful ideas for the design of new molecules, which could be explored in the future to identify novel vegetable growth promoters with similar or greater biological activity than natural brassinosteroids. The reliability of this study was supported by the robust statistical parameters obtained from CoMFA (Model A, r2pred = 0.751; Model B, r2pred = 0.770) and CoMSIA (Model A, r2pred = 0.946; Model B, r2pred = 0.923) analysis.

CoMFA, CoMSIA analysis of 4-[5-(4-Fluoro-benzyl-1H-pyrazol-3-yl]-pyridine derivatives as CYP3A4 inhibitors

3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) studies were performed on a series of 4-[5-(4-Fluoro-benzyl-1H-pyrazol-3-yl]-pyridine derivatives as CYP3A4 inhibitors. The molecular superimposition of most active compound structure was performed by atom/shape-based root mean square (RMS) fit. The statistically significant model was established from 95 molecules; which were validated by evaluation of test set of 24 compounds and training set of 71 compounds. The atom-based RMS alignment yielded best predictive comparative molecular field analysis (CoMFA) model q2 = 0.784; r2 = 0.974; F-value = 479.682 with five components; while the comparative molecular similarity indices analysis (CoMSIA) model yielded q2 = 0.797; r2 = 0.987; F-value = 791.297 with six components. Contour maps obtained from 3D-QSAR CoMFA; CoMSIA studies were evaluated for the biological activity trends of the molecules analysed. The statistical analysis results indicate that the steric; electrostatic; hydrogen bond donor and acceptor substituents play a significant role in CYP3A4 activity. The contribution of the ligand-based study approach is expected to become more significant and effective in the future.

CoMFA, CoMSIA analysis of 4-[5-(4-Fluoro-benzyl-1H-pyrazol-3-yl]-pyridine derivatives as CYP3A4 inhibitors

3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) studies were performed on a series of 4-[5-(4-Fluoro-benzyl-1H-pyrazol-3-yl]-pyridine derivatives as CYP3A4 inhibitors. The molecular superimposition of most active compound structure was performed by atom/shape-based root mean square (RMS) fit. The statistically significant model was established from 95 molecules; which were validated by evaluation of test set of 24 compounds and training set of 71 compounds. The atom-based RMS alignment yielded best predictive comparative molecular field analysis (CoMFA) model q2 = 0.784; r2 = 0.974; F-value = 479.682 with five components; while the comparative molecular similarity indices analysis (CoMSIA) model yielded q2 = 0.797; r2 = 0.987; F-value = 791.297 with six components. Contour maps obtained from 3D-QSAR CoMFA; CoMSIA studies were evaluated for the biological activity trends of the molecules analysed. The statistical analysis results indicate that the steric; electrostatic; hydrogen bond donor and acceptor substituents play a significant role in CYP3A4 activity. The contribution of the ligand-based study approach is expected to become more significant and effective in the future.

Comprehensive Modeling and Discovery of Mebendazole as a Novel TRAF2- and NCK-interacting Kinase Inhibitor

TRAF2- and NCK-interacting kinase (TNIK) represents one of the crucial targets for Wnt-activated colorectal cancer. In this study, we curated two datasets and conducted a comprehensive modeling study to explore novel TNIK inhibitors with desirable biopharmaceutical properties. With Dataset I, we derived Comparative Molecular Similarity Indices Analysis (CoMSIA) and variable-selection k-nearest neighbor models, from which 3D-molecular fields and 2D-descriptors critical for the TNIK inhibitor activity were revealed. Based on Dataset II, predictive CoMSIA-SIMCA (Soft Independent Modelling by Class Analogy) models were obtained and employed to screen 1,448 FDA-approved small molecule drugs. Upon experimental evaluations, we discovered that mebendazole, an approved anthelmintic drug, could selectively inhibit TNIK kinase activity with a dissociation constant Kd = ~1 μM. The subsequent CoMSIA and kNN analyses indicated that mebendazole bears the favorable molecular features that are needed to bind and inhibit TNIK.

Pharmacophore-Based Comparative Molecular Similarity Indices Analysis of CRTh2 Antagonists

Chemoattractant Receptor Homologous molecule expressed on Th2 cells (CRTh2) is a chemoattractant receptor with seven transmembrane helices targeted for inflammatory diseases such as asthma and allergic rhinitis. In this study, pharmacophore based Comparative Molecular Similarity Indices Analysis (CoMSIA) were performed on the series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids derivatives. Initially, GASP module was used for generation of pharmacophore models using five highly active compounds from the dataset. Among the generated pharmacophores, the best pharmacophore model was selected based on fitness score and was used as template for the alignment of compounds which was used for CoMSIA analysis. The best predictions were obtained utilizing steric, hydrophobic and H-bond acceptor parameters showing a <TEX>$q^2$</TEX>=0.559 and <TEX>$r^2$</TEX>=0.730. 15 test set compounds was used to investigate the predictive ability of the CoMSIA model. Contour maps suggested that presence of bulky substituents and H-bond acceptor atoms at <TEX>$5^{th}$</TEX> position of benzene ring will increase the activity of the compounds. The results obtained from this study will be useful to design more potent CRTh2 antagonists.

Prediction of octanol-air partition coefficients for polychlorinated biphenyls (PCBs) using 3D-QSAR models

Based on the experimental data of octanol-air partition coefficients (KOA) for 19 polychlorinated biphenyl (PCB) congeners, two types of QSAR methods, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), are used to establish 3D-QSAR models using the structural parameters as independent variables and using logKOA values as the dependent variable with the Sybyl software to predict the KOA values of the remaining 190 PCB congeners. The whole data set (19 compounds) was divided into a training set (15 compounds) for model generation and a test set (4 compounds) for model validation. As a result, the cross-validation correlation coefficient (q2) obtained by the CoMFA and CoMSIA models (shuffled 12 times) was in the range of 0.825–0.969 (>0.5), the correlation coefficient (r2) obtained was in the range of 0.957–1.000 (>0.9), and the SEP (standard error of prediction) of test set was within the range of 0.070–0.617, indicating that the models were robust and predictive. Randomly selected from a set of models, CoMFA analysis revealed that the corresponding percentages of the variance explained by steric and electrostatic fields were 23.9% and 76.1%, respectively, while CoMSIA analysis by steric, electrostatic and hydrophobic fields were 0.6%, 92.6%, and 6.8%, respectively. The electrostatic field was determined as a primary factor governing the logKOA. The correlation analysis of the relationship between the number of Cl atoms and the average logKOA values of PCBs indicated that logKOA values gradually increased as the number of Cl atoms increased. Simultaneously, related studies on PCB detection in the Arctic and Antarctic areas revealed that higher logKOA values indicate a stronger PCB migration ability. From CoMFA and CoMSIA contour maps, logKOA decreased when substituents possessed electropositive groups at the 2-, 3-, 3′-, 5- and 6- positions, which could reduce the PCB migration ability. These results are expected to be beneficial in predicting logKOA values of PCB homologues and derivatives and in providing a theoretical foundation for further elucidation of the global migration behaviour of PCBs.

CoMFA and CoMSIA analysis of ACE-inhibitory, antimicrobial and bitter-tasting peptides

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to the ACE-inhibitory, antimicrobial, and bitter-tasting peptides. Predictive 3D-QSAR models were established using SYBYL multifit molecular alignment rule over a training set and a test set. The optimum models were all statistically significant with cross-validated coefficients (Q2) >0.5 and conventional coefficients (R2) >0.9, indicating that they were reliable enough for activity prediction. The obtained results may aid in the design of novel bioactive peptides.

Docking, CoMFA, and CoMSIA analyses of phenoxy triazole derivatives as enoyl-ACP reductase inhibitors for Escherichia coli

Docking experiments on the published crystal structure of enoyl-ACP reductase (1C14.PDB) with potent phenoxy triazoles, phenoxy oxadiazoles, and fused phenoxy triazole compounds have been described to indicate that {2-[(4-amino-5-thioxo-4,5-dihydro-1H-1,2,4-triazol-3-yl)methoxy]-4-bromophenyl}(phenyl)methanone (compound 2) binds to the enzyme active site. The methoxy and methyl groups seem to play a key role in the binding as well as its biological activity as shown by the structure-activity relationship studies reported for analogs of phenoxy triazole. Furthermore, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed based on the binding conformation predicted by molecular docking. The predicted CoMFA/CoMSIA models showed the satisfactory statistical significance with a good predicted power. The information gained from molecular docking and 3D-QSAR studies gives us a valuable clue for optimizing the lead molecules.

CoMSIA and POM analyses of anti-malarial activity of synthetic prodiginines

In present work, 53 synthetic prodiginines were selected to establish thriving CoMSIA (Comparative Molecular Similarity Indices Analysis) model to explore the structural features influencing their anti-malarial activity. POM (Petra/Osiris/Molinspiration) was carried out to get insight into requirements that can lead to the improvement of the activity of these molecules. The CoMSIA model, based on a combination of steric, electrostatic and H-bond acceptor/donor effects, is with R2cv=0.738 and R2=0.911. The analyses reveal that lipophilicity, hydrogen donor/acceptor and steric factors play crucial role. The study with constructive propositions could be useful for the design of new analogues with enhanced activity.

Docking-based CoMFA and CoMSIA analyses of tetrahydro-β-carboline derivatives as type-5 phosphodiesterase inhibitors

Tetrahydro-β-carboline derivatives (THBCs) have been identified as a class of potent Type-5 Phosphodiesterase (PDE5) inhibitors, showing benefits for the treatment of erectile dysfunction and also bearing anticancer properties. A computational strategy based on molecular docking studies, followed by docking-based Comparative Molecular Fields Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA), has been used to elucidate the atomic details of the PDE5/THBC interactions and to identify the most important features impacting the THBC PDE5 inhibitory activity. The final CoMSIA model resulted to be the more predictive, showing rncv2 = 0.96, rcv2 = 0.688, SEE = 0.248, F = 104.800, and r2pred = 0.78. The results allowed us to obtain useful information for the design of new THBC analogues, potentially acting as PDE5 inhibitors, and to predict their potency prior to synthesis.

CoMFA and CoMSIA analysis of tetrahydroquinolines as potential antimalarial agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used on a dataset of compounds, some of them having been reported to inhibit Plasmodium falciparum protein, farnesyltransferase. The co-crystal structure of the lead molecule, BMS–214662 bound to Rat-PFT was used as a template. CoMFA yielded a good model, with = 0.909, = 0.617 and was validated using an external set ( = 0.748). It compared favourably with CoMSIA. In the CoMFA model the steric and electrostatic fields exerted an almost equal influence on activity. The contour maps indicated the necessity for sterically large electropositive groups with electronegative tail to be present in these molecules for activity, and sterically large electronegative moieties on the sulfonamide linker. By incorporating these features some new compounds have been identified for further investigation.

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis – a case study on HIV-1 protease inhibitors

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r (loo) (2) values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r(2) values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r (pred) (2) ) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r (pred) (2) of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.

3,4,5-Trisubstituted-1,2,4-4H-triazoles as WT and Y188L mutant HIV-1 non-nucleoside reverse transcriptase inhibitors: docking-based CoMFA and CoMSIA analyses

3,4,5-Trisubstituted-1,2,4-4H-triazoles (TTs) have recently been identified as a new class of potent non-nucleoside HIV-1 reverse transcriptase (RT) inhibitors. Two series of triazoles have been studied, one of which was also screened against the Y188L mutant. A computational strategy based on molecular docking studies followed by comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) has been used to elucidate the atomic details of the RT/TT interactions and to identify the most important features impacting the TT antiretroviral activity. Two 3D-QSAR CoMFA and CoMSIA models were derived, using the TT pEC₅₀ values measured against wild-type (WT) HIV-1 (model A) and the Y188L mutant form (model B), respectively, as the dependent variable. The final model A CoMSIA (r(ncv)² = 0.97, r(cv)² = 0.89, SEE = 0.314, and r(pred)² = 0.82) and model B CoMSIA (r(ncv)² = 0.91, r(cv)² = 0.61, SEE = 0.236, and r(pred)² = 0.73) analyses were more predictive. The results allowed us to obtain useful information for the design of new compounds with improved potency towards WT HIV-1 or that are potentially active against the Y188L mutant.

CoMFA and CoMSIA 3D-QSAR studies on quionolone caroxylic acid derivatives inhibitors of HIV-1 integrase

A series of quionolone caroxylic acid derivatives inhibitors of HIV-1 integrase were subjected to three-dimensional quantitative structure–activity relationship (3D-QSAR) studies using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The CoMFA model includes steric and electrostatic fields for the training set with the cross-validated q 2 value of 0.67 and the non-cross-validated r 2 value of 0.98. The cross-validated q 2 value of CoMSIA Model is 0.76 and the non-cross-vaildated r 2 value is 0.99. From the cross-validated results, it can be seen that the CoMSIA model has a better predictive ability than CoMFA model. Based on the above results, the CoMFA and CoMSIA analyses can be used in the design of more potent HIV-1 integrase inhibitors.

CoMFA and CoMSIA analyses on 1,2,3,4-tetrahydropyrrolo[3,4-b]indole and benzimidazole derivatives as selective CB2 receptor agonists

Novel classes of cannabinoid 2 receptor (CB2) agonists based on 1,2,3,4-tetrahydropyrrolo[3,4-b]indole and benzimidazole scaffolds have shown high binding affinity toward CB2 receptor and good selectivity over cannabinoid 1 receptor (CB1). A computational study of comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed, initially on each series of agonists, and subsequently on all compounds together, in order to identify the key structural features impacting their binding affinity. The final CoMSIA model resulted to be the more predictive, showing cross-validated r2 (r(cv)2) = 0.680, non cross-validated r2 (r(ncv)2) = 0.97 and test set r²(r²(pred) = 0.93. The study provides useful suggestions for the design of new analogues with improved affinity.

CoMFA and CoMSIA studies on HIV-1 attachment inhibitors

A series of human immunodeficiency virus type 1 (HIV-1) attachment inhibitors were subjected to ligand-based three-dimensional quantitative structure–activity relationship (3D-QSAR) studies using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The CoMFA model yielded satisfactory statistical data with the cross-validated q 2 value of 0.589 and the non-cross-validated r 2 value of 0.963. The cross-validated q 2 value of CoMSIA Model was 0.621 and the non-cross-validated r 2 value was 0.972. From the cross-validated results, it can be seen that the CoMSIA model has a better predictive ability than CoMFA model. Based on the above results, the CoMFA and CoMSIA analyses can be used in the design of more potent HIV-1 attachment inhibitors.

CoMFA and CoMSIA analyses on 4-oxo-1,4-dihydroquinoline and 4-oxo-1,4-dihydro-1,5-, -1,6- and -1,8-naphthyridine derivatives as selective CB2 receptor agonists

Novel classes of CB2 agonists based on 4-oxo-1,4-dihydroquinoline and 4-oxo-1,4-dihydro-1,5-, -1,6- and -1,8-naphthyridine scaffolds have shown high binding affinity toward CB2 receptor and good selectivity over CB1. A computational study of comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed, in order to identify the key structural features impacting their binding affinity. The final CoMSIA model resulted to be the more predictive, showing r(2)ncv=0.84, r(2)cv=0.619, SEE = 0.369, and r(2)pred=0.75. The study provides useful suggestions for the synthesis of new selective analogues with improved affinity.

The Effect of Molecular Fields, Lattice Spacing and Analysis Options on CoMFA Predictive Ability

AbstractComparative Molecular Field Analysis (CoMFA) is a popular Three‐Dimensional Quantitative Structure–Activity Relationship (3D‐QSAR) method. The effect of varying molecular fields [CoMFA vs. Comparative Molecular Similarity Indices Analysis (CoMSIA)], lattice spacing and analysis options on predictive performance was assessed based on cross validated R2 values of 30 datasets taken from the literature. The CoMFA method results in statistically significantly higher cross validated R2 values compared to CoMSIA when only steric and electrostatic fields are used. When the hydrophobic molecular field is included in the CoMSIA analysis (as is most commonly the case) the difference between CoMFA and CoMSIA is no longer statistically significant. Addition of hydrogen bond field does not improve CoMFA predictivity. Although there was a trend towards increased predictivity with decreased lattice spacing, this was not statistically significant. Altering the default filtering criteria and cut‐off values for Lennard Jones and Coulomb fields also did not generally result in a statistically significant effect on predictive performance.

3D-QSAR and docking studies of estrogen compounds based on estrogen receptor β

Close attention has been paid to estrogen compounds because these chemicals may pose a serious threat to the health of humans and wildlife. Estrogen receptor (ER) exists as two subtypes, ERα and ERβ. The difference in amino acids sequence of the binding sites of ERα and ERβ might lead to a result that some synthetic estrogens and naturally occurring steroidal ligands have different relative affinities and binding modes for ERα and ERβ. In this investigation, comparative molecular similarity indices analysis (CoMSIA) was performed on 50 estrogen compounds binding ERβ to find out the structural relationship with the activities. We also compared two alignment schemes employed in CoMSIA analysis, namely, atom-fit and receptor-based alignment, with respect to the predictive capability of their respective models for structurally diverse data sets. The model with the significant correlation and the best predictive power (R2=0.961, qLOO2 =0.671, RPred2 =0.722) was achieved. The CoMSIA and docking results revealed the structural features related to an activity and provided an insight into molecular mechanisms of estrogenic activities for estrogen compounds.

Acylthiocarbamates as non-nucleoside HIV-1 reverse transcriptase inhibitors: docking studies and ligand-based CoMFA and CoMSIA analyses

Acylthiocarbamates (ATCs) have been identified as a class of potent non-nucleoside HIV-1 reverse transcriptase (RT) inhibitors. A computational strategy based on molecular docking studies followed by comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was used to identify the most important features impacting ATC antiretroviral activity. The CoMSIA model proved to be the more predictive, with r(2)(ncv) = 0.89, r(cv)(2) = 0.38, standard error of estimate (SEE) = 0.494, F = 84, and r(2)(pred) = 0.81. The results of these studies will be useful in designing new ATCs with improved potency, also against clinically relevant resistant mutants.