Molecular Field Analysis Model Research Articles

Investigations of retinoic acid receptor-related orphan receptor-gamma t (RORγt) agonists: a combination of 3D-QSAR, molecular docking and molecular dynamics

Retinoic acid receptor-related orphan receptor-gamma t (RORγt) is an attractive target for Th17-driven autoimmune diseases. In the present work, a series of RORγt agonists were investigated by a molecular modeling study combining three-dimensional quantitative structure activity relationship (3D-QSAR), molecular docking, molecular dynamics (MD) simulations and binding free energies to get insight into the molecular features that would promote binding activity. The optimum comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for 3D-QSAR studies possess satisfactory predictive ability, with R2 cv=0.615, R2 pred=0.8702 for CoMFA, and R2 cv=0.670, R2 pred=0.7683 for CoMSIA model, respectively. In addition, molecular docking studies, molecular dynamics simulations and binding free energies were used to find the actual conformations of compounds in the active site of RORγt, and key residues GLN-286, LEU-287, HIS-323 and ARG-367 for higher binding activity were pointed out. The predicted models will help us to understand the structural requirements of RORγt agonists for the designing of better active compounds. Communicated by Ramaswamy H. Sarma

Structural characterization of plasmodial aminopeptidase: a combined molecular docking and QSAR-based in silico approaches

Aminopeptidase M1 (PfAM1) is one of the key enzymes involved in the development of new antimalarials. To accelerate the discovery of inhibitors with selective activity against PfAM1 and microsomal neutral aminopeptidase (pAPN), in the present work, the optimum comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were built based on PfAM1 and pAPN inhibitors. The results of the developed 3D-QSAR models were as follows: PfAM1/CoMFA: [Formula: see text] = 0.740, [Formula: see text] = 0.7781; PfAM1/CoMSIA: [Formula: see text] = 0.740, [Formula: see text] = 0.7354; pAPN/CoMFA: [Formula: see text] = 0.612, [Formula: see text] = 0.7318; pAPN/CoMSIA: [Formula: see text] = 0.609, [Formula: see text] = 0.7480, and the models derived from MLR, PLSR and SVR methods provided high R2 values of 0.6960, 0.6965, 0.7971 for PfAM1, 0.7700, 0.7697, 0.8228 for pAPN and Q2 of 0.7004, 0.7004, 0.5632 for PfAM1, 0.7551, 0.7566 and 0.8394 for pAPN, respectively, indicating that the developed 3D-QSAR and 2D-QSAR models possess good ability for prediction of the relative compound activities. Furthermore, all inhibitors were docked into the active site of the PfAM1 and pAPN receptors, the hydrogen-bond interactions between the compound 33 with Glu497, Glu463 and Arg489 of the PfAM1, and the compound 4 with Ala348, Glu384 and Phe467 of the receptor pAPN are able to help to stabilize the conformation. The above results would provide helpful clues to predicting the binding activity of novel inhibitors and the foundation for understanding the interaction mechanism between the inhibitors and the receptors.

3D-QSAR and docking studies on ursolic acid derivatives for anticancer activity based on bladder cell line T24 targeting NF-kB pathway inhibition

Bladder cancer is the common reason for mortality worldwide, and its increasing rate announces as a significant area of research in drug designing. The side effects and toxicity of existing drugs and the consequence of gradual cancer cell resistance against the available therapy make the treatment poor. Globally, there is a continuous high demand to develop new, more potent, and easily affordable drugs against cancer. The current research article illustrates the application of developed three-dimensional quantitative structure-activity relationship (3D-QSAR) based on human bladder cancer cell line T24 in vitro anticancer activity. The derived QSAR model has been used for prediction of natural compounds and analogs with 80% similarity of the most active compound of the dataset. The developed model describes the structure-activity relationship for terpenes and their derivatives at the molecular level. The developed comparative molecular field analysis (CoMFA) model shows a satisfactory cross-validation correlation coefficient (q2) of 0.54 and a regression correlation coefficient (r2) of 0.86. In order to evaluate the compliance with electronic pharmacokinetic parameters, Lipinski’s rule of five filter, absorption, distribution, metabolism, and excretion (ADME) and toxicity of predicted compounds have been calculated. Furthermore, molecular-docking study has been performed to prioritize these predicted compounds based on their docking score and binding pocket similarity through the identified potential anticancer targets. Finally, two compounds T9 and B42 have been identified as the best hit because these two fall within the standard limits of all filters and show a good binding affinity. Conclusively, all satisfactory results strongly suggest that the derived 3D-QSAR model and obtained candidate’s binding structures are reasonable in the prediction of a new antagonist’s activity. The strategy adopted in the present research is expected to be of immense importance and a great support in the identification and optimization of lead in the early and advance drug discovery.

Insights into the interaction and binding mode of a set of antifungal azoles as inhibitors of potential fungal enzyme-based targets.

Azole-containing compounds are a kind of chemical entities of natural and synthetic origin having a wide-range of activities. They are therefore considered as important moieties for fungicide development, mostly due to the possible action on several enzyme-based targets. As part of our research on fungicidal agents, the relationship between the ligand-enzyme affinities of several synthetic azole-containing compounds against a set of fungal enzyme-based targets was in silico evaluated through molecular docking. The affinity values of the test compounds were mostly higher than those of the respective test controls. Binding modes between enzymes and test compounds were firstly investigated through Vina scores and ligand-residue interactions. Furthermore, statistically relationships among docking scores were successfully found by multivariate analysis. They were mostly correlated with reported MIC80 values, so it denoted an evident discrimination of the test compounds. Strong electron withdrawing groups on phenylacrylamide moiety were responsible for establishing stronger complexes with the enzyme targets, being trichodiene synthase and α-L-fucosidase the most important ones. Moreover, stability of a set of representative protein/ligand complexes was also analyzed by 10ns molecular dynamics simulations (MD). Significant differences into the MD runs were detected and directly correlated to docking performances. Finally, docking affinity scores and HOMO-LUMO energy gaps resulted well predicted by comparative molecular field analysis (CoMFA) models, demonstrating the structure type is particularly associated with those calculated properties and these results were thus consistent with the respective validation parameters.

Exploration of structural and physicochemical properties of small molecules to inhibit NMDA functionality

The N-methyl-D-aspartate (NMDA) is the family of glutamate receptor, which is involved in controlling synaptic plasticity and memory function; but overactivation of this receptor results to excess intracellular calcium formation, triggers neuronal injury and also involves in several pathologies. Both ligand- and structure-based quantitative structure-activity relationship (QSAR), pharmacophore, docking and simulation studies have been performed on a set of structurally diverse inhibitors to explore prime molecular structural features involve for specific binding to NMDA, and vis-a-vis inhibiting enzyme activity. 3D QSAR studies, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models showed the importance of steric, electrostatic and hydrophobic features; while hydrogen bond acceptor and hydrophobic features are depicted as important pharmacophore features of the molecule. Molecular docking and simulation studies corroborated the consequence of the features obtained from ligand-based Bayesian model (AUROCcv = 0.878); 3D QSAR CoMFA (R2 = 0.895, se = 0.513, Q2 = 0.602, R2pred = 0.673); CoMSIA (R2 = 0.877, se = 0.555, Q2 = 0.615, R2pred = 0.727); hologram QSAR (Q2 = 0.812, R2 = 0.941, R2pred = 0.772), and pharmacophore models (Q2 = 0.926, R2 = 0.927, R2pred = 0.621). Presence of aromatic ring, hetero and halogen atoms along with alkyl group of molecular scaffold shows their importance for binding affinity to NMDA receptor. Stability of the complex is adjudged by both docking and simulation studies.

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.

Molecular modeling study for the design of novel acetyl-CoA carboxylase inhibitors using 3D QSAR, molecular docking and dynamic simulations

Acetyl-CoA carboxylase (ACC) enzyme plays an important role in the regulation of biosynthesis and oxidation of fatty acids. ACC is a recognized drug target for the treatment of obesity and diabetes. Combination of ligand and structure-based in silico methods along with activity and toxicity prediction provides best lead compounds in the drug discovery process. In this study, a data-set of 100 ACC inhibitors were used for the development of comparative molecular field analysis (CoMFA) and comparative molecular similarity index matrix analysis (CoMSIA) models. The generated contour maps were used for the design of novel ACC inhibitors. CoMFA and CoMSIA models were used for the predication of activity of designed compounds. In silico toxicity risk prediction study was carried out for the designed compounds. Molecular docking and dynamic simulations studies were performed to know the binding mode of designed compounds with the ACC enzyme. The designed compounds showed interactions with key amino acid residues important for catalysis, and good correlation was observed between binding free energy and inhibition of ACC.

Development of a credible 3D-QSAR CoMSIA model and docking studies for a series of triazoles and tetrazoles containing 11β-HSD1 inhibitors

Type 2 diabetes mellitus is described by insulin resistance and high fasting blood glucose. Increased levels of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) enzyme result in insulin resistance and metabolic syndrome. Inhibition of 11β-HSD1 decreases glucose production and increases hepatic insulin sensitivity. Use of selective 11β-HSD1 inhibitors could prove to be an effective strategy for the treatment of the disease. It was decided to identify the essential structural features required by any compound to possess 11β-HSD1 inhibitory activity. A dataset of 139 triazoles and tetrazoles having 11β-HSD1 inhibitory activity was used for the development of a 3D-QSAR model. The best comparative molecular field analysis (CoMFA) model was generated with databased alignment, which was further used for comparative molecular similarity indices analysis (CoMSIA). The optimal CoMSIA model showed = 0.809 with five components, = 0.931, SEE = 0.323 and F-value = 249.126. The CoMSIA model offered better prediction than the CoMFA model with = 0.522 and 0.439, respectively, indicating that the CoMSIA model appeared to be a better one for the prediction of activity for the newly designed 11β-HSD1 inhibitors. The selectivity aspect of 11β-HSD1 over 11β-HSD2 was studied with the help of docking studies.

Synthesis, Antiplatelet Aggregation Activity Evaluation and 3D‐QSAR of a Series of Novel 6‐Alkylamino(Alkoxyl)‐2‐Propylthio‐8‐Azapurine Nucleosides

A series of novel 6‐alkylamino(alkoxyl)‐2‐propylthio‐8‐azapurine nucleosides were synthesized by an improved route, and the human antiplatelet aggregation activities of these new compounds were evaluated. A self‐organizing molecular field analysis method was used to study the three‐dimensional quantitative structure–activity relationship of these novel nucleosides. The results of the antiplatelet aggregation activity evaluation and analysis of the self‐organizing molecular field analysis models through shape and electrostatic grids may provide a basis for the development of new and potent antiplatelet agents.

A selectivity study of sodium-dependent glucose cotransporter 2/sodium-dependent glucose cotransporter 1 inhibitors by molecular modeling.

Sodium-dependent glucose cotransporters (SGLTs) play an important role in glucose reabsorption in the kidney and have been identified as promising targets to treat diabetes. Because of the side effects like glucose and galactose malabsorption by targeting SGLT1, highly selective SGLT2 inhibitors are more promising in the treatment of diabetes. To understand the mechanism of selectivity, we conducted selectivity-based three-dimensional quantitative structure-activity relationship studies to highlight the structure requirements for highly selective SGLT2 inhibitors. The best comparative molecular field analysis and comparative molecular similarity indices analysis models showed the noncross-validated coefficient (r(2) ) of 0.967 and 0.943, respectively. The predicted correlation coefficients (r(2) pred ) of 0.974 and 0.938 validated the reliability and predictability of these models. Besides, homology models of SGLT2 and SGLT1 were also constructed to investigate the selective mechanism from structure-based perspective. Molecular dynamics simulation and binding free energy calculation were performed on the systems of a potent and selective compound interacting with SGLT2 and SGLT1 to compare the different binding modes. The simulation results showed that the stretch of the methylthio group on Met241 had an essential effect on the different binding modes between SGLT1 and SGLT2, which was consistent with the three-dimensional quantitative structure-activity relationship analysis. Hydrogen bond analysis and binding free energy calculation revealed that SGLT2 binding complex was more stable and favorable than SGLT1 complex, which was highly correlated with the experimental results. Our obtained results give useful information for the investigation of the inhibitors' selectivity between SGLT2 and SGLT1 and will help for further development of highly selective SGLT2 inhibitors.

Natural flavonoid derivatives as oral human epidermoid carcinoma cell inhibitors.

Natural flavonoid derivatives against cancer for selective KB cell lines (oral human epidermoid carcinoma) are analysed to determine the relationship between biological activities and structural properties of these molecules. Molecular alignment was performed for 88 natural flavonoid derivatives; out of these 88 molecules, 69 molecules were taken into training set and rest of the 19 molecules were used in test set prediction. We describe our elucidation of their structure activity relation (SAR) using three-dimensional quantitative structure activity relationship (3D-QSAR) models. A predictive comparative molecular field analysis (CoMFA) model of q² = 0.888 and r² = 0.940 was obtained and a comparative molecular similarity indices analysis (CoMSIA) model q² = 0.778 and r² = 0.971 was used to describe the non-linearly combined affinity of each functional group in the inhibitors. The contour maps obtained from 3D-QSAR studies were evaluated for the activity trends of the molecules analysed.

Study on the agonists for the human Toll-like receptor-8 by molecular modeling.

Toll-like receptor-8 agonists could be promising candidates for vaccine adjuvants, especially for neonatal vaccines. In this study, we established reliable models and explored valuable information which could explain the known experimental facts at the molecular level. Firstly, we divided the whole dataset into four splits and obtained many dependable models based on the simplified molecular input line entry system (SMILES). Secondly, the whole dataset was divided into three splits and other reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were established. Thirdly, we validated the prediction ability of these models using various validation methods for the test set. Lastly, for a better understanding of the binding modes between agonists and Toll-like receptor-8, molecular docking was applied to reveal the structural factors that impact the activity of agonists towards Toll-like receptor-8. Furthermore, molecular dynamics simulation was employed to further validate the docking results. The results obtained from molecular modeling support each other, which not only provides models to predict the activities of agonists but also leads to a better understanding of the essential features that should be considered when designing novel agonists with desired activities.

Docking-based 3D-QSAR study of pyridyl aminothiazole derivatives as checkpoint kinase 1 inhibitors

Checkpoint kinase 1 (Chk1) is a promising target for the design of novel anticancer agents. In the present work, molecular docking simulations and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on pyridyl aminothiazole derivatives as Chk1 inhibitors. AutoDock was used to determine the probable binding conformations of all the compounds inside the active site of Chk1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed based on the docking conformations and alignments. The CoMFA model produced statistically significant results with a cross-validated correlation coefficient (q2) of 0.608 and a coefficient of determination (r2) of 0.972. The reliable CoMSIA model with q2 of 0.662 and r2 of 0.970 was obtained from the combination of steric, electrostatic and hydrogen bond acceptor fields. The predictive power of the models were assessed using an external test set of 14 compounds and showed reasonable external predictabilities (r2pred) of 0.668 and 0.641 for CoMFA and CoMSIA models, respectively. The models were further evaluated by leave-ten-out cross-validation, bootstrapping and progressive scrambling analyses. The study provides valuable information about the key structural elements that are required in the rational design of potential drug candidates of this class of Chk1 inhibitors.

Structure-based 3D-QSAR studies on quinazoline derivatives as platelets-derived growth factor (PDGFR) inhibitors

Platelet-derived growth factor (PDGF) is one of the various growth factors, which involves in regulation of cell growth and division. In this work, 3D-quantitative structure–activity relationship studies of 75 quinazolines derivative as PDGFR’s inhibitor were performed. Based on the cognate ligand (PBD code: 3MJG 2.3 A), numerous alignment methods were used to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) models. Docked pose of the most active compound followed by database alignment led to derived best CoMFA model (q 2 = 0.531, r ncv 2 = 0.913). With the same alignment, a statistically reliable CoMSIA model with all the five fields was also derived (q 2 = 0.525, r ncv 2 = 0.889). A test set was used to validate both the models, which gave satisfactory predictive (r pred 2 ) values of 0.77 and 0.79, respectively. Contour maps of CoMFA and CoMSIA revealed the effect of important structural features on biological activity within the binding pocket and explained its interactions with ligand.

Docking-based CoMFA and CoMSIA study of azaindole carboxylic acid derivatives as promising HIV-1 integrase inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed based on a series of azaindole carboxylic acid derivatives that had previously been reported as promising HIV-1 integrase inhibitors. Docking studies to explore the binding mode were performed based on the highly active molecule 36. The best docked conformation of molecule 36 was used as template for alignment. The comparative molecular field analysis (CoMFA) model (including steric and electrostatic fields) yielded the cross validation q 2 = 0.655, non-cross validation r 2 = 0.989 and predictive r 2 pred = 0.979. The best comparative molecular similarity indices analysis (CoMSIA) model (including steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields) yielded the cross validation q 2 = 0.719, non-cross validation r 2 = 0.992 and predictive r 2 pred = 0.953. A series of new azaindole carboxylic acid derivatives were designed and the HIV-1 integrase inhibitory activities of these designed compounds were predicted based on the CoMFA and CoMSIA models.

The discovery of inhibitors of Fas-mediated cell death pathway using the combined computational method

In this study, pharmacophore and 3D-QSAR models were developed for analogues of 3-substituted-benzofuran-2-carboxylate as inhibitors of Fas-mediated cell death pathways. Our pharmacophore model has good correspondence with experimental results and can explain the variance in biological activities coherently with respect to the structure of the data set compounds. The predictive power for our synthesized compounds were 0.96 for the pharmacophore model, 0.58 for the comparative molecular field analysis (CoMFA) model, and 0.57 for the comparative molecular similarity analysis (CoMSIA) model.

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 (q2) of 0.816 and 0.838, respectively; cross-validated coefficients (\(r_{\text{cv}}^{2}\)) of 0.812 and 0.837, respectively; and conventional coefficients (r2) 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.

Binding site exploration of CCR5 using in silico methodologies: a 3D-QSAR approach

Chemokine receptor 5 (CCR5) is an important receptor used by human immunodeficiency virus type 1 (HIV-1) to gain viral entry into host cell. In this study, we used a combined approach of comparative modeling, molecular docking, and three dimensional quantitative structure activity relationship (3D-QSAR) analyses to elucidate detailed interaction of CCR5 with their inhibitors. Docking study of the most potent inhibitor from a series of compounds was done to derive the bioactive conformation. Parameters such as random selection, rational selection, different charges and grid spacing were utilized in the model development to check their performance on the model predictivity. Final comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were chosen based on the rational selection method, Gasteiger-Hückel charges and a grid spacing of 0.5 Å. Rational model for CoMFA (q(2) = 0.722, r(2) = 0.884, Q(2) = 0.669) and CoMSIA (q(2) = 0.712, r(2) = 0.825, Q(2) = 0.522) was obtained with good statistics. Mapping of contour maps onto CCR5 interface led us to better understand of the ligand-protein interaction. Docking analysis revealed that the Glu283 is crucial for interaction. Two new amino acid residues, Tyr89 and Thr167 were identified as important in ligand-protein interaction. No site directed mutagenesis studies on these residues have been reported.

Synthesis and insect antifeedant activity of stilbene derivatives against Brontispa longissima Larvae

Continuing our search for natural product-based compounds for the control of B. longissima Larvae, 25 stilbene analogs were synthesized and evaluated for insect antifeedant activity against third-instar larvae of B. longissima for the first time. Among all the tested compounds, especially compounds 3a, 3c, and 6 showed pronounced antifeedant activities with AFC50 values of 0.218, 0.327, and 0.226 mg/mL, respectively. The different antifeedant activity ranges of these compounds indicated that variation of chemical structures in the stilbene skeleton markedly affected the activity profiles of this compound class, and some important SAR information has been revealed from it. In addition, to understand the structural requirements for antifeedant activities of the 25 synthesized stilbene analogs, a comparative molecular field analysis (CoMFA) model, which yielded the leave-one-out (LOO) cross-validated correlation coefficient (q2) of 0.533 and a non-cross-validated correlation coefficient (r2) of 0.929, was constructed. Together, these preliminary results may be useful in guiding further modification of stilbenes in the development of potential new antifeedants.

A combined 3D QSAR and pharmacophore-based virtual screening for the identification of potent p38 MAP kinase inhibitors: an in silico approach

p38 kinase plays a vital role in inflammation mediated by tumor necrosis factor-α and interleukin-1β pathways. Inhibition of p38 kinase provides an effective way to treat inflammatory diseases. 3D-QSAR study was performed to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for a series of p38 inhibitors with three different alignment methods (Receptor based, atom by atom matching, and pharmacophore based). Among the different alignment methods, better statistics were obtained with receptor-based alignment (CoMFA: q 2 = 0.777, r 2 = 0.958; CoMSIA: q 2 = 0.782, r 2 = 0.927). Superposing CoMFA/CoMSIA contour maps on the p38 active site gave a valuable insight to understand physical factors which are important for binding. In addition, this pharmacophore model was used as a 3D query for virtual screening against NCI database. The hit compounds were further filtered by docking and scoring, and their biological activities were predicted by CoMFA and CoMSIA models.