CoMSIA Fields Research Articles

3D‐QSAR, Molecular Docking and Molecular Dynamics Analysis of 1,2,3,4‐Tetrahydroquinoxalines as BRD4/BD2 Inhibitors

AbstractBRD4 plays an indispensable role in cell cycle regulation, affecting processes such as cell proliferation, apoptosis and transcription. In this article, a three‐dimensional quantitative conformational relationship (3D‐QSAR) was used to investigate the molecular simulations related to 45 tetrahydrooxazole BRD4/BD2 selective inhibitors. 3D‐QSAR models were developed based on two different analytical methods, COMFA and COMSIA. All CoMSIA field combinations were compared together and the best CoMSIA model was selected based on external validation and model prediction results. Both CoMSIA (S+E+H+D (q2=0.808, r2=0.99)) and CoMFA models (q2=0.779;,r2=0.962) predicted the internal and external well. Twenty small molecules with higher inhibitory activity than the template compound were designed by 3D‐QSAR model prediction and quantum chemistry analysis, screened by molecular docking and ADMET methods, and five novel compounds were found to have better predicted activity and binding affinity. Molecular dynamics simulations showed that PRO70, PRO371, TYR428, ASN429 and VAL435 are important residues that can interact with the ligands. Moreover, further structural optimization of the more active candidate compounds will provide an essential theoretical basis of the synthesis and design of novel BRD4/BD2 inhibitors.

Docking-Based 3D-QSAR Studies for 1,3,4-oxadiazol-2-one Derivatives as FAAH Inhibitors

This work aimed to construct 3D-QSAR CoMFA and CoMSIA models for a series of 31 FAAH inhibitors, containing the 1,3,4-oxadiazol-2-one moiety. The obtained models were characterized by good statistical parameters: CoMFA Q2 = 0.61, R2 = 0.98; CoMSIA Q2 = 0.64, R2 = 0.93. The CoMFA model field contributions were 54.1% and 45.9% for steric and electrostatic fields, respectively. In the CoMSIA model, electrostatic, steric, hydrogen bond donor, and hydrogen acceptor properties were equal to 34.6%, 23.9%, 23.4%, and 18.0%, respectively. These models were validated by applying the leave-one-out technique, the seven-element test set (CoMFA r2test-set = 0.91; CoMSIA r2test-set = 0.91), a progressive scrambling test, and external validation criteria developed by Golbraikh and Tropsha (CoMFA r20 = 0.98, k = 0.95; CoMSIA r20 = 0.98, k = 0.89). As the statistical significance of the obtained model was confirmed, the results of the CoMFA and CoMSIA field calculation were mapped onto the enzyme binding site. It gave us the opportunity to discuss the structure–activity relationship based on the ligand–enzyme interactions. In particular, examination of the electrostatic properties of the established CoMFA model revealed fields that correspond to the regions where electropositive substituents are not desired, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one moiety. This highlights the importance of heterocycle, a highly electronegative moiety in this area of each ligand. Examination of hydrogen bond donor and acceptor properties contour maps revealed several spots where the implementation of another hydrogen-bond-donating moiety will positively impact molecules’ binding affinity, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one ring. On the other hand, there is a large isopleth that refers to the favorable H-bond properties close to the terminal phenoxy group of a ligand, which means that, generally speaking, H-bond acceptors are desired in this area.

Insights into the interactions between maleimide derivates and GSK3β combining molecular docking and QSAR.

Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure–activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure–activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q2 value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors.

Exploiting ChEMBL database to identify indole analogs as HCV replication inhibitors

Molecular docking, 3D-QSAR CoMSIA and similarity search were combined in a multi-step framework with the ultimate goal to identify potent indole analogs, in the ChEMBL database, as inhibitors of HCV replication. The crystal structure of HCV RNA-dependent RNA polymerase (NS5B GT1b) was utilized and 41 known inhibitors were docked into the enzyme “Palm II” active site. In a second step, the docking pose of each compound was used in a receptor-based alignment for the generation of the CoMSIA fields. A validated 3D-QSAR CoMSIA model was subsequently built to accurately estimate the activity values. The proposed framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogs on HCV polymerase. The obtained in silico model was used to predict the activity of novel compounds prior to their synthesis and biological testing, within a Virtual Screening framework. The ChEMBL database was mined to afford compounds containing the indole scaffold that are predicted to possess high activity and thus can be prioritized for biological screening.

Molecular Modeling on Pyrimidine-Urea Inhibitors of TNF-α Production: An Integrated Approach Using a Combination of Molecular Docking, Classification Techniques, and 3D-QSAR CoMSIA

Molecular docking, classification techniques, and 3D-QSAR CoMSIA were combined in a multistep framework with the ultimate goal of identifying potent pyrimidine-urea inhibitors of TNF-α production. Using the crystal structure of p38α, all the compounds were docked into the enzyme active site. The docking pose of each compound was subsequently used in a receptor-based alignment for the generation of the CoMSIA fields. "Active" and "inactive" compounds were used to build a Random Tree classification model using the docking score and the CoMSIA fields as input parameters. Domain of applicability indicated the compounds for which activity estimations can be accepted with confidence. For the active compounds, a 3D-QSAR CoMSIA model was subsequently built to accurately estimate the IC(50) values. This novel multistep framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogues on p38α MAP kinase, and it can be extended to other classes of small-molecule inhibitors. In addition, the simplicity of the proposed approach provides expansion to its applicability such as in virtual screening procedures.

3D‐QSAR and Molecular Docking Studies on 3‐Anilino‐4‐Arylmaleimide Derivatives as Glycogen Synthase Kinase‐3β Inhibitors

Glycogen synthase kinase-3, a serine/threonine kinase, is a fascinating enzyme with diverse biological actions in intracellular signaling systems, making it an emerging target for diseases such as diabetes mellitus, cancer, chronic inflammation, bipolar disorders, and Alzheimer's disease. It is important to inhibit glycogen synthase kinase-3 selectively, and the net effect of the glycogen synthase kinase-3 inhibitors thus should be target specific, over other phylogenetically related kinases such as CDK-2. In the present work, we have carried out three-dimensional quantitative structure-activity relationship studies on novel class of 3-anilino-4-arylmaleimide derivatives to have improved cellular activity. Docked conformation of the most active molecule in the series, which shows desirable interactions in the receptor, was taken as template for the alignment of the molecules. Statistically significant CoMSIA (r2(cv)=0.614, r2(ncv)=0.948) and comparative molecular field analysis (r2(cv) =0.652, r2(ncv)=0.958) models were generated using 57 molecules in training set. The predictive ability of CoMSIA and comparative molecular field analysis models was determined using a test set of 17 molecules, which gave predictive correlation coefficients (r2(pred)) of 0.87 and 0.82, respectively, indicating good predictive power. Based on the information derived from CoMSIA and comparative molecular field analysis contour maps, we have identified some key features that explain the observed variance in the activity and have been used to design new anilinoarylmaleimide derivatives. The designed molecules showed better binding affinity in terms of estimated docking scores with respect to the already reported systems, hence suggesting that newly designed molecules can be more potent and selective toward glycogen synthase kinase-3β inhibition.

Improvement of the Prediction Power of the CoMFA and CoMSIA Models of Histamine H3 Antagonists by Different Variable Selection Methods

The aim of this study is to enhance the predictivity power of CoMFA and CoMSIA models by means of different variable selection algorithms. The genetic algorithm (GA), successive projection algorithm (SPA), stepwise multiple linear regression (SW-MLR), and the enhanced replacement method (ERM) were used and tested as variable selection algorithms. Then, the selected variables were used to generate a simple and predictive model by the multilinear regression algorithm. A set of 74 histamine H3 antagonists were split into 40 compounds as a training set, and 17 compounds as a test set, by the Kennard-Stone algorithm. Before splitting the data, 17 compounds were randomly selected from the pool of the whole data set as an evaluation set without any supervision, pretreatment, or visual inspection. Among applied variable selection algorithms, ERM had noticeable improvement on the statistical parameters. The r2 values of training, test, and evaluation sets for the ERM-MLR model using CoMFA fields were 0.9560, 0.8630, and 0.8460 and using the CoMSIA fields were 0.9800, 0.8521, and 0.9080, respectively. In this study, the principles of organization for economic cooperation and development (OECD) for regulatory acceptability of QSARs are considered.

Structure-based CoMFA and CoMSIA study of indolinone inhibitors of PDK1

Phosphoinositide-dependent protein kinase-1 (PDK1) is a Ser/Thr kinase which phosphorylates and activates members of the AGC kinase group known to control processes such as tumor cell growth, protection from apoptosis, and tumor angiogenesis. In this paper, CoMFA and CoMSIA studies were carried out on a training set of 56 conformationally rigid indolinone inhibitors of PDK1. Predictive 3D QSAR models, established using atom fit alignment rule based on crystallographic-bound conformation, had cross-validated (r(cv)(2)) values of 0.738 and 0.816 and non-cross-validated (r(ncv)(2)) values of 0.912 and 0.949 for CoMFA and CoMSIA models, respectively. The predictive ability of the CoMFA and CoMSIA models was determined using a test set of 14 compounds, which gave predictive correlation coefficients (r(pred)(2)) of 0.865 and 0.837, respectively. Structure-based interpretation of the CoMFA and CoMSIA field properties provided further insights for the rational design of new PDK1 inhibitors.

QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR

The recent wide spreading of the H5N1 avian influenza virus (AIV) in Asia, Europe and Africa and its ability to cause fatal infections in human has raised serious concerns about a pending global flu pandemic. Neuraminidase (NA) inhibitors are currently the only option for treatment or prophylaxis in humans infected with this strain. However, drugs currently on the market often meet with rapidly emerging resistant mutants and only have limited application as inadequate supply of synthetic material. To dig out helpful information for designing potent inhibitors with novel structures against the NA, we used automated docking, CoMFA, CoMSIA, and HQSAR methods to investigate the quantitative structure-activity relationship for 126 NA inhibitors (NIs) with great structural diversities and wide range of bioactivities against influenza A virus. Based on the binding conformations discovered via molecular docking into the crystal structure of NA, CoMFA and CoMSIA models were successfully built with the cross-validated q (2) of 0.813 and 0.771, respectively. HQSAR was also carried out as a complementary study in that HQSAR technique does not require 3D information of these compounds and could provide a detailed molecular fragment contribution to the inhibitory activity. These models also show clearly how steric, electrostatic, hydrophobicity, and individual fragments affect the potency of NA inhibitors. In addition, CoMFA and CoMSIA field distributions are found to be in well agreement with the structural characteristics of the corresponding binding sites. Therefore, the final 3D-QSAR models and the information of the inhibitor-enzyme interaction should be useful in developing novel potent NA inhibitors.

2D and 3D Quantitative Structure‐Activity Relationship Studies on a Series of bis‐Pyridinium Compounds as Choline Kinase Inhibitors

AbstractTwo‐dimensional (2D) and three‐dimensional (3D) quantitative structure activity relationship (QSAR) studies have been carried out on a series of 55 bis‐pyridinium compounds to find out the structural requirements of choline kinase (ChoK) inhibitors. The best predictions were obtained from the model where 44 compounds were considered in the training set and the remaining 11 in the test set. The heuristic and BMLR methods resulted with r2 and q2 values of 0.86, 0.83 and 0.87, 0.84 respectively. The obtained Fisher and S2 values for both the methods are 49.02 and 54.05, 0.053 and 0.0397 respectively. The best model for 3D‐QSAR has been obtained with r2=0.97, q2 = 0.58 and r$\rm{ {_{pred}^{2}}}$=0.68 when CoMFA fields were used. The r2 of 0.85, q2 of 0.55 and r$\rm{ {_{pred}^{2}}}$=0.66 have been observed when CoMSIA fields were used. The results that are obtained from 2D and 3D‐QSAR studies may provide useful insights into the roles of various substitution patterns on the bis‐pyridinium skeleton and may also help to design more potent compounds.

The 3D Structure of the Binding Pocket of the Human Oxytocin Receptor for Benzoxazine Antagonists, Determined by Molecular Docking, Scoring Functions and 3D-QSAR Methods

Molecular docking and 3D-QSAR studies were performed to determine the binding mode for a series of benzoxazine oxytocin antagonists taken from the literature. Structural hypotheses were generated by docking the most active molecule to the rigid receptor by means of AutoDock 3.05. The cluster analysis yielded seven possible binding conformations. These structures were refined by using constrained simulated annealing, and the further ligands were aligned in the refined receptor by molecular docking. A good correlation was found between the estimated deltaG(bind) and the pKi values for complex F. The Connolly-surface analysis, CoMFA and CoMSIA models q2(CoMFA) = 0.653, q2(CoMSA) = 0.630 and r2(pred,CoMFA) = 0.852 , r2(pred,CoMSIA) = 0.815) confirmed the scoring function results. The structural features of the receptor-ligand complex and the CoMFA and CoMSIA fields are in closely connected. These results suggest that receptor-ligand complex F is the most likely binding hypothesis for the studied benzoxazine analogs.

Molecular docking and 3D-QSAR studies on the binding mechanism of statine-based peptidomimetics with β-secretase

β-Secretase is an important protease in the pathogenesis of Alzheimer’s disease. Some statine-based peptidomimetics show inhibitory activities to the β-secretase. To explore the inhibitory mechanism, molecular docking and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies on these analogues were performed. The Lamarckian Genetic Algorithm (LGA) was applied to locate the binding orientations and conformations of the peptidomimetics with the β-secretase. A good correlation between the calculated binding free energies and the experimental inhibitory activities suggests that the identified binding conformations of these potential inhibitors are reliable. Based on the binding conformations, highly predictive 3D-QSAR models were developed with q 2 values of 0.582 and 0.622 for CoMFA and CoMSIA, respectively. The predictive abilities of these models were validated by some compounds that were not included in the training set. Furthermore, the 3D-QSAR models were mapped back to the binding site of the β-secretase, to get a better understanding of vital interactions between the statine-based peptidomimetics and the protease. Both the CoMFA and the CoMSIA field distributions are in well agreement with the structural characteristics of the binding groove of the β-secretase. Therefore, the final 3D-QSAR models and the information of the inhibitor–enzyme interaction would be useful in developing new drug leads against Alzheimer’s disease.

Rational design of an indolebutanoic acid derivative as a novel aldose reductase inhibitor based on docking and 3D QSAR studies of phenethylamine derivatives.

A series of 45 phenethylamine derivatives were synthesized and evaluated for their inhibitory activity against pig kidney aldose reductase (ALR2, EC 1.1.1.21). Their IC(50) values ranged from 400 microM to 24 microM. The binding modes of compounds at the active site of ALR2 were examined using flexible docking. The results indicated that phenethylamine derivatives nicely fit into the active pocket of ALR2 by forming various hydrogen bonding and hydrophobic interactions. 3D-QSAR analysis was also conducted using FlexX-docked alignment of the compounds. The best prediction was obtained by CoMSIA combined with hydrophobic and hydrogen bond donor/acceptor field (q(2) = 0.557, r(2) = 0.934). A new derivative, 4-oxo-4-(4-hydroxyindole)butanoic acid, was designed, taking into account the CoMSIA field and the binding mode derived by FlexX docking. This rationally designed compound exhibits an ALR2 inhibition with an IC(50) value of 7.4 microM, which compares favorably to that of a well-known ALR2 inhibitor, tolrestat (IC(50) = 16 microM) and represents a potency approximately 240-fold higher than that of an original phenethylamine lead compound, YUA001.

Molecular docking and 3D-QSAR studies on gag peptide analogue inhibitors interacting with human cyclophilin A.

The interaction of a series gag peptide analogues with human cyclophilin A (hCypA) have been studied employing molecular docking and 3D-QSAR approaches. The Lamarckian Genetic Algorithm (LGA) and divide-and-conquer methods were applied to locate the binding orientations and conformations of the inhibitors interacting with hCypA. Good correlations between the calculated interaction free energies and experimental inhibitory activities suggest that the binding conformations of these inhibitors are reasonable. A novel interaction model was identified for inhibitors 11, 15, and 17 whose N-termini were modified by addition of the deaminovaline (Dav) group and the C-termini of 15 and 17 were modified by addition of a benzyl group. Accordingly, two new binding sites (sites A and D in Figure 1) were revealed, which show a strong correlation with inhibitor potency and thus can be used as a starting point for new inhibitor design. In addition, two predictive 3D-QSAR models were obtained by CoMFA and CoMSIA analyses based on the binding conformations derived from the molecular docking calculations. The reasonable r(cross)(2) (cross-validated) values 0.738 and 0.762 were obtained for CoMFA and CoMSIA models, respectively. The predictive ability of these models was validated by four peptide analogues test set. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding groove of hCypA. This indicates the reasonableness of the binding model of the inhibitors with hCypA. Considering all these results together with the valuable clues of binding from references published recently, reasonable pharmacophore elements have been suggested, demonstrating that the 3D-QSAR models about peptide analogue inhibitors are expected to be further employed in predicting activities of the novel compounds for inhibiting hCypA.

CoMFA, CoMSIA and GRID/GOLPE studies on calcium entry blocking 1,4-dihydropyridines

Three different 3D QSAR methods have been applied for a common pharmacophore model of 45 calcium antagonistically active 1,4-dihydropyridines (DHP) in order to find best correlation of interaction fields and biological activity. Analysis for the entire data set yielded r2/q values in a range starting from 0.821/0.620 (GRID/GOLPE) over 0.872/0.600 (CoMFA) to 0.908/0.744 (CoMSIA). The robustness of these models was tested not only via leave-one-out but also by leave-9-out crossvalidations. Furthermore, models were constructed using a subset of 37 DHPs (training set) allowing the prediction of activity for the residual 8 DHPs (test set). The training set yielded r2/q values starting from 0.826/0.672 (GRID/GOLPE) over 0.872/0.540 (CoMFA) to 0.899/0.662 (CoMSIA). For the test set r values from 0.677 (GRID/GOLPE) over 0.639 (CoMFA) to 0.470 (CoMSIA) were calculated. Besides the statistics, each 3D QSAR model yields further information by analysis of the generated contour maps. Consideration of the CoMFA and CoMSIA fields indicates unfavourable steric interactions for bulky moieties in 4′-position. On the other hand, sterical demanding 2′- and 3′-substituents are favourable and the biological activity of DHPs is further increased if these moieties produce a negative electrostatic potential. In contrast, high π-electron density on top of and parallel to the 4-phenyl ring beside the 2′-position is associated with decreasing activity. This could point to repulsive electronic interactions with binding site residues or to the potential of electron-deficient 4-aryl moieties to behave as electron acceptors in a charge transfer (CT) mechanism.

CoMFA and CoMSIA 3D-quantitative structure-activity relationship model on benzodiazepine derivatives, inhibitors of phosphodiesterase IV.

Recently, we reported structurally novel PDE4 inhibitors based on 1,4-benzodiazepine derivatives. The main interest in developing bezodiazepine-based PDE4 inhibitors is in their lack of adverse effects of emesis with respect to rolipram-like compounds. A large effort has thus been made toward the structural optimization of this series. In the absence of structural information on the inhibitor binding mode into the PDE4 active site, 2D-QSAR (H-QSAR) and two 3D-QSAR (CoMFA and CoMSIA) methods were applied to improve our understanding of the molecular mechanism controlling the PDE4 affinity of the benzodiazepine derivatives. As expected, the CoMSIA 3D contour maps have provided more information on the benzodiazepine interaction mode with the PDE4 active site whereas CoMFA has built the best tool for activity prediction. The 2D pharmacophoric model derived from CoMSIA fields is consistent with the crystal structure of the PDE4 active site reported recently. The combination of the 2D and 3D-QSAR models was used not only to predict new compounds from the structural optimization process, but also to screen a large library of bezodiazepine derivatives.