In silico identification of structure requirement for novel thiazole and oxazole derivatives as potent fructose 1,6-bisphosphatase inhibitors

Fructose 1,6-bisphosphatase (FBPase) has been identified as a drug discovery target for lowering glucose in type 2 diabetes mellitus. In this study, a large series of 105 FBPase inhibitors were studied using a combinational method by 3D-QSAR, molecular docking and molecular dynamics simulations for a further improvement in potency. The optimal 3D models exhibit high statistical significance of the results, especially for the CoMFA results with r(ncv) (2), q(2) values of 0.986, 0.514 for internal validation, and r(pred) (2), r(m) (2) statistics of 0.902, 0.828 statistics for external validation. Graphic representation of the results, as contoured 3D coefficient plots, also provides a clue to the reasonable modification of molecules. (1) Substituents with a proper length and size at the C5 position of the thiazole core are required to enhance the potency; (2) A small and electron-withdrawing group at the C2 position linked to the thiazole core is likely to help increase the FBPase inhibition; (3) Substituent groups as hydrogen bond acceptors at the C2 position of the furan ring are favored. In addition, the agreement between 3D-QSAR, molecular docking and molecular dynamics simulation proves the rationality of the developed models. These results, we hope, may be helpful in designing novel and potential FBPase inhibitors.     

A Computational Study on Thiourea Analogs as Potent MK-2 Inhibitors

Mitogen-activated protein kinase-activated protein kinase 2 (MK-2) has been identified as a drug target for the treatment of inflammatory diseases. Currently, a series of thiourea analogs as potent MK-2 inhibitors were studied using comprehensive computational methods by 3D-QSAR, molecular docking and molecular dynamics simulations for a further improvement in activities. The optimal 3D models exhibit high statistical significance of the results, especially for the CoMFA results with r(2) (ncv), q(2) values of 0.974, 0.536 for the internal validation, and r(2) (pred), r(2) (m) values of 0.910, 0.723 for the external validation and Roy's index, respectively. In addition, more rigorous validation criteria suggested by Tropsha were also employed to check the built models. Graphic representation of the results, as contoured 3D coefficient plots, also provides a clue to the reasonable modification of molecules: (i) The substituent with a bulky size and electron-rich group at the C5 position of the pyrazine ring is required to enhance the potency; (ii) The H-bond acceptor group in the C3 position of the pyrazine ring is likely to be helpful to increase MK-2 inhibition; (iii) The small and electropositive substituent as a hydrogen bond donor of the C2 position in the oxazolone ring is favored; In addition, several important amino acid residues were also identified as playing an important role in MK-2 inhibition. The agreement between 3D-QSAR, molecular docking and molecular dynamics simulations also proves the rationality of the developed models. These results, we hope, may be helpful in designing novel and potential MK-2 inhibitors.     

Structural Determination of Three Different Series of Compounds as Hsp90 Inhibitors Using 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Methods

Hsp90 is involved in correcting, folding, maturation and activation of a diverse array of client proteins; it has also been implicated in the treatment of cancer in recent years. In this work, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular docking and molecular dynamics were performed on three different series of Hsp90 inhibitors to build 3D-QSAR models, which were based on the ligand-based or receptor-based methods. The optimum 3D-QSAR models exhibited reasonable statistical characteristics with averaging internal q(2) > 0.60 and external r(2) (pred) > 0.66 for Benzamide tetrahydro-4H-carbazol-4-one analogs (BT), AT13387 derivatives (AT) and Dihydroxylphenyl amides (DA). The results revealed that steric effects contributed the most to the BT model, whereas H-bonding was more important to AT, and electrostatic, hydrophobic, H-bond donor almost contributed equally to the DA model. The docking analysis showed that Asp93, Tyr139 and Thr184 in Hsp90 are important for the three series of inhibitors. Molecular dynamics simulation (MD) further indicated that the conformation derived from docking is basically consistent with the average structure extracted from MD simulation. These results not only lead to a better understanding of interactions between these inhibitors and Hsp90 receptor but also provide useful information for the design of new inhibitors with a specific activity.     

3D-QSAR and Molecular Docking Studies on Fused Pyrazoles as p38α Mitogen-Activated Protein Kinase Inhibitors

The p38α mitogen-activated protein kinase (MAPK) has become an attractive target for the treatment of many diseases such as rheumatoid arthritis, inflammatory bowel disease and Crohn's disease. In this paper, 3D-QSAR and molecular docking studies were performed on 59 p38α MAPK inhibitors. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to determine the structural requirements for potency in inhibiting p38α MAPK. The resulting model of CoMFA and CoMSIA exhibited good r(2) (cv) values of 0.725 and 0.609, and r(2) values of 0.961 and 0.905, respectively. Molecular docking was used to explore the binding mode between the inhibitors and p38α MAPK. We have accordingly designed a series of novel p38α MAPK inhibitors by utilizing the structure-activity relationship (SAR) results revealed in the present study, which were predicted with excellent potencies in the developed models. The results provided a useful guide to design new compounds for p38α MAPK inhibitors.     

Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) Studies on α(1A)-Adrenergic Receptor Antagonists Based on Pharmacophore Molecular Alignment

The α(1A)-adrenergic receptor (α(1A)-AR) antagonist is useful in treating benign prostatic hyperplasia, lower urinary tract symptoms, and cardiac arrhythmia. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on a set of α(1A)-AR antagonists of N-aryl and N-nitrogen class. Statistically significant models constructed from comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were established based on a training set of 32 ligands using pharmacophore-based molecular alignment. The leave-oneout cross-validation correlation coefficients were q(2) (CoMFA) = 0.840 and q(2) (CoMSIA) = 0.840. The high correlation between the cross-validated/predicted and experimental activities of a test set of 12 ligands revealed that the CoMFA and CoMSIA models were robust (r(2) (pred) (/CoMFA) = 0.694; r(2) (pred) (/CoMSIA) = 0.671). The generated models suggested that electrostatic, hydrophobic, and hydrogen bonding interactions play important roles between ligands and receptors in the active site. Our study serves as a guide for further experimental investigations on the synthesis of new compounds. Structural modifications based on the present 3D-QSAR results may lead to the discovery of other α(1A)-AR antagonists.     

Combined 3D-QSAR and Docking Modelling Study on Indolocarbazole Series Compounds as Tie-2 Inhibitors

Tie-2, a kind of endothelial cell tyrosine kinase receptor, is required for embryonic blood vessel development and tumor angiogenesis. Several compounds that showed potent activity toward this attractive anticancer drug target in the assay have been reported. In order to investigate the structure-activity correlation of indolocarbazole series compounds and modify them to improve their selectivity and activity, 3D-QSAR models were built using CoMFA and CoMSIA methods and molecular docking was used to check the results. Based on the common sketch align, two good QSAR models with high predictabilities (CoMFA model: q(2) = 0.823, r(2) = 0.979; CoMSIA model: q(2) = 0.804, r(2) = 0.967) were obtained and the contour maps obtained from both models were applied to identify the influence on the biological activity. Molecular docking was then used to confirm the results. Combined with the molecular docking results, the detail binding mode between the ligands and Tie-2 was elucidated, which enabled us to interpret the structure-activity relationship. These satisf actory results not only offered help to comprehend the action mechanism of indolocarbazole series compounds, but also provide new information for the design of new potent inhibitors.     

Inhibitory mode of N-phenyl-4-pyrazolo[1,5-b] pyridazin-3-ylpyrimidin-2-amine series derivatives against GSK-3: molecular docking and 3D-QSAR analyses

Glycogen synthase kinase 3 (GSK-3) inhibition is an important research topic because of its wide range of associated health implications. The interaction mode of a series of N-phenyl-4-pyrazolo[1,5-b]pyridazin-3-ylpyrimidin-2-amine compounds with human GSK-3 has been studied using molecular docking and 3D-QSAR approaches. In the 3D-QSAR studies, the molecular alignment and conformation determination are so important that they affect the success of a model. Flexible docking (AutoDock3.0.5) was used for the determination of 'active' conformation and molecular alignment. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to develop 3D-QSAR models of 80 N-phenyl-4-pyrazolo[1,5-b]pyridazin-3-ylpyrimidin-2-amine compounds. The r(2) values were 0.870 and 0.861 for CoMFA and CoMSIA models, respectively. The predictive ability of these models was validated by 10 compounds of the test set. Mapping these models back to the topology of the active site of GSK-3 led to a better understanding of the vital N-phenyl-4-pyrazolo[1,5-b]pyridazin-3-ylpyrimidin-2-amines-GSK-3 interactions. The results demonstrate that combination of ligand-based and receptor-based modeling is a powerful approach to build 3D-QSAR models. The interaction mode from this study may be helpful for the design of a novel inhibitor and guide the selection of candidate sites for further experimental studies on site-directed mutagenesis.     

Pyrrolo[3,2-d]pyrimidine Derivatives as Type II Kinase Insert Domain Receptor (KDR) Inhibitors: CoMFA and CoMSIA Studies

Kinase insert domain receptor (KDR) inhibitors have been proved to be very effective anticancer agents. Molecular docking, 3D-QSAR methods, CoMFA and CoMSIA were performed on pyrrolo[3,2-d]pyrimidine derivatives as non-ATP competitive KDR inhibitors (type II). The bioactive conformation was explored by docking one potent compound 20 into the active site of KDR in its DFG-out inactive conformation. The constructed CoMFA and CoMSIA models produced statistically significant results with the cross-validated correlation coefficients q(2) of 0.542 and 0.552, non-cross-validated correlation coefficients r(2) of 0.912 and 0.955, and predicted correction coefficients r(2) (pred) of 0.913 and 0.897, respectively. These results ensure the CoMFA and CoMSIA models as a tool to guide the design of a series of new potent KDR inhibitors.     

Combined 3D-QSAR Modeling and Molecular Docking Studies on Pyrrole-Indolin-2-ones as Aurora A Kinase Inhibitors

Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r(2) (cv) values of 0.726 and 0.566, and r(2) values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed.     

Combined 3D-QSAR, Molecular Docking and Molecular Dynamics Study on Derivatives of Peptide Epoxyketone and Tyropeptin-Boronic Acid as Inhibitors Against the β5 Subunit of Human 20S Proteasome

An abnormal ubiquitin-proteasome is found in many human diseases, especially in cancer, and has received extensive attention as a promising therapeutic target in recent years. In this work, several in silico models have been built with two classes of proteasome inhibitors (PIs) by using 3D-QSAR, homology modeling, molecular docking and molecular dynamics (MD) simulations. The study resulted in two types of satisfactory 3D-QSAR models, i.e., the CoMFA model (Q(2) = 0.462, R(2) (pred) = 0.820) for epoxyketone inhibitors (EPK) and the CoMSIA model (Q(2) = 0.622, R(2) (pred) = 0.821) for tyropeptin-boronic acid derivatives (TBA). From the contour maps, some key structural factors responsible for the activity of these two series of PIs are revealed. For EPK inhibitors, the N-cap part should have higher electropositivity; a large substituent such as a benzene ring is favored at the C6-position. In terms of TBA inhibitors, hydrophobic substituents with a larger size anisole group are preferential at the C8-position; higher electropositive substituents like a naphthalene group at the C3-position can enhance the activity of the drug by providing hydrogen bond interaction with the protein target. Molecular docking disclosed that residues Thr60, Thr80, Gly106 and Ser189 play a pivotal role in maintaining the drug-target interactions, which are consistent with the contour maps. MD simulations further indicated that the binding modes of each conformation derived from docking is stable and in accord with the corresponding structure extracted from MD simulation overall. These results can offer useful theoretical references for designing more potent PIs.     

Probing Structural Features and Binding Mode of 3-Arylpyrimidin-2,4-diones within Housefly γ-Aminobutyric Acid (GABA) Receptor

In order to obtain structural features of 3-arylpyrimidin-2,4-diones emerged as promising inhibitors of insect γ-aminobutyric acid (GABA) receptor, a set of ligand-/receptor-based 3D-QSAR models for 60 derivatives are generated using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA). The statistically optimal CoMSIA model is produced with highest q(2) of 0.62, r(2) (ncv) of 0.97, and r(2) (pred) of 0.95. A minor/bulky electronegative hydrophilic polar substituent at the 1-/6-postion of the uracil ring, and bulky substituents at the 3'-, 4'- and 5'-positions of the benzene ring are beneficial for the enhanced potency of the inhibitors as revealed by the obtained 3D-contour maps. Furthermore, homology modeling, molecular dynamics (MD) simulation and molecular docking are also carried out to gain a better understanding of the probable binding modes of these inhibitors, and the results show that residues Ala-183(C), Thr-187(B), Thr-187(D) and Thr-187(E) in the second transmembrane domains of GABA receptor are responsible for the H-bonding interactions with the inhibitor. The good correlation between docking observations and 3D-QSAR analyses further proves the model reasonability in probing the structural features and the binding mode of 3-arylpyrimidin-2,4-dione derivatives within the housefly GABA receptor.     

Combined pharmacophore modeling, docking, and 3D-QSAR studies of PLK1 inhibitors

Polo-like kinase 1, an important enzyme with diverse biological actions in cell mitosis, is a promising target for developing novel anticancer drugs. A combined molecular docking, structure-based pharmacophore modeling and three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on a set of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as PLK1 inhibitors. The common substructure, molecular docking and pharmacophore-based alignment were used to develop different 3D-QSAR models. The comparative molecular field analysis (CoMFA) and comparative molecule similarity indices analysis (CoMSIA) models gave statistically significant results. These models showed good q(2) and r(2) (pred) values and revealed a good response to test set validation. All of the structural insights obtained from the 3D-QSAR contour maps are consistent with the available crystal structure of PLK1. The contour maps obtained from the 3D-QSAR models in combination with the structure based pharmacophore model help to better interpret the structure-activity relationship. These satisfactory results may aid the design of novel PLK1 inhibitors. This is the first report on 3D-QSAR study of PLK1 inhibitors.     

In Silico Screening of Novel α1-GABAA Receptor PAMs towards Schizophrenia Based on Combined Modeling Studies of Imidazo [1,2-a]-Pyridines

The ionotropic GABA_A receptor (GABA_AR) has been proven to be an important target of atypical antipsychotics. A novel series of imidazo [1,2-a]-pyridine derivatives, as selective positive allosteric modulators (PAMs) of α1-containing GABA_ARs with potent antipsychotic activities, have been reported recently. To better clarify the pharmacological essentiality of these PAMs and explore novel antipsychotics hits, three-dimensional quantitative structure–activity relationships (3D-QSAR), molecular docking, pharmacophore modeling, and molecular dynamics (MD) were performed on 33 imidazo [1,2-a]-pyridines. The constructed 3D-QSAR models exhibited good predictive abilities. The dockings results and MD simulations demonstrated that hydrogen bonds, π–π stackings, and hydrophobic interactions play essential roles in the binding of these novel PAMs in the GABA_AR binding pocket. Four hit compounds (DS01–04) were then screened out by the combination of the constructed models and computations, including the pharmacophore model, Topomer Search, molecular dockings, ADME/T predictions, and MD simulations. The compounds DS03 and DS04, with higher docking scores and better predicted activities, were also found to be relatively stable in the binding pocket by MD simulations. These results might provide a significant theoretical direction or information for the rational design and development of novel α1-GABA_AR PAMs with antipsychotic activities.     

Exploration of Novel Xanthine Oxidase Inhibitors Based on 1,6-Dihydropyrimidine-5-Carboxylic Acids by an Integrated in Silico Study

Xanthine oxidase (XO) is an important target for the effective treatment of hyperuricemia-associated diseases. A series of novel 2-substituted 6-oxo-1,6-dihydropyrimidine-5-carboxylic acids (ODCs) as XO inhibitors (XOIs) with remarkable activities have been reported recently. To better understand the key pharmacological characteristics of these XOIs and explore more hit compounds, in the present study, the three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking, pharmacophore modeling, and molecular dynamics (MD) studies were performed on 46 ODCs. The constructed 3D-QSAR models exhibited reliable predictability with satisfactory validation parameters, including q² = 0.897, R² = 0.983, r_pred² = 0.948 in a CoMFA model, and q² = 0.922, R² = 0.990, r_pred² = 0.840 in a CoMSIA model. Docking and MD simulations further gave insights into the binding modes of these ODCs with the XO protein. The results indicated that key residues Glu802, Arg880, Asn768, Thr1010, Phe914, and Phe1009 could interact with ODCs by hydrogen bonds, π-π stackings, or hydrophobic interactions, which might be significant for the activity of these XOIs. Four potential hits were virtually screened out using the constructed pharmacophore model in combination with molecular dockings and ADME predictions. The four hits were also found to be relatively stable in the binding pocket by MD simulations. The results in this study might provide effective information for the design and development of novel XOIs.     

Computational Analysis of Structure-Based Interactions for Novel H1-Antihistamines

As a chronic disorder, insomnia affects approximately 10% of the population at some time during their lives, and its treatment is often challenging. Since the antagonists of the H₁ receptor, a protein prevalent in human central nervous system, have been proven as effective therapeutic agents for treating insomnia, the H₁ receptor is quite possibly a promising target for developing potent anti-insomnia drugs. For the purpose of understanding the structural actors affecting the antagonism potency, presently a theoretical research of molecular interactions between 129 molecules and the H₁ receptor is performed through three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques. The ligand-based comparative molecular similarity indices analysis (CoMSIA) model (Q² = 0.525, R²_ncv = 0.891, R²_pred = 0.807) has good quality for predicting the bioactivities of new chemicals. The cross-validated result suggests that the developed models have excellent internal and external predictability and consistency. The obtained contour maps were appraised for affinity trends for the investigated compounds, which provides significantly useful information in the rational drug design of novel anti-insomnia agents. Molecular docking was also performed to investigate the mode of interaction between the ligand and the active site of the receptor. Furthermore, as a supplementary tool to study the docking conformation of the antagonists in the H₁ receptor binding pocket, molecular dynamics simulation was also applied, providing insights into the changes in the structure. All of the models and the derived information would, we hope, be of help for developing novel potent histamine H₁ receptor antagonists, as well as exploring the H₁-antihistamines interaction mechanism.     

Pharmacophore and Molecular Docking Guided 3D-QSAR Study of Bacterial Enoyl-ACP Reductase (FabI) Inhibitors

Enoyl acyl carrier protein (ACP) reductase (FabI) is a potential target for the development of antibacterial agents. Three-dimensional quantitative structure-activity relationships (3D-QSAR) for substituted formamides series of FabI inhibitors were investigated using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Pharmacophore and molecular docking methods were used for construction of the molecular alignments. A training set of 36 compounds was performed to create the 3D-QSAR models and their external predictivity was proven using a test set of 11 compounds. Graphical interpretation of the results revealed important structural features of the formamides related to the active site of FabI. The results may be exploited for further optimization of the design of new potent FabI inhibitors.     

A Combination of 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation Studies of Benzimidazole-Quinolinone Derivatives as iNOS Inhibitors

Inducible Nitric Oxide Synthase (iNOS) has been involved in a variety of diseases, and thus it is interesting to discover and optimize new iNOS inhibitors. In previous studies, a series of benzimidazole-quinolinone derivatives with high inhibitory activity against human iNOS were discovered. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking and molecular dynamics (MD) simulation approaches were applied to investigate the functionalities of active molecular interaction between these active ligands and iNOS. A QSAR model with R² of 0.9356, Q² of 0.8373 and Pearson-R value of 0.9406 was constructed, which presents a good predictive ability in both internal and external validation. Furthermore, a combined analysis incorporating the obtained model and the MD results indicates: (1) compounds with the proper-size hydrophobic substituents at position 3 in ring-C (R₃ substituent), hydrophilic substituents near the X₆ of ring-D and hydrophilic or H-bond acceptor groups at position 2 in ring-B show enhanced biological activities; (2) Met368, Trp366, Gly365, Tyr367, Phe363, Pro344, Gln257, Val346, Asn364, Met349, Thr370, Glu371 and Tyr485 are key amino acids in the active pocket, and activities of iNOS inhibitors are consistent with their capability to alter the position of these important residues, especially Glu371 and Thr370. The results provide a set of useful guidelines for the rational design of novel iNOS inhibitors.     

Molecular Modeling Studies of 4,5-Dihydro-1H-pyrazolo[4,3-h] quinazoline Derivatives as Potent CDK2/Cyclin A Inhibitors Using 3D-QSAR and Docking

CDK2/cyclin A has appeared as an attractive drug targets over the years with diverse therapeutic potentials. A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) followed by molecular docking studies were performed on a series of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as potent CDK2/cyclin A inhibitors. The CoMFA and CoMSIA models, using 38 molecules in the training set, gave r(2) (cv) values of 0.747 and 0.518 and r(2) values of 0.970 and 0.934, respectively. 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. Molecular docking was applied to explore the binding mode between the ligands and the receptor. The information obtained from molecular modeling studies may be helpful to design novel inhibitors of CDK2/cyclin A with desired activity.     

The Three Dimensional Quantitative Structure Activity Relationships (3D-QSAR) and Docking Studies of Curcumin Derivatives as Androgen Receptor Antagonists

Androgen receptor antagonists have been proved to be effective anti-prostate cancer agents. 3D-QSAR and Molecular docking methods were performed on curcumin derivatives as androgen receptor antagonists. The bioactive conformation was explored by docking the potent compound 29 into the binding site of AR. The constructed Comparative Molecular Field Analysis (CoMFA) and Comparative Similarity Indices Analysis (CoMSIA) models produced statistically significant results with the cross-validated correlation coefficients q(2) of 0.658 and 0.567, non-cross-validated correlation coefficients r(2) of 0.988 and 0.978, and predicted correction coefficients r(2) (pred) of 0.715 and 0.793, respectively. These results ensure the CoMFA and CoMSIA models as a tool to guide the design of novel potent AR antagonists. A set of 30 new analogs were proposed by utilizing the results revealed in the present study, and were predicted with potential activities in the developed models.