2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors

Synthesis, Antiproliferative Evaluation and QSAR Analysis of Novel Halogen- and Amidino-Substituted Benzothiazoles and Benzimidazoles

Syntheses of 6-halogen-substituted benzothiazoles were performed by condensation of 4-hydroxybenzaldehydes and 2-aminotiophenoles and subsequent O-alkylation with appropriate halides, whereas 6-amidino-substituted benzothiazoles were synthesized by condensation of 5-amidino-2-aminothiophenoles and corresponding benzaldehydes. While most of the compounds from non-substituted and halogen-substituted benzothiazole series showed marginal antiproliferative activity on tested tumor cell lines, amidino benzazoles exhibited stronger inhibitory activity. Generally, imidazolyl benzothiazoles showed pronounced and nonselective activity, with the exception of 36c which had a strong inhibitory effect on HuT78 cells (IC50 = 1.6 µM) without adverse cytotoxicity on normal BJ cells (IC50 >100 µM). Compared to benzothiazoles, benzimidazole structural analogs 45a−45c and 46c containing the 1,2,3-triazole ring exhibited pronounced and selective antiproliferative activity against HuT78 cells with IC50 < 10 µM. Moreover, compounds 45c and 46c containing the methoxy group at the phenoxy unit were not toxic to normal BJ cells. Of all the tested compounds, benzimidazole 45a with the unsubstituted phenoxy central core showed the most pronounced cell growth inhibition on THP1 cells in the nanomolar range (IC50 = 0.8 µM; SI = 70). QSAR models of antiproliferative activity for benzazoles on T-cell lymphoma (HuT78) and non-tumor MDCK-1 cells elucidated the effects of the substituents at position 6 of benzazoles, demonstrating their dependence on the topological and spatial distribution of atomic mass, polarizability, and van der Waals volumes. A notable cell cycle perturbation with higher accumulation of cells in the G2/M phase, and a significant cell increase in subG0/G1 phase were found in HuT78 cells treated with 36c, 42c, 45a−45c and 46c. Apoptotic morphological changes, an externalization of phosphatidylserine, and changes in the mitochondrial membrane potential of treated cells were observed as well.

Computational Modeling to Explain Why 5,5-Diarylpentadienamides are TRPV1 Antagonists

Several years ago, the crystallographic structures of the transient receptor potential vanilloid 1 (TRPV1) in the presence of agonists and antagonists were reported, providing structural information about its chemical activation and inactivation. TRPV1’s activation increases the transport of calcium and sodium ions, leading to the excitation of sensory neurons and the perception of pain. On the other hand, its antagonistic inactivation has been explored to design analgesic drugs. The interactions between the antagonists 5,5-diarylpentadienamides (DPDAs) and TRPV1 were studied here to explain why they inactivate TRPV1. The present work identified the structural features of TRPV1–DPDA complexes, starting with a consideration of the orientations of the ligands inside the TRPV1 binding site by using molecular docking. After this, a chemometrics analysis was performed (i) to compare the orientations of the antagonists (by using LigRMSD), (ii) to describe the recurrent interactions between the protein residues and ligand groups in the complexes (by using interaction fingerprints), and (iii) to describe the relationship between topological features of the ligands and their differential antagonistic activities (by using a quantitative structure–activity relationship (QSAR) with 2D autocorrelation descriptors). The interactions between the DPDA groups and the residues Y511, S512, T550, R557, and E570 (with a recognized role in the binding of classic ligands), and the occupancy of isoquinoline or 3-hydroxy-3,4-dihydroquinolin-2(1H)-one groups of the DPDAs in the vanilloid pocket of TRPV1 were clearly described. Based on the results, the structural features that explain why DPDAs inactivate TRPV1 were clearly exposed. These features can be considered for the design of novel TRPV1 antagonists.

Synthesis of diN-Substituted Glycyl-Phenylalanine Derivatives by Using Ugi Four Component Reaction and Their Potential as Acetylcholinesterase Inhibitors

Ugi four component reaction (Ugi-4CR) isocyanide-based multicomponent reactions were used to synthesize diN-substituted glycyl-phenylalanine (diNsGF) derivatives. All of the synthesized compounds were characterized by spectroscopic and spectrometric techniques. In order to evaluate potential biological applications, the synthesized compounds were tested in computational models that predict the bioactivity of organic molecules by using only bi-dimensional molecular information. The diNsGF derivatives were predicted as cholinesterase inhibitors. Experimentally, all of the synthesized diNsGF derivatives showed moderate inhibitory activities against acetylcholinesterase (AChE) and poor activities against butyrylcholinesterase (BuChE). Compound 7a has significant activity and selectivity against AChE, which reveals that the diNsGF scaffold could be improved to reach novel candidates by combining other chemical components of the Ugi-4CR in a high-throughput combinatorial screening experiment. Molecular docking experiments of diNsGF derivatives inside AChE suggest that these compounds placed the phenylalanine group at the peripheral site of AChE. The orientations and chemical interactions of diNsGF derivatives were analyzed, and the changeable groups were identified for future exploration of novel candidates that could lead to the improvement of diNsGF derivative inhibitory activities.

Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata

Both the acute toxicity and chronic toxicity data on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). However, most of the present modelling actions are focused on developing predictive models for the acute toxicity of chemicals to aquatic organisms. As regards chronic aquatic toxicity, considerable work is needed. The major objective of the present study was to construct in silico models for predicting chronic toxicity data for Daphnia magna and Pseudokirchneriella subcapitata. In the modelling, a set of chronic toxicity data was collected for D. magna (21 days no observed effect concentration (NOEC)) and P. subcapitata (72 h NOEC), respectively. Then, binary classification models were developed for D. magna and P. subcapitata by employing the k-nearest neighbour method (k-NN). The model assessment results indicated that the obtained optimum models had high accuracy, sensitivity and specificity. The model application domain was characterized by the Euclidean distance-based method. In the future, the data gap for other chemicals within the application domain on their chronic toxicity for D. magna and P. subcapitata could be filled using the models developed here.

Pred-binding: large-scale protein-ligand binding affinity prediction

Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by Ki values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for Ki predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.

PLS and shape-based similarity analysis of maleimides--GSK-3 inhibitors

CONTEXT

Glycogen synthase kinase-3 (GSK-3) overactivity was correlated with several pathologies including type 2 diabetes mellitus, Alzheimer's disease, cancer, inflammation, obesity, etc.

OBJECTIVE

The aim of the current investigation was to model the inhibitory activity of maleimide derivatives--inhibitors of GSK-3, to evaluate the impact of alignment on statistical performances of the Quantitative Structure-Activity Relationship (QSAR) and the effect of the template on shape-similarity--binding affinity relationship.

MATERIALS AND METHODS

Dragon descriptors were used to generate Projection to Latent Structures (PLS) models in order to identify the structural prerequisites of maleimides to inhibit GSK-3. Additionally, shape/volume structural analysis of binding site interactions was evaluated.

RESULTS

Reliable statistics R(2)(Y(CUM)) = 0.938/0.920, Q((2)(Y)(CUM)) = 0.866/0.838 for aligned and alignment free QSAR models and significant (Pearson, Kendall and Spearman) correlations between shape/volume similarity and affinities were obtained.

DISCUSSION AND CONCLUSIONS

The crucial structural features modulating the activity of maleimides include topology, charge, geometry, 2D autocorrelations, 3D-MoRSE as well as shape/volume and molecular flexibility.

Investigation of the differences in activity between hydroxycycloalkyl N1 substituted pyrazole derivatives as inhibitors of B-Raf kinase by using docking, molecular dynamics, QM/MM, and fragment-based de novo design: study of binding mode of diastereomer compounds

N1 substituted pyrazole derivatives show diverse B-Raf kinase inhibitory activities when different hydroxy-substituted cycloalkyl groups are placed at this position. Docking, molecular dynamics (MD) simulations, and hybrid calculation methods (Quantum Mechanics/Molecular Mechanics (QM/MM)) were performed on the complexes, in order to explain these differences. Docking of the inhibitors showed the same orientation that X-ray crystal structure of the analogous (1E)-5-[1-(4-piperidinyl)-3-(4-pyridinyl)-1H-pyrazol-4-yl]-2,3-dihydro-1H-inden-1-one oxime. MD simulations of the most active diastereomer compounds containing cis- and trans-3-hydroxycyclohexyl substituents showed stable interactions with residue Ile463 at the entrance of the B-Raf active site. On the other hand, the less active diastereomer compounds containing cis- and trans-2-hydroxycyclopentyl substituents showed interactions with inner residues Asn580 and Ser465. We found that the differences in activity can be explained by considering the dynamic interactions between the inhibitors and their surrounding residues within the B-Raf binding site. We also explained the activity trend by using a testing scoring function derived from more reliable QM/MM calculations. In addition, we search for new inhibitors from a virtual screening carried out by fragment-based de novo design. We generated a set of approximately 200 virtual compounds, which interact with Ile463 and fulfill druglikeness properties according to Lipinski, Veber, and Ghose rules.

Study of differences in the VEGFR2 inhibitory activities between semaxanib and SU5205 using 3D-QSAR, docking, and molecular dynamics simulations

Semaxanib (SU5416) and 3-[4'-fluorobenzylidene]indolin-2-one (SU5205) are structurally similar drugs that are able to inhibit vascular endothelial growth factor receptor-2 (VEGFR2), but the former is 87 times more effective than the latter. Previously, SU5205 was used as a radiolabelled inhibitor (as surrogate for SU5416) and a radiotracer for positron emission tomography (PET) imaging, but the compound exhibited poor stability and only a moderate IC(50) toward VEGFR2. In the current work, the relationship between the structure and activity of these drugs as VEGFR2 inhibitors was studied using 3D-QSAR, docking and molecular dynamics (MD) simulations. First, comparative molecular field analysis (CoMFA) was performed using 48 2-indolinone derivatives and their VEGFR2 inhibitory activities. The best CoMFA model was carried out over a training set including 40 compounds, and it included steric and electrostatic fields. In addition, this model gave satisfactory cross-validation results and adequately predicted 8 compounds contained in the test set. The plots of the CoMFA fields could explain the structural differences between semaxanib and SU5205. Docking and molecular dynamics simulations showed that both molecules have the same orientation and dynamics inside the VEGFR2 active site. However, the hydrophobic pocket of VEGFR2 was more exposed to the solvent media when it was complexed with SU5205. An energetic analysis, including Embrace and MM-GBSA calculations, revealed that the potency of ligand binding is governed by van der Waals contacts.

Docking and quantitative structure-activity relationship studies for 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline, 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline, and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine derivatives as c-Met kinase inhibitors

We have performed docking of 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline (FPTA), 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline (FPPA), and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine (AFPP) derivatives complexed with c-Met kinase to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 103 compounds with variations both in structure and activity. Docking helped to analyze the molecular features which contribute to a high inhibitory activity for the studied compounds. In addition, the predicted biological activities of the c-Met kinase inhibitors, measured as IC(50) values were obtained by using quantitative structure-activity relationship (QSAR) methods: Comparative molecular similarity analysis (CoMSIA) and multiple linear regression (MLR) with topological vectors. The best CoMSIA model included steric, electrostatic, hydrophobic, and hydrogen bond-donor fields; furthermore, we found a predictive model containing 2D-autocorrelation descriptors, GETAWAY descriptors (GETAWAY: Geometry, Topology and Atom-Weight AssemblY), fragment-based polar surface area (PSA), and MlogP. The statistical parameters: cross-validate correlation coefficient and the fitted correlation coefficient, validated the quality of the obtained predictive models for 76 compounds. Additionally, these models predicted adequately 25 compounds that were not included in the training set.

Computational study on the interaction of N1 substituted pyrazole derivatives with B-raf kinase: an unusual water wire hydrogen-bond network and novel interactions at the entrance of the active site

Docking and molecular dynamics (MD) simulations of N1 substituted pyrazole derivatives complexed with B-Raf kinase were performed to gain insight into the structural and energetic preferences of these inhibitors. First, a comparative study of fully automated docking programs AutoDock, ICM, GLIDE, and Surflex-Dock in closely approximating the X-ray crystal structure of the inhibitor (1E)-5-[1-(4-piperidinyl)-3-(4-pyridinyl)-1H-pyrazol-4-yl]-2,3-dihydro-1H-inden-1-one oxime was performed. Afterward, the dynamics of the above-mentioned compound and the less active analogous compounds with 1-methyl-4-piperidinyl and tetrahydro-2H-pyran-4-yl groups at position N1 of pyrazole ring inside the B-Raf active site were analyzed by MD simulations. We found that the most active compound has stable interactions with residues Ile463 and His539 at the entrance of the B-Raf active site. Those interactions were in very good agreement with more reliable quantum mechanics/molecular mechanics calculations performed on the torsional angle phi between the pyrazole ring and the substituents at position N1. In addition, we identified a water wire connecting N2 of the pyrazole ring, Cys532, and Ser536, which is composed of three water molecules for the most active compound. We found some differences in the water wire hydrogen-bond network formed by less active compounds. We suggest that the differences between these structural features are responsible for the differences in activity among the studied compounds.

Computational study of the interactions between guanine derivatives and cyclin-dependent kinase 2 (CDK2) by CoMFA and QM/MM

Comparative molecular field analysis (CoMFA) and QM/MM hybrid calculations were performed on 9H-purine derivatives as CDK2 inhibitors. CoMFA was carried out to describe the activities of 78 analogues. The models were applied to a training set including 64 compounds. The best CoMFA model included steric and electrostatic fields, had a good Q(2) value of 0.845, and adequately predicted the compounds contained in the test set. Furthermore, plots of the steric CoMFA field allowed conclusions to be drawn for the choice of suitable inhibitors. In addition, the dynamical behavior of compounds with 4-(aminosulfonyl)phenyl, 4-[(methylamino)sulfonyl]phenyl, 4-[(dimethylamino)sulfonyl]phenyl, and [3-methoxy-4-(aminosulfonyl)]phenyl groups at position 2 of the 9H-purine scaffold inside the CDK2 active site were analyzed by QM/MM calculations. The interactions of these compounds with residues Lys89, Asp86, and Ile10 were characterized.

2D Autocorrelation, CoMFA, and CoMSIA modeling of protein tyrosine kinases' inhibition by substituted pyrido[2,3-d]pyrimidine derivatives

2D Autocorrelation, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of substituted pyrido[2,3-d]pyrimidine derivatives to correlate platelet-derived growth factor receptor (PDGFR), fibroblast growth factor receptor (FGFR), and c-Src tyrosine kinases' inhibition with 2D and 3D structural properties of 22 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling each protein tyrosine kinase (PTK) inhibitory activity were selected by genetic algorithm (GA) and multiple linear regression (MLR) approach. The 2D autocorrelation space brings different descriptors for each PTK inhibition and suggests the atomic properties relevant for the inhibitors to interact with each PTK active site. CoMFA and CoMSIA were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results for all correlations. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the hydrophobic and H-bond donor fields around them.

Quantitative Structure?Activity Relationship Modeling of Growth Hormone Secretagogues Agonist Activity of some Tetrahydroisoquinoline 1-Carboxamides

Growth hormone secretagogue agonist activities for a data set of 45 tetrahydroisoquinoline 1-carboxamides were modeled using several kinds of molecular descriptors from dragon software. A linear model with six variables selected from a large pool of two-dimensional descriptors described 80% of cross-validation data variance. Similar results were found for a model obtained from a pool of three-dimensional descriptors. Size and hydrophilicity-related atomic properties such as mass, polarizability, and van der Waals volume were determined to be the most relevant for the differential growth hormone secretagogue agonist activities of the compounds studied. In addition, Artificial Neural Networks were trained using optimum variables from the linear models; however, they were found to overfit the data and resulted in similar or lower predictive power.