Pharmacophore modeling and in silico screening for new P450 19 (aromatase) inhibitors

Cytochrome P450 19 (P450 19, aromatase) constitutes a successful target for the treatment of breast cancer. This study analyzes chemical features common to P450 19 inhibitors to develop ligand-based, selective pharmacophore models for this enzyme. The HipHop and HypoRefine algorithms implemented in the Catalyst software package were employed to create both common feature and quantitative models. The common feature model for P450 19 includes two ring aromatic features in its core and two hydrogen bond acceptors at the ends. The models were used as database search queries to identify active compounds from the NCI database.     

A ligand-based molecular modeling study on some matrix metalloproteinase-1 inhibitors using several 3D QSAR techniques

Some three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) for a series of 84 proline-based plus 12 structurally more diversified nonproline matrix metalloproteinase inhibitors. The structures of these inhibitors were built from a structure template extracted from the crystal structure of stromelysin. The structures built were divided into the training and test sets for both the CoMFA and CoMSIA analyses for each being composed of 60 and 24 inhibitors, respectively. The structures in the training set were aligned using some alignment rules derived from the analysis of the Ligplot program on a recent crystal structure of ligand-collagenase-1 complex. Some stepwise CoMSIA's were performed on the aligned training set on which the best CoMFA result was obtained. The best CoMSIA model was identified from the stepwise results, and the corresponding pharmacophore features were used for the construction of a pharmacophore hypothesis by the Catalyst 4.9 program. The training set was extended to include 11 structurally more diversified and nonproline inhibitors. To construct a pharmacophore hypothesis, the conformation of 60 structurally aligned proline-based inhibitors was fixed, while that of the 11 structurally more diversified nonproline inhibitors was allowed to vary during the hypothesis construction process. It was found that the predicted activities by the top hypothesis constructed for both the training and test sets were as good in statistics as those predicted by the best CoMSIA model from which the hypothesis was derived. The top hypothesis was mapped onto the structures of several highly active inhibitors selected from both the training and test sets. The goodness of mapping on each inhibitor was found to be correlated well with the activity of each inhibitor.     

Docking-based comparative intermolecular contacts analysis as new 3-D QSAR concept for validating docking studies and in silico screening: NMT and GP inhibitors as case studies

The significant role played by docking algorithms in drug discovery combined with their serious pitfalls prompted us to envisage a novel concept for validating docking solutions, namely, docking-based comparative intermolecular contacts analysis (dbCICA). This novel approach is based on the number and quality of contacts between docked ligands and amino acid residues within the binding pocket. It assesses a particular docking configuration on the basis of its ability to align a set of ligands within a corresponding binding pocket in such a way that potent ligands come into contact with binding site spots distinct from those approached by low-affinity ligands and vice versa. In other words, dbCICA evaluates the consistency of docking by assessing the correlation between ligands' affinities and their contacts with binding site spots. Optimal dbCICA models can be translated into valid pharmacophore models that can be used as 3-D search queries to mine structural databases for new bioactive compounds. dbCICA was implemented to search for new inhibitors of candida N-myristoyl transferase as potential antifungal agents and glycogen phosphorylase (GP) inhibitors as potential antidiabetic agents. The process culminated in five selective micromolar antifungal leads and nine GP inhibitory leads.     

Structure-based 3D QSAR and design of novel acetylcholinesterase inhibitors

The paper describes the construction, validation and application of a structure-based 3D QSAR model of novel acetylcholinesterase (AChE) inhibitors. Initial use was made of four X-ray structures of AChE complexed with small, non-specific inhibitors to create a model of the binding of recently developed aminopyridazine derivatives. Combined automated and manual docking methods were applied to dock the co-crystallized inhibitors into the binding pocket. Validation of the modelling process was achieved by comparing the predicted enzyme-bound conformation with the known conformation in the X-ray structure. The successful prediction of the binding conformation of the known inhibitors gave confidence that we could use our model to evaluate the binding conformation of the aminopyridazine compounds. The alignment of 42 aminopyridazine compounds derived by the docking procedure was taken as the basis for a 3D QSAR analysis applying the GRID/GOLPE method. A model of high quality was obtained using the GRID water probe, as confirmed by the cross-validation method (q² _LOO=0.937, q² _{L50% O}=0.910). The validated model, together with the information obtained from the calculated AChE-inhibitor complexes, were considered for the design of novel compounds. Seven designed inhibitors which were synthesized and tested were shown to be highly active. After performing our modelling study the X-ray structure of AChE complexed with donepezil, an inhibitor structurally related to the developed aminopyirdazines, has been made available. The good agreement found between the predicted binding conformation of the aminopyridazines and the one observed for donepezil in the crystal structure further supports our developed model.     

Molecular modeling on pyruvate phosphate dikinase of Entamoeba histolytica and in silico virtual screening for novel inhibitors

Pyruvate phosphate dikinase (PPDK) is the key enzyme essential for the glycolytic pathway in most common and perilous parasite Entamoeba histolytica. Inhibiting the function of this enzyme could control the wide spread of intestinal infections caused by Entamoeba histolytica in humans. With this objective, we modeled the three dimensional structure of the PPDK protein. We used templates with 51% identity and 67% similarity to employ homology-modeling approach. Stereo chemical quality of protein structure was validated by protein structure validation program PROCHECK and VERIFY3D. Experimental proof available in literature along with the in silico studies indicated Lys21, Arg91, Asp323, Glu325 and Gln337 to be the probable active sites in the target protein. Virtual screening was carried out using the genetic docking algorithm GOLD and a consensus scoring function X-Score to substantiate the prediction. The small molecule libraries (ChemDivision database, Diversity dataset, Kinase inhibitor database) were used for screening process. Along with the high scoring results, the interaction studies provided promising ligands for future experimental screening to inhibit the function of PPDK in Entamoeba histolytica. Further, the phylogeny study was carried out to assess the possibility of using the proposed ligands as inhibitors in related pathogens.     

Novel inhibitors of trihydroxynaphthalene reductase with antifungal activity identified by ligand-based and structure-based virtual screening

Curvularia lunata is a dark pigmented fungus that is the causative agent of several diseases in plants and in both immunodeficient and immunocompetent patients. 1,8-Dihydroxynaphthalene-melanin is found in the cell wall of C. lunata and is believed to be the important virulence factor of dematiaceous fungi. Trihydroxynaphthalene reductase is an enzyme of the 1,8-dihydroxynaphthalene-melanin biosynthetic pathway, and it thus represents an emerging target for the development of novel fungicides and antimycotics. In the present study, we describe novel inhibitors of trihydroxynaphthalene reductase from C. lunata. These inhibitors were identified by ligand-based three-dimensional similarity searching and docking to a homology-built model and by subsequent biochemical and antifungal evaluation. Discovery of competitive inhibitors with K(i) values in low micromolar and even nanomolar concentration range proves the aplicability of homology-built model of 3HNR for hit finding by virtual screening methods.     

Development of predictive pharmacophore model for in silico screening,and 3D QSAR CoMFA and CoMSIA studies for lead optimization,for designing of potent tumor necrosis factor alpha converting enzyme inhibitors

A chemical feature-based pharmacophore model was developed for Tumor Necrosis Factor-α converting enzyme (TACE) inhibitors. A five point pharmacophore model having two hydrogen bond acceptors (A), one hydrogen bond donor (D) and two aromatic rings (R) with discrete geometries as pharmacophoric features was developed. The pharmacophore model so generated was then utilized for in silico screening of a database. The pharmacophore model so developed was validated by using four compounds having proven TACE inhibitory activity which were grafted into the database. These compounds mapped well onto the five listed pharmacophoric features. This validated pharmacophore model was also used for alignment of molecules in CoMFA and CoMSIA analysis. The contour maps of the CoMFA/CoMSIA models were utilized to provide structural insight for activity improvement of potential novel TACE inhibitors. The pharmacophore model so developed could be used for in silico screening of any commercial/in house database for identification of TACE inhibiting lead compounds, and the leads so identified could be optimized using the developed CoMSIA model. The present work highlights the tremendous potential of the two mutually complementary ligand-based drug designing techniques (i.e. pharmacophore mapping and 3D-QSAR analysis) using TACE inhibitors as prototype biologically active molecules.     

Multiple QSAR and molecular modelling for identification of potent human adenovirus inhibitors

This study has investigated docking-based 2D- and 3D-quantitative structure-activity relationships (QSARs) for a range of 53 hydroxybenzamide analogues as anti- Human adenoviruses (HAdVs). The best 3D-QSAR (Schrodinger, LLC, NY, 2020) and 2D-QSAR models were obtained for the training set and were found to be statistically significant, with cross-validated coefficients (q²) of 0.6775 and 0.7875, and coefficients of determination (r²) of 0.8106 and 0.8122, respectively. Our in-silico docking and virtual screening studies revealed significant higher binding affinity of dataset molecule 34 (-141.444 ​kcal/mol) and hit ZINC01088642 (-114.357 ​kcal/mol) with 4PIE protein than the standard drugs. In in-silico ADME/toxicity studies, molecule 34 and proposed hit ZINC01088642 were found safe with good intestinal absorption, aqueous solubility, medium blood–brain barrier (BBB), no eye corrosion, no skin irritancy, and non-mutagenic profiles. Molecular dynamics analysis showed good stability of complex, hit ZINC01088642 with protein, 4PIE over the simulation period of 20 ns. We believe that further experimental, as well as in-vitro investigation, will shed more lights on the identification of ZINC01088642 as a potential human adenovirus agent.     

Molecular docking and QSAR of aplyronine A and analogues: potent inhibitors of actin

Actin-binding natural products have been identified as a potential basis for the design of cancer therapeutic agents. We report flexible docking and QSAR studies on aplyronine A analogues. Our findings show the macrolide ‘tail’ to be fundamental for the depolymerisation effect of actin-binding macrolides and that it is the tail which forms the initial interaction with the actin rather than the macrocycle, as previously believed. Docking energy scores for the compounds were highly correlated with actin depolymerisation activity. The 3D-QSAR models were predictive, with the best model giving a q ² value of 0.85 and a r ² of 0.94. Results from the docking simulations and the interpretation from QSAR “coeff*stdev” contour maps provide insight into the binding mechanism of each analogue and highlight key features that influence depolymerisation activity. The results herein may aid the design of a putative set of analogues that can help produce efficacious and tolerable anti-tumour agents. Finally, using the best QSAR model, we have also made genuine predictions for an independent set of recently reported aplyronine analogues.     

Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors

A new version of an ant colony optimization (ACO) algorithm has been proposed. A modified ACO algorithm is proposed to select variables in QSAR modeling and to predict inhibiting action of some diarylimidazole derivatives on cyclooxygenase (COX) enzyme. As a comparison to this method, the evolution algorithm (EA) was also tested. Experimental results have demonstrated that the modified ACO is a useful tool for variable selection that needs few parameters to be adjusted and converges quickly toward the optimal position.     

NMR and in silico screening

NMR-based screening and virtual, or in silico, screening can be highly complementary and synergistic. NMR-based screening is a rapid and reliable method for validating hits that come from in silico screens. In addition, ligand-binding data derived from NMR-based screens can focus and direct subsequent in silico screening. We will first give a short overview of existing NMR and in silico screening methods, discuss the drawbacks associated with each, and finally present applications that highlight the combination of the two technologies.     

QSAR modeling and chemical space analysis of antimalarial compounds

Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.