Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch.
Structure validation has become a major issue in the structural biology community, and an essential step is checking the ligand structure. This paper introduces MotiveValidator, a web-based application for the validation of ligands and residues in PDB or PDBx/mmCIF format files provided by the user. Specifically, MotiveValidator is able to evaluate in a straightforward manner whether the ligand or residue being studied has a correct annotation (3-letter code), i.e. if it has the same topology and stereochemistry as the model ligand or residue with this annotation. If not, MotiveValidator explicitly describes the differences. MotiveValidator offers a user-friendly, interactive and platform-independent environment for validating structures obtained by any type of experiment. The results of the validation are presented in both tabular and graphical form, facilitating their interpretation. MotiveValidator can process thousands of ligands or residues in a single validation run that takes no more than a few minutes. MotiveValidator can be used for testing single structures, or the analysis of large sets of ligands or fragments prepared for binding site analysis, docking or virtual screening. MotiveValidator is freely available via the Internet at http://ncbr.muni.cz/MotiveValidator.
- MeSH
- Acetylglucosamine chemistry MeSH
- Ephrin-B3 chemistry MeSH
- Glycoproteins chemistry MeSH
- Internet MeSH
- Cholic Acid chemistry MeSH
- Ligands MeSH
- Macromolecular Substances chemistry MeSH
- Proteins chemistry MeSH
- Software * MeSH
- Binding Sites MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
502 s.
On the basis of the highly branched ovomucoid-type undecasaccharide that had been shown previously to be an endogenous ligand for CD69 leukocyte receptor, a systematic investigation of smaller oligosaccharide mimetics was performed based on linear and branched N-acetyl-d-hexosamine homooligomers prepared synthetically using hitherto unexplored reaction schemes. The systematic structure-activity studies revealed the tetrasaccharide GlcNAcbeta1-3(GlcNAcbeta1-4)(GlcNAcbeta1-6)GlcNAc (compound 52) and its alpha-benzyl derivative 49 as the best ligand for CD69 with IC(50) as high as 10(-9) M. This compound thus approaches the affinity of the classical high-affinity neoglycoprotein ligand GlcNAc(23)BSA. Compound 68, GlcNAc tetrasaccharide 52 dimerized through a hydrophilic flexible linker, turned out to be effective in activating CD69(+) lymphocytes. It also proved efficient in enhancing natural killing in vitro, decreasing the growth of tumors in vivo, and activating the CD69(+) tumor infiltrating lymphocytes examined ex vivo. This compound is thus a candidate for carbohydrate-based immunomodulators with promising antitumor potential.
- MeSH
- Acetylglucosamine analogs & derivatives chemical synthesis chemistry pharmacology MeSH
- Lymphocyte Activation MeSH
- Killer Cells, Natural drug effects immunology metabolism MeSH
- Antigens, CD metabolism MeSH
- Antigens, Differentiation, T-Lymphocyte metabolism MeSH
- Dimerization MeSH
- Immunologic Factors chemical synthesis chemistry pharmacology MeSH
- Rats MeSH
- NK Cell Lectin-Like Receptor Subfamily B metabolism MeSH
- Lectins, C-Type metabolism MeSH
- Humans MeSH
- Ligands MeSH
- Melanoma, Experimental immunology pathology MeSH
- Molecular Mimicry MeSH
- Models, Molecular MeSH
- Molecular Sequence Data MeSH
- Mice, Inbred C57BL MeSH
- Mice MeSH
- Cell Line, Tumor MeSH
- Oligosaccharides chemical synthesis chemistry pharmacology MeSH
- Antineoplastic Agents chemical synthesis chemistry pharmacology MeSH
- Recombinant Proteins chemistry MeSH
- Carbohydrate Sequence MeSH
- Drug Screening Assays, Antitumor MeSH
- In Vitro Techniques MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Humans MeSH
- Mice MeSH
- Female MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Retracted Publication MeSH
- Research Support, Non-U.S. Gov't MeSH
A single protein structure is rarely sufficient to capture the conformational variability of a protein. Both bound and unbound (holo and apo) forms of a protein are essential for understanding its geometry and making meaningful comparisons. Nevertheless, docking or drug design studies often still consider only single protein structures in their holo form, which are for the most part rigid. With the recent explosion in the field of structural biology, large, curated datasets are urgently needed. Here, we use a previously developed application (AHoJ) to perform a comprehensive search for apo-holo pairs for 468,293 biologically relevant protein-ligand interactions across 27,983 proteins. In each search, the binding pocket is captured and mapped across existing structures within the same UniProt, and the mapped pockets are annotated as apo or holo, based on the presence or absence of ligands. We assemble the results into a database, AHoJ-DB (www.apoholo.cz/db), that captures the variability of proteins with identical sequences, thereby exposing the agents responsible for the observed differences in geometry. We report several metrics for each annotated pocket, and we also include binding pockets that form at the interface of multiple chains. Analysis of the database shows that about 24% of the binding sites occur at the interface of two or more chains and that less than 50% of the total binding sites processed have an apo form in the PDB. These results can be used to train and evaluate predictors, discover potentially druggable proteins, and reveal protein- and ligand-specific relationships that were previously obscured by intermittent or partial data. Availability: www.apoholo.cz/db.
- MeSH
- Apoproteins chemistry metabolism MeSH
- Databases, Protein * MeSH
- Protein Conformation * MeSH
- Humans MeSH
- Ligands MeSH
- Models, Molecular MeSH
- Proteins * chemistry metabolism MeSH
- Protein Binding * MeSH
- Binding Sites MeSH
- Computational Biology methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
A series of new benzene-based derivatives was designed, synthesized and comprehensively characterized. All of the tested compounds were evaluated for their in vitro ability to potentially inhibit the acetyl- and butyrylcholinesterase enzymes. The selectivity index of individual molecules to cholinesterases was also determined. Generally, the inhibitory potency was stronger against butyryl- compared to acetylcholinesterase; however, some of the compounds showed a promising inhibition of both enzymes. In fact, two compounds (23, benzyl ethyl(1-oxo-1-phenylpropan-2-yl)carbamate and 28, benzyl (1-(3-chlorophenyl)-1-oxopropan-2-yl) (methyl)carbamate) had a very high selectivity index, while the second one (28) reached the lowest inhibitory concentration IC50 value, which corresponds quite well with galanthamine. Moreover, comparative receptor-independent and receptor-dependent structure⁻activity studies were conducted to explain the observed variations in inhibiting the potential of the investigated carbamate series. The principal objective of the ligand-based study was to comparatively analyze the molecular surface to gain insight into the electronic and/or steric factors that govern the ability to inhibit enzyme activities. The spatial distribution of potentially important steric and electrostatic factors was determined using the probability-guided pharmacophore mapping procedure, which is based on the iterative variable elimination method. Additionally, planar and spatial maps of the host⁻target interactions were created for all of the active compounds and compared with the drug molecules using the docking methodology.
- MeSH
- Acetylcholinesterase metabolism MeSH
- Principal Component Analysis MeSH
- Benzene chemical synthesis chemistry pharmacology MeSH
- Butyrylcholinesterase metabolism MeSH
- Cholinesterase Inhibitors chemical synthesis chemistry pharmacology MeSH
- Electrophorus MeSH
- Inhibitory Concentration 50 MeSH
- Carbamates chemical synthesis chemistry pharmacology MeSH
- Horses MeSH
- Ligands MeSH
- Probability MeSH
- Drug Design MeSH
- Molecular Docking Simulation MeSH
- Structure-Activity Relationship MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
To predict unknown reactivation potencies of 12 mono- and bis-pyridinium aldoximes for VX-inhibited rat acetylcholinesterase (rAChE), three-dimensional quantitative structure-activity relationship (3D QSAR) analysis has been carried out. Utilizing molecular interaction fields (MIFs) calculated by molecular mechanical (MMFF94) and quantum chemical (B3LYP/6-31G*) methods, two satisfactory ligand-based CoMFA models have been developed: 1. R(2)=0.9989, Q(LOO)(2)=0.9090, Q(LTO)(2)=0.8921, Q(LMO(20%))(2)=0.8853, R(ext)(2)=0.9259, SDEP(ext)=6.8938; 2. R(2)=0.9962, Q(LOO)(2)=0.9368, Q(LTO)(2)=0.9298, Q(LMO(20%))(2)=0.9248, R(ext)(2)=0.8905, SDEP(ext)=6.6756. High statistical significance of the 3D QSAR models has been achieved through the application of several data noise reduction techniques (i.e. smart region definition SRD, fractional factor design FFD, uninformative/iterative variable elimination UVE/IVE) on the original MIFs. Besides the ligand-based CoMFA models, an alignment molecular set constructed by flexible molecular docking has been also studied. The contour maps as well as the predicted reactivation potencies resulting from 3D QSAR analyses help better understand which structural features are associated with increased reactivation potency of studied compounds.
- MeSH
- Acetylcholinesterase chemistry MeSH
- Enzyme Activation MeSH
- Chemical Warfare Agents chemistry MeSH
- Cholinesterase Inhibitors chemistry MeSH
- GPI-Linked Proteins agonists antagonists & inhibitors chemistry MeSH
- Kinetics MeSH
- Rats MeSH
- Quantitative Structure-Activity Relationship MeSH
- Quantum Theory MeSH
- Ligands MeSH
- Organothiophosphorus Compounds chemistry MeSH
- Oximes chemistry MeSH
- Pyridinium Compounds chemistry MeSH
- Cholinesterase Reactivators chemistry MeSH
- Molecular Dynamics Simulation MeSH
- Molecular Docking Simulation MeSH
- Thermodynamics MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
The authors would like to add the funding number to the published article [...].
- Publication type
- Published Erratum MeSH
A structural series of 7-MEOTA-adamantylamine thioureas was designed, synthesized and evaluated as inhibitors of human acetylcholinesterase (hAChE) and human butyrylcholinesterase (hBChE). The compounds were prepared based on the multi-target-directed ligand strategy with different linker lengths (n = 2-8) joining the well-known NMDA antagonist adamantine and the hAChE inhibitor 7-methoxytacrine (7-MEOTA). Based on in silico studies, these inhibitors proved dual binding site character capable of simultaneous interaction with the peripheral anionic site (PAS) of hAChE and the catalytic active site (CAS). Clearly, these structural derivatives exhibited very good inhibitory activity towards hBChE resulting in more selective inhibitors of this enzyme. The most potent cholinesterase inhibitor was found to be thiourea analogue 14 (with an IC₅₀ value of 0.47 µM for hAChE and an IC₅₀ value of 0.11 µM for hBChE, respectively). Molecule 14 is a suitable novel lead compound for further evaluation proving that the strategy of dual binding site inhibitors might be a promising direction for development of novel AD drugs.
- MeSH
- Acetylcholinesterase metabolism MeSH
- Alzheimer Disease drug therapy MeSH
- Amantadine chemical synthesis chemistry pharmacology therapeutic use MeSH
- Cholinesterase Inhibitors chemical synthesis chemistry pharmacology therapeutic use MeSH
- Dimerization * MeSH
- Enzyme Assays MeSH
- Inhibitory Concentration 50 MeSH
- Humans MeSH
- Models, Molecular * MeSH
- Reference Standards MeSH
- Molecular Docking Simulation MeSH
- Tacrine analogs & derivatives chemical synthesis chemistry pharmacology therapeutic use MeSH
- Thiourea chemistry MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Simul 5 Complex is a one-dimensional dynamic simulation software designed for electrophoresis, and it is based on a numerical solution of the governing equations, which include electromigration, diffusion and acid-base equilibria. A new mathematical model has been derived and implemented that extends the simulation capabilities of the program by complexation equilibria. The simulation can be set up with any number of constituents (analytes), which are complexed by one complex-forming agent (ligand). The complexation stoichiometry is 1:1, which is typical for systems containing cyclodextrins as the ligand. Both the analytes and the ligand can have multiple dissociation states. Simul 5 Complex with the complexation mode runs under Windows and can be freely downloaded from our web page http://natur.cuni.cz/gas. The article has two separate parts. Here, the mathematical model is derived and tested by simulating the published results obtained by several methods used for the determination of complexation equilibrium constants: affinity capillary electrophoresis, vacancy affinity capillary electrophoresis, Hummel-Dreyer method, vacancy peak method, frontal analysis, and frontal analysis continuous capillary electrophoresis. In the second part of the paper, the agreement of the simulated and the experimental data is shown and discussed.
