Mycobacterium tuberculosis
bacteria are cause deadly infections in patients. The rise of multidrug resistance associated with tuberculosis further makes the situation worse in treating the disease.
...M. tuberculosis
proteasome is necessary for the pathogenesis of the bacterium validated as an anti-tubercular target, thus making it an attractive enzyme for designing Mtb inhibitors. In this study, a computational screening approach was applied to identify new proteasome inhibitor candidates from a library of 50,000 compounds. This chemical library was procured from the ChemBridge (20,000 compounds) and the ChemDiv (30,000 compounds) databases. After a detailed analysis of the computational screening results, 50 in silico hits were retrieved and tested in vitro finding 15 compounds with
IC
50
values ranging from 35.32 to 64.15
μ
M on lysate. A structural analysis of these hits revealed that 14 of these compounds probably have non-covalent mode of binding to the target and have not reported for anti-tubercular or anti-proteasome activity. The binding interactions of all the 14 protein-inhibitor complexes were analyzed using molecular docking studies. Further, molecular dynamics simulations of the protein in complex with the two most promising hits were carried out so as to identify the key interactions and validate the structural stability.
UDP-glucuronosyltransferase 2B7 (UGT2B7) is an important enzyme responsible for clearance of many drugs. Here, we report two 3D quantitative structure-activity relationship (QSAR) models for UGT2B7 ...using the pharmacophore and VolSurf approach, respectively.
The dataset included 53 structurally diverse UGT2B7 substrates, 36 of which were used for the training set and 17 of which for the external test set. Pharmacophore-based 3D-QSAR model (or hypothesis) was developed using the Discovery Studio program. A user-defined "glucuronidation site" feature was forcefully included in a pharmacophore hypothesis. VolSurf-based 3D-QSAR model was generated using the VolSurf program. This involves calculation of VolSurf descriptors, variable selection with the FFD algorithm, and partial least squares (PLS) analyses.
The best pharmacophore model (r2 = 0.736) consists of one glucuronidation site, one hydrogen bond acceptor, and three hydrophobic regions. Using this model, Km values for 14 of 17 test substrates were predicted within one log unit. The yielded VolSurf (PLS) model with two components shows statistical significance in both fitting and internal predicting (r2 = 0.866, q2 = 0.728). Further, the Km values for all test substrates were predicted within one log unit. In addition, the VolSurf model reveals an overlay of chemical features influencing the enzyme-substrate binding. Those include molecular size and shape, integy moments, capacity factors, best volumes of DRY probe, H-bonding, and log P.
In conclusion, the pharmacophore and VolSurf approaches are successfully utilized to establish predictive models for UGT2B7. The derived models should be an efficient tool for high throughput prediction of UGT2B7 metabolism.
Tubulin inhibition represents an established target in the field of anticancer research, and over the last 20 years, an intensive search for new antimicrotubule agents has occurred. Indeed, in silico ...models have been presented that might aid the discovery of novel agents. Among these, a 7‐point pharmacophore model has been recently proposed. As a formal proof of this model, we carried out a ligand‐based virtual screening on the colchicine‐binding site. In vitro testing demonstrated that two compounds displayed a cytotoxic profile on neuroblastoma cancer cells (SH‐SY5H) and one had an antitubulinic profile.
An intensive search for new antimicrotubule agents has occurred. Indeed, in silico models have been presented that might aid the discovery of novel agents. Among these, a 7‐point pharmacophore model has been recently proposed. As a formal proof of this model, we carried out a ligand‐based virtual screening on the colchicine binding site. In vitro testing demonstrated that two compounds displayed a cytotoxic profile on neuroblastoma cancer cells (SH‐SY5H) and one had an antitubulinic profile.
In this study, ten anthra-, nine naphtho-, and five benzoquinone compounds of natural origin and five synthetic naphthoquinones were assessed, using an enzymatic in vitro assay, for their potential ...to inhibit cyclooxygenase-1 and -2 (COX-1 and COX-2), the key enzymes of the arachidonic acid cascade. IC₅₀ values comparable with COX reference inhibitor indomethacin were recorded for several quinones (primin, alkannin, diospyrin, juglone, 7-methyljuglone, and shikonin). For some of the compounds, we suggest the redox potential of quinones as the mechanism responsible for in vitro COX inhibition because of the quantitative correlation with their pro-oxidant effect. Structure-relationship activity studies revealed that the substitutions at positions 2 and 5 play the key roles in the COX inhibitory and pro-oxidant actions of naphthoquinones. In contrast, the redox mechanism alone could not explain the activity of primin, embelin, alkannin, and diospyrin. For these four quinones, molecular modeling suggested similar binding modes as for conventional nonsteroidal anti-inflammatory drugs (NSAIDs).
A single, merged pharmacophore hypothesis is derived combining 2000 pharmacophore models obtained during a 20 ns molecular dynamics simulation of a protein-ligand complex with one pharmacophore model ...derived from the initial PDB structure. This merged pharmacophore model contains all features that are present during the simulation and statistical information about the dynamics of the pharmacophore features. Based on the dynamics of the pharmacophore features we derive two distinctive feature patterns resulting in two different pharmacophore models for the analyzed system – the first model consists of features that are obtained from the PDB structure and the second uses two features that can only be derived from the molecular dynamics simulation. Both models can distinguish between active and decoy molecules in virtual screening. Our approach represents an objective way to add/remove features in pharmacophore models and can be of interest for the investigation of any naturally occurring system that relies on ligand-receptor interactions for its biological activity.
•79 MIF inhibitors were collected from published literature.•403 different pharmacophore models were developed.•QSAR modeling selected two pharmacophores.•Optimal pharmacophores and QSAR model were ...used to mine for new inhibitors.•10 inhibitors illustrated excellent potencies.
Recent research suggested the involvement of migration inhibitor factor (MIF) in cancer and inflammatory diseases, which prompted several attempts to develop new MIF inhibitors. Accordingly, we investigated the pharmacophoric space of 79 MIF inhibitors using seven diverse subsets of inhibitors to identify plausible binding hypotheses (pharmacophores). Subsequently, we implemented genetic algorithm and multiple linear regression analysis to select optimal combination of pharmacophores and physicochemical descriptors capable of explaining bioactivity variation within the training compounds (QSAR model, r63=0.62, F=42.8, rLOO2=0.721,rPRESS2 against 16 external test inhibitors=0.58). Two orthogonal pharmacophores appeared in the optimal QSAR model suggestive of at least two binding modes available to ligands inside MIF binding pocket. Subsequent validation using receiver operating characteristic (ROC) curves analysis established the validity of these two pharmacophores. We employed these pharmacophoric models and associated QSAR equation to screen the National Cancer Institute (NCI) list of compounds. Eight compounds gave >50% inhibition at 100μM. Two molecules illustrated >75% inhibition at 10μM.
Human neutrophil elastase inhibitors (HNE-Is) have been recently implicated in inflammatory diseases. Accordingly, we applied a drug discovery workflow to unveil novel inhibitory HNE leads via ...combining pharmacophore modeling, quantitative structure–activity relationship (QSAR) analysis, and in silico screening. We employed the pharmacophoric models and associated QSAR equation to screen the National Cancer Institute (NCI) list of compounds. Virtual screening identified 14 novel leads from NCI compounds. The most potent hit
126
exhibited 93 % inhibition at 10 μM.
As a result of the ring-into-ring conversion of nitrosoimidazole derivatives, we obtained a molecular scaffold that, when properly decorated, is able to decrease inotropy by blocking L-type calcium ...channels. Previously, we used this scaffold to develop a quantitative structure-activity relationship (QSAR) model, and we used the most potent oxadiazolothiazinone as a template for ligand-based virtual screening. Here, we enlarge the diversity of chemical decorations, present the synthesis and in vitro data for 11 new derivatives, and develop a new 3D-QSAR model with recent in silico techniques. We observed a key role played by the oxadiazolone moiety: given the presence of positively charged calcium ions in the transmembrane channel protein, we hypothesize the formation of a ternary complex between the oxadiazolothiazinone, the Ca2+ ion and the protein. We have supported this hypothesis by means of pharmacophore generation and through the docking of the pharmacophore into a homology model of the protein. We also studied with docking experiments the interaction with a homology model of P-glycoprotein, which is inhibited by this series of molecules, and provided further evidence toward the relevance of this scaffold in biological interactions.
A validated 3D pharmacophore model was generated for a series of ACE inhibitory peptides, which consisted of five features (two hydrophobic functions, two hydrogen bond acceptors, and a negative ...ionizable function). The built model was able to correctly predict the activity of known ACE inhibitors. The model was then used as query to search 3D databases of peptides. Three novel peptides (I, II and III) were synthesized and biologically evaluated
in vitro. It appears that the
in vitro activity of peptides I, II and III was consistent with their molecular modeling results. Our results provided confidence for the utility of the pharmacophore model to retrieve novel ACE inhibitory peptides with desired biological activity by virtual screening.
3D pharmacophore model for ACE inhibitory peptides was generated.
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► A validated 3D pharmacophore model was generated for ACE inhibitory peptides. ► The pharmacophore model was able to correctly predict the activity of known ACE inhibitors. ► The pharmacophore model can be used to retrieve novel ACE inhibitory peptides with desired biological activity.
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