3D-QSAR studies on fluroquinolones derivatives as inhibitors for tuberculosis.
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ABSTRACT:
SUBMITTER: Bhattacharjee A
PROVIDER: S-EPMC3346023 | biostudies-literature | 2012
REPOSITORIES: biostudies-literature
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Project description:Sortase A (SrtA) is an enzyme that catalyzes the attachment of proteins to the cell wall of Gram-positive bacterial membrane, preventing the spread of pathogenic bacterial strains. Here, one class of oxadiazole compounds was distinguished as an efficient inhibitor of SrtA via the "S. aureus Sortase A" substrate-based virtual screening. The current study on 3D-QSAR was done by utilizing preparation of the structure in the Schrödinger software suite and an assessment of 120 derivatives with the crystal structure of 1,2,4-oxadiazole which was extracted from the PDB data bank. The docking operation of the best compound in terms of pMIC (pMIC = 2.77) was done to determine the drug likeliness and binding form of 1,2,4-oxadiazole derivatives as antibiotics in the active site. Using the kNN-MFA way, seven models of 3D-QSAR were created and amongst them, and one model was selected as the best. The chosen model based on q 2 (pred_r 2) and R 2 values related to the sixth factor of PLS illustrates better and more acceptable external and internal predictions. Values of crossvalidation (pred_r 2), validation (q 2), and F were observed 0.5479, 0.6319, and 179.0, respectively, for a test group including 24 molecules and the training group including 96 molecules. The external reliability outcomes showed that the acceptable and the selective 3D-QSAR model had a high predictive potential (R 2 = 0.9235) which was confirmed by the Y-randomization test. Besides, the model applicability domain was described successfully to validate the estimation of the model.
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Project description:PTP-MEG2 plays a critical role in the diverse cell signalling processes, so targeting PTP-MEG2 is a promising strategy for various human diseases treatments. In this study, a series of novel dibenzofuran derivatives was synthesized and assayed for their PTP-MEG2 inhibitory activities. 10a with highest inhibitory activity (320 nM) exhibited significant selectivity for PTP-MEG2 over its close homolog SHP2, CDC25 (IC50 > 50 μM). By means of the powerful ''HipHop'' technique, a 3D-QSAR study was carried out to explore structure activity relationship of these molecules. The generated pharmacophore model revealed that the one RA, three Hyd, and two HBA features play an important role in binding to the active site of the target protein-PTP-MEG2. Docking simulation study indicated that 10a achieved its potency and specificity for PTP-MEG2 by targeting unique nearby peripheral binding pockets and the active site. The absorption, distribution, metabolism and excretion (ADME) predictions showed that the 11 compounds hold high potential to be novel lead compounds for targeting PTP-MEG2. Our findings here can provide a new strategy or useful insights for designing the effective PTP-MEG2 inhibitors.
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Project description:This work aimed to construct 3D-QSAR CoMFA and CoMSIA models for a series of 31 FAAH inhibitors, containing the 1,3,4-oxadiazol-2-one moiety. The obtained models were characterized by good statistical parameters: CoMFA Q2 = 0.61, R2 = 0.98; CoMSIA Q2 = 0.64, R2 = 0.93. The CoMFA model field contributions were 54.1% and 45.9% for steric and electrostatic fields, respectively. In the CoMSIA model, electrostatic, steric, hydrogen bond donor, and hydrogen acceptor properties were equal to 34.6%, 23.9%, 23.4%, and 18.0%, respectively. These models were validated by applying the leave-one-out technique, the seven-element test set (CoMFA r2test-set = 0.91; CoMSIA r2test-set = 0.91), a progressive scrambling test, and external validation criteria developed by Golbraikh and Tropsha (CoMFA r20 = 0.98, k = 0.95; CoMSIA r20 = 0.98, k = 0.89). As the statistical significance of the obtained model was confirmed, the results of the CoMFA and CoMSIA field calculation were mapped onto the enzyme binding site. It gave us the opportunity to discuss the structure-activity relationship based on the ligand-enzyme interactions. In particular, examination of the electrostatic properties of the established CoMFA model revealed fields that correspond to the regions where electropositive substituents are not desired, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one moiety. This highlights the importance of heterocycle, a highly electronegative moiety in this area of each ligand. Examination of hydrogen bond donor and acceptor properties contour maps revealed several spots where the implementation of another hydrogen-bond-donating moiety will positively impact molecules' binding affinity, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one ring. On the other hand, there is a large isopleth that refers to the favorable H-bond properties close to the terminal phenoxy group of a ligand, which means that, generally speaking, H-bond acceptors are desired in this area.
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Project description:BackgroundThe computational development of human monoamine oxidase (MAO) inhibitors led to advancement in drug design and the treatment of many neurodegenerative diseases and neuropsychiatric disorders. The computational development of human monoamine oxidase (MAO) inhibitors led to advancement in drug design and the treatment of many neurodegenerative diseases and neuropsychiatric disorders. Different natural heterocyclic structures are reported to display selective MAO inhibitory activity by preclinical and in-silico modeling.ObjectiveCurrently, the major interest is devoted to the study of natural based therapeutic agents from the different categories. Therefore, we presenting the review to critically discuss and outline the recent advances in our knowledge on the importance of natural and natural based ligand-MAO insilico methods for novel MAO inhibitors.DiscussionSeveral natural and related synthetic heterocyclic compounds such as coumarins, β- carboline, piperine, naphthoquinone, morpholine, caffeine, amphetamine moreover flavonoids, chalcones, xanthones, curcumin are discussed for their MAO inhibitory profile along with molecular docking and quantitative structure-activity relationship studies.ConclusionIt is clear that, by this computational drug design approach, more particular, reversible and potent compounds can be proposed as MAO inhibitors by exact changes on the fundamental framework.
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Project description:Influenza virus disease is one of the most infectious diseases responsible for many human deaths, and the high mutability of the virus causes drug resistance effects in recent times. As such, it became necessary to explore more inhibitors that could avert future influenza pandemics. The present research utilized some in-silico modelling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions on some 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase (NA) inhibitors. The 2D-QSAR modelling results revealed GFA-MLR ( Rtrain2 =0.8414, Q2 = 0.7680) and GFA-ANN ( Rtrain 2 =0.8754, Q2 = 0.8753) models with the most relevant descriptors (MATS3i, SpMax5_Bhe, minsOH and VE3_D) for predicting the inhibitory activities of the molecules which has passed the global criteria of accepting QSAR models. The results of the 3D-QSAR modelling results showed that CoMFA_ES ( Rtrain 2 =0.9030, Q2 = 0.5390) and CoMSIA_EA ( Rtrain2 =0.880, Q2 = 0.547) models are having good predicting ability among other developed models. The molecules were virtually screened via molecular docking simulation with the active site of NA protein receptor (pH1N1) which confirms their resilient potency when compared with zanamivir standard drug. Molecule 11 as the most potent molecule formed more H-bond interactions with the key residues such as TRP178, ARG152, ARG292, ARG371, and TYR406 that triggered the catalytic reactions for NA inhibition. Furthermore, six (6) molecules (9, 10, 11, 17, 22, and 31) with relatively high inhibitory activities and docking scores were identified as the possible leads for in-silico exploration of novel NA inhibitors. The drug-likeness and ADMET predictions of the lead molecules revealed non-violation of Lipinski's rule and good pharmacokinetic profiles respectively, which are important guidelines for rational drug design. Hence, the outcome of this study overlaid a solid foundation for the in-silico design and exploration of novel NA inhibitors with improved potency.
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Project description:Human mitotic kinesin Eg5 plays an essential role in mitoses and is an interesting drug target against cancer. To find the correlation between Eg5 and its inhibitors, structure-based 3D-quantitative structure-activity relationship (QSAR) studies were performed on a series of dihydropyrazole and dihydropyrrole derivatives using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Based on the LigandFit docking results, predictive 3D-QSAR models were established, with cross-validated coefficient values (q²) up to 0.798 for CoMFA and 0.848 for CoMSIA, respectively. Furthermore, the CoMFA and CoMSIA models were mapped back to the binding sites of Eg5, which could provide a better understanding of vital interactions between the inhibitors and the kinase. Ligands binding in hydrophobic part of the inhibitor-binding pocket were found to be crucial for potent ligand binding and kinases selectivity. The analyses may be used to design more potent EG5 inhibitors and predict their activities prior to synthesis.
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Project description: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 R(2) of 0.9356, Q(2) 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 (R(3) substituent), hydrophilic substituents near the X(6) 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.
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Project description:PGAM1 is overexpressed in a wide range of cancers, thereby promoting cancer cell proliferation and tumor growth, so it is gradually becoming an attractive target. Recently, a series of inhibitors with various structures targeting PGAM1 have been reported, particularly anthraquinone derivatives. In present study, the structure-activity relationships and binding mode of a series of anthraquinone derivatives were probed using three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking, and molecular dynamics (MD) simulations. Comparative molecular field analysis (CoMFA, r2 = 0.97, q2 = 0.81) and comparative molecular similarity indices analysis (CoMSIA, r2 = 0.96, q2 = 0.82) techniques were performed to produce 3D-QSAR models, which demonstrated satisfactory results, especially for the good predictive abilities. In addition, molecular dynamics (MD) simulations technology was employed to understand the key residues and the dominated interaction between PGAM1 and inhibitors. The decomposition of binding free energy indicated that the residues of F22, K100, V112, W115, and R116 play a vital role during the ligand binding process. The hydrogen bond analysis showed that R90, W115, and R116 form stable hydrogen bonds with PGAM1 inhibitors. Based on the above results, 7 anthraquinone compounds were designed and exhibited the expected predictive activity. The study explored the structure-activity relationships of anthraquinone compounds through 3D-QSAR and molecular dynamics simulations and provided theoretical guidance for the rational design of new anthraquinone derivatives as PGAM1 inhibitors.
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Project description:1,3,4-Thiadiazole and sugar-derived molecules have proven to be promising agrochemicals with growth promoting, insecticidal and fungicidal activities. In the research field of agricultural fungicide, applying union of active group we synthesized a new set of 1,3,4-thiadiazole xylofuranose derivatives and all of the compounds were characterized by 1H NMR and HRMS. In precise toxicity measurement, some of compounds exhibited more potent fungicidal activities than the most widely used commercial fungicide Chlorothalonil, promoting further research and development. Based on our experimental data, 3D-QSAR (three-dimensional quantitative structure-activity relationship) was established and investigated using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques, helping to better understand the structural requirements of lead compounds with high fungicidal activity and environmental compatibility.
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