| Literature DB >> 28938860 |
Swayansiddha Tripathy1, Mohammed Afzal Azam2, Srikanth Jupudi2, Susanta Kumar Sahu1.
Abstract
FtsZ is an appealing target for the design of antimicrobial agent that can be used to defeat the multidrug-resistant bacterial pathogens. Pharmacophore modelling, molecular docking and molecular dynamics (MD) simulation studies were performed on a series of three-substituted benzamide derivatives. In the present study a five-featured pharmacophore model with one hydrogen bond acceptors, one hydrogen bond donors, one hydrophobic and two aromatic rings was developed using 97 molecules having MIC values ranging from .07 to 957 μM. A statistically significant 3D-QSAR model was obtained using this pharmacophore hypothesis with a good correlation coefficient (R2 = .8319), cross validated coefficient (Q2 = .6213) and a high Fisher ratio (F = 103.9) with three component PLS factor. A good correlation between experimental and predicted activity of the training (R2 = .83) and test set (R2 = .67) molecules were displayed by ADHRR.1682 model. The generated model was further validated by enrichment studies using the decoy test and MAE-based criteria to measure the efficiency of the model. The docking studies of all selected inhibitors in the active site of FtsZ protein showed crucial hydrogen bond interactions with Val 207, Asn 263, Leu 209, Gly 205 and Asn-299 residues. The binding free energies of these inhibitors were calculated by the molecular mechanics/generalized born surface area VSGB 2.0 method. Finally, a 15 ns MD simulation was done to confirm the stability of the 4DXD-ligand complex. On a wider scope, the prospect of present work provides insight in designing molecules with better selective FtsZ inhibitory potential.Entities:
Keywords: 3D-QSAR; 3D-QSAR = three-dimensional quantitative structure–activity relationship; AAE = average absolute error; AE = average error; FtsZ = filamentous temperature sensitive protein Z; GTPase; Glide XP = glide extra precision; MAE = mean absolute error; MD = molecular dynamics; MIC = minimum inhibitory concentration; MM-GBSA = molecular mechanics-generalized born surface area; MPE = mean positive error; NNE = number of negative errors; NPE = number of positive errors; PLS = partial least square; Q2 = correlation coefficient for test set; R2 = correlation coefficient; RMSD = root-mean-square deviation; RMSE = root-mean-square error; SD = standard deviation; dynamics simulation; molecular docking; pharmacophore hypotheses; σAE = standard deviation of the absolute error
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Year: 2017 PMID: 28938860 DOI: 10.1080/07391102.2017.1384401
Source DB: PubMed Journal: J Biomol Struct Dyn ISSN: 0739-1102