2010
DOI: 10.1016/j.jmgm.2010.07.002
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Insilico studies on anthrax lethal factor inhibitors: Pharmacophore modeling and virtual screening approaches towards designing of novel inhibitors for a killer

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Cited by 10 publications
(10 citation statements)
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“…Accurate and validated pharmacophore hypotheses have proven useful for identifying new LF inhibitor scaffolds via database searching on the basis of ligand–receptor interactions observed for one or more series of active compounds (33, 3638). Several LF inhibitor pharmacophore hypotheses have been outlined in the literature (33, 3638) ; however, these models were developed from relatively small training sets that occupy only one or two subsites of the LF active site and therefore do not necessarily represent the majority of key interactions that are essential for ligand binding.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Accurate and validated pharmacophore hypotheses have proven useful for identifying new LF inhibitor scaffolds via database searching on the basis of ligand–receptor interactions observed for one or more series of active compounds (33, 3638). Several LF inhibitor pharmacophore hypotheses have been outlined in the literature (33, 3638) ; however, these models were developed from relatively small training sets that occupy only one or two subsites of the LF active site and therefore do not necessarily represent the majority of key interactions that are essential for ligand binding.…”
Section: Methodsmentioning
confidence: 99%
“…Several LF inhibitor pharmacophore hypotheses have been outlined in the literature (33, 3638) ; however, these models were developed from relatively small training sets that occupy only one or two subsites of the LF active site and therefore do not necessarily represent the majority of key interactions that are essential for ligand binding. We recently reported (2) a new comprehensive pharmacophore map based on experimentally determined bound configurations for active compounds; this new hypothesis covers all three subsites (S1′, S1–S2, and S2′) of the LF active site and selectively identifies inhibitors with biological activity against LF in the nanomolar range.…”
Section: Methodsmentioning
confidence: 99%
“…A model developed computationally by Roy et al used as a training set both reported hydroxamate analogs of L915 and a series of structurally unrelated furan derivatives reported by the Pellecchia group [53]. Their best model, which involved two hydrogen bond acceptors and two hydrophobic features, performed well in predicting the activity of a set of 98 reported inhibitors that were excluded from the training set ( R 2 = 0.77 for predicted activity vs. reported activity).…”
Section: Identification Of Lf Inhibitors By Virtual Screeningmentioning
confidence: 99%
“…The resulting LF pharmacophore was used in a computational screen of a broad chemical library to identify chemically diverse leads that will be synthesized and evaluated [123]. Another study has analyzed pre-steady-state kinetics to investigate the binding of LF to substrates and hydroxymates [124].…”
Section: Current Research Directionsmentioning
confidence: 99%