2012
DOI: 10.1021/ci300121p
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Development of a Comprehensive, Validated Pharmacophore Hypothesis for Anthrax Toxin Lethal Factor (LF) Inhibitors Using Genetic Algorithms, Pareto Scoring, and Structural Biology

Abstract: Anthrax is an acute infectious disease caused by the spore-forming bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), an 89-kDa zinc hydrolase secreted by the bacilli, is the toxin component chiefly responsible for pathogenesis and has been a popular target for rational and structure-based drug design. Although hundreds of small-molecule compounds have been designed to target the LF active site, relatively few reported inhibitors have exhibited activity in cell-based assays, and no LF inhibito… Show more

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Cited by 11 publications
(12 citation statements)
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“…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. As reported by Chiu and Amin (2), for accurate and useful pharmacophore mapping based on active LF inhibitors, we recommend a genetic algorithm approach incorporating Pareto scoring, as implemented in the GALAHAD pharmacophore perception module (23) (see Note 1), together with ligand–receptor interaction analysis based on experimental structural biology (see Note 2).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…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. As reported by Chiu and Amin (2), for accurate and useful pharmacophore mapping based on active LF inhibitors, we recommend a genetic algorithm approach incorporating Pareto scoring, as implemented in the GALAHAD pharmacophore perception module (23) (see Note 1), together with ligand–receptor interaction analysis based on experimental structural biology (see Note 2).…”
Section: Methodsmentioning
confidence: 99%
“…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. As reported by Chiu and Amin (2), for accurate and useful pharmacophore mapping based on active LF inhibitors, we recommend a genetic algorithm approach incorporating Pareto scoring, as implemented in the GALAHAD pharmacophore perception module (23) (see Note 1), together with ligand–receptor interaction analysis based on experimental structural biology (see Note 2). …”
Section: Methodsmentioning
confidence: 99%
“…The Amin group also attempted to computationally define a universal LF active site inhibitor pharmacophore based on five reported inhibitors falling into three structural classes [54]. The resulting in silico screening model correctly identified 72% of the most potent experimentally identified LF inhibitors (IC 50 values in the nM range) and rejected all reported weakly active compounds (IC 50 values >100 µM).…”
Section: Identification Of Lf Inhibitors By Virtual Screeningmentioning
confidence: 99%
“…A new pharmacophore map assembly designed to rapidly identify and prioritize promising LF inhibitor scaffolds from virtual compound libraries was reported (138). The authors utilized structural information from five available LF enzyme-inhibitor complexes deposited into the Protein Data Bank and considered all three key subsites of the LF catalytic binding region.…”
Section: Pa Lf and Ef As Antitoxin Design Targetsmentioning
confidence: 99%