Ligand-supported Homology Modelling of Protein Binding-sites using Knowledge-based Potentials

Evers, Andreas; Gohlke, Holger; Klebe, G.

doi:10.1016/j.jmb.2003.09.032

Cited by 120 publications

(110 citation statements)

References 108 publications

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“…The accuracy of virtual screening was estimated by the enrichment for the known ligands among the top-scoring decoy compounds (61,62), generated by the Directory of Useful Decoys protocol (16,18) …”

Section: Methodsmentioning

confidence: 99%

Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET

Schlessinger

Geier

Fan

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

118

136

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The norepinephrine transporter (NET) transports norepinephrine from the synapse into presynaptic neurons, where norepinephrine regulates signaling pathways associated with cardiovascular effects and behavioral traits via binding to various receptors (e.g., β2-adrenergic receptor). NET is a known target for a variety of prescription drugs, including antidepressants and psychostimulants, and may mediate off-target effects of other prescription drugs. Here, we identify prescription drugs that bind NET, using virtual ligand screening followed by experimental validation of predicted ligands. We began by constructing a comparative structural model of NET based on its alignment to the atomic structure of a prokaryotic NET homolog, the leucine transporter LeuT. The modeled binding site was validated by confirming that known NET ligands can be docked favorably compared to nonbinding molecules. We then computationally screened 6,436 drugs from the Kyoto Encyclopedia of Genes and Genomes (KEGG DRUG) against the NET model. Ten of the 18 high-scoring drugs tested experimentally were found to be NET inhibitors; five of these were chemically novel ligands of NET. These results may rationalize the efficacy of several sympathetic (tuaminoheptane) and antidepressant (tranylcypromine) drugs, as well as side effects of diabetes (phenformin) and Alzheimer's (talsaclidine) drugs. The observations highlight the utility of virtual screening against a comparative model, even when the target shares less than 30% sequence identity with its template structure and no known ligands in the primary binding site.

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Section: Methodsmentioning

confidence: 99%

Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET

Schlessinger

Geier

Fan

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

118

136

View full text Add to dashboard Cite

show abstract

“…In the absence of experimentally determined structures, several successful virtual screening campaigns have been reported 342 based on comparative models of target proteins Warner et al, 2006;Budzik et al, 2010). Efforts have also been made to incorporate information about binding properties of known ligands back into comparative modeling process (Evers et al, 2003;Evers and Klebe, 2004).…”

Section: A Preparation Of a Target Structurementioning

confidence: 99%

Computational Methods in Drug Discovery

et al. 2013

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“…Mixed approach It is also possible to incorporate knowledge coming from the ligand-based approach or other experimental information, such as mutational data, to refine the modeling procedure and help in selecting the right ligand pose (Yarnitzky et al 2010). For example, Evers et al (2003) proposed a ligand-supported homology modeling procedure in which the models are refined iteratively by including information on active ligands as spatial restraints and optimizing the mutual interactions between the ligands and the binding sites. In the GPCRdock contest, in which a number of groups were asked to predict the 3D structure of the complex of the A2A adenosine receptor with an antagonist (ZM241385) before its publication, the most successful predictions were those using this mixed approach Michino and Abola 2009).…”

Section: Virtual Screeningmentioning

confidence: 99%

Modeling of mammalian olfactory receptors and docking of odorants

et al. 2012

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Olfactory receptors (ORs) belong to the superfamily of G protein-coupled receptors (GPCRs), the second largest class of genes after those related to immunity, and account for about 3 % of mammalian genomes. ORs are present in all multicellular organisms and represent more than half the GPCRs in mammalian species (e.g., the mouse OR repertoire contains >1,000 functional genes). ORs are mainly expressed in the olfactory epithelium where they detect odorant molecules, but they are also expressed in a number of other cells, such as sperm cells, although their functions in these cells remain mostly unknown. It has recently been reported that ORs are present in tumoral tissues where they are expressed at different levels than in healthy tissues. A specific OR is overexpressed in prostate cancer cells, and activation of this OR has been shown to inhibit the proliferation of these cells. Odorant stimulation of some of these receptors results in inhibition of cell proliferation. Even though their biological role has not yet been elucidated, these receptors might constitute new targets for diagnosis and therapeutics. It is important to understand the activation mechanism of these receptors at the molecular level, in particular to be able to predict which ligands are likely to activate a particular receptor ('deorphanization') or to design antagonists for a given receptor. In this review, we describe the in silico methodologies used to model the three-dimensional (3D) structure of ORs (in the more general framework of GPCR modeling) and to dock ligands into these 3D structures.

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Ligand-supported Homology Modelling of Protein Binding-sites using Knowledge-based Potentials

Cited by 120 publications

References 108 publications

Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET

Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET

Computational Methods in Drug Discovery

Modeling of mammalian olfactory receptors and docking of odorants

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