Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening.

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Standard

Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening. / Norn, Christoffer; Pedersen, Maria Hauge; Engelstoft, Maja S.; Kim, Sun Hee; Lehmann, Juerg; Jones, Robert M.; Schwartz, Thue W.; Frimurer, Thomas M.

I: Structure, Bind 23, Nr. 12, 2015, s. 2377-2386.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Norn, C, Pedersen, MH, Engelstoft, MS, Kim, SH, Lehmann, J, Jones, RM, Schwartz, TW & Frimurer, TM 2015, 'Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening.', Structure, bind 23, nr. 12, s. 2377-2386. https://doi.org/10.1016/j.str.2015.09.014

APA

Norn, C., Pedersen, M. H., Engelstoft, M. S., Kim, S. H., Lehmann, J., Jones, R. M., Schwartz, T. W., & Frimurer, T. M. (2015). Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening. Structure, 23(12), 2377-2386. https://doi.org/10.1016/j.str.2015.09.014

Vancouver

Norn C, Pedersen MH, Engelstoft MS, Kim SH, Lehmann J, Jones RM o.a. Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening. Structure. 2015;23(12):2377-2386. https://doi.org/10.1016/j.str.2015.09.014

Author

Norn, Christoffer ; Pedersen, Maria Hauge ; Engelstoft, Maja S. ; Kim, Sun Hee ; Lehmann, Juerg ; Jones, Robert M. ; Schwartz, Thue W. ; Frimurer, Thomas M. / Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening. I: Structure. 2015 ; Bind 23, Nr. 12. s. 2377-2386.

Bibtex

@article{964458fe4e6640d88f7be6ef5c89e5c6,

title = "Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening.",

abstract = "Recent benchmark studies have demonstrated the difficulties in obtaining accurate predictions of ligand binding conformations to comparative models of G-protein-coupled receptors. We have developed a data-driven optimization protocol, which integrates mutational data and structural information from multiple X-ray receptor structures in combination with a fully flexible ligand docking protocol to det. the binding conformation of AR231453, a small-mol. agonist, in the GPR119 receptor. Resulting models converge to one conformation that explains the majority of data from mutation studies and is consistent with the structure-activity relationship for a large no. of AR231453 analogs. Another key property of the refined models is their success in sepg. active ligands from decoys in a large-scale virtual screening. These results demonstrate that mutation-guided receptor modeling can provide predictions of practical value for describing receptor-ligand interactions and drug discovery. ",

author = "Christoffer Norn and Pedersen, {Maria Hauge} and Engelstoft, {Maja S.} and Kim, {Sun Hee} and Juerg Lehmann and Jones, {Robert M.} and Schwartz, {Thue W.} and Frimurer, {Thomas M.}",

year = "2015",

doi = "10.1016/j.str.2015.09.014",

language = "English",

volume = "23",

pages = "2377--2386",

journal = "Structure",

issn = "0969-2126",

publisher = "Cell Press",

number = "12",

}

RIS

TY - JOUR

T1 - Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening.

AU - Norn, Christoffer

AU - Pedersen, Maria Hauge

AU - Engelstoft, Maja S.

AU - Kim, Sun Hee

AU - Lehmann, Juerg

AU - Jones, Robert M.

AU - Schwartz, Thue W.

AU - Frimurer, Thomas M.

PY - 2015

Y1 - 2015

N2 - Recent benchmark studies have demonstrated the difficulties in obtaining accurate predictions of ligand binding conformations to comparative models of G-protein-coupled receptors. We have developed a data-driven optimization protocol, which integrates mutational data and structural information from multiple X-ray receptor structures in combination with a fully flexible ligand docking protocol to det. the binding conformation of AR231453, a small-mol. agonist, in the GPR119 receptor. Resulting models converge to one conformation that explains the majority of data from mutation studies and is consistent with the structure-activity relationship for a large no. of AR231453 analogs. Another key property of the refined models is their success in sepg. active ligands from decoys in a large-scale virtual screening. These results demonstrate that mutation-guided receptor modeling can provide predictions of practical value for describing receptor-ligand interactions and drug discovery.

AB - Recent benchmark studies have demonstrated the difficulties in obtaining accurate predictions of ligand binding conformations to comparative models of G-protein-coupled receptors. We have developed a data-driven optimization protocol, which integrates mutational data and structural information from multiple X-ray receptor structures in combination with a fully flexible ligand docking protocol to det. the binding conformation of AR231453, a small-mol. agonist, in the GPR119 receptor. Resulting models converge to one conformation that explains the majority of data from mutation studies and is consistent with the structure-activity relationship for a large no. of AR231453 analogs. Another key property of the refined models is their success in sepg. active ligands from decoys in a large-scale virtual screening. These results demonstrate that mutation-guided receptor modeling can provide predictions of practical value for describing receptor-ligand interactions and drug discovery.

U2 - 10.1016/j.str.2015.09.014

DO - 10.1016/j.str.2015.09.014

M3 - Journal article

VL - 23

SP - 2377

EP - 2386

JO - Structure

JF - Structure

SN - 0969-2126

IS - 12

ER -

ID: 150703222