Predicted 3D structure for the human β2 adrenergic receptor and its binding site for agonists and antagonists
Abstract
We report the 3D structure of human β2 adrenergic receptor (AR) predicted by using the MembStruk first principles method. To validate this structure, we use the HierDock first principles method to predict the ligand-binding sites for epinephrine and norepinephrine and for eight other ligands, including agonists and antagonists to β2 AR and ligands not observed to bind to β2 AR. The binding sites agree well with available mutagenesis data, and the calculated relative binding energies correlate reasonably with measured binding affinities. In addition, we find characteristic differences in the predicted binding sites of known agonists and antagonists that allow us to infer the likely activity of other ligands. The predicted ligand-binding properties validate the methods used to predict the 3D structure and function. This validation is a successful step toward applying these procedures to predict the 3D structures and function of the other eight subtypes of ARs, which should enable the development of subtype-specific antagonists and agonists with reduced side effects.
Additional Information
© 2004 The National Academy of Sciences. Contributed by William A. Goddard III, January 5, 2004 This research was supported partially by National Institutes of Health Grants BRGRO1-GM625523, R29AI40567, and HD36385. The computational facilities were provided by a Shared UniversityResearch grant from IBM and Defense University Research Instrumentation Program grants from the Army Research Office (ARO) and the Office of Naval Research (ONR). The facilities of the Materials and Process Simulation Center are also supported by the Department of Energy, the National Science Foundation, the Multidisciplinary University Research Initiative (MURI)-ARO, MURI-ONR, General Motors, ChevronTexaco, Seiko-Epson, the Beckman Institute, and Asahi Kasei.Attached Files
Published - PNAS-2004-Freddolino-2736-41.pdf
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Additional details
- PMCID
- PMC365690
- Eprint ID
- 52018
- Resolver ID
- CaltechAUTHORS:20141120-150243775
- NIH
- BRGRO1-GM625523
- NIH
- R29AI40567
- NIH
- HD36385
- IBM
- Army Research Office (ARO)
- Office of Naval Research (ONR)
- Department of Energy (DOE)
- NSF
- General Motors
- ChevronTexaco
- Seiko-Epson
- Caltech Beckman Institute
- Asahi Kasei
- Created
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2014-11-24Created from EPrint's datestamp field
- Updated
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2021-11-10Created from EPrint's last_modified field