Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1
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Abstract
With approximately 400 encoding genes in humans, odorant receptors (ORs) are the largest subfamily of class A G protein-coupled receptors (GPCRs). Despite its high relevance and representation, the odorant-GPCRome is structurally poorly characterized: no experimental structures are available, and the low sequence identity of ORs to experimentally solved GPCRs is a significant challenge for their modeling. Moreover, the receptive range of most ORs is unknown. The odorant receptor OR5K1 was recently and comprehensively characterized in terms of cognate agonists. Here, we report two additional agonists and functional data of the most potent compound on two mutants, L1043.32 and L2556.51. Experimental data was used to guide the investigation of the binding modes of OR5K1 ligands into the orthosteric binding site using structural information from AI-driven modeling, as recently released in the AlphaFold Protein Structure Database, and from homology modeling. Induced-fit docking simulations were used to sample the binding site conformational space for ensemble docking. Mutagenesis data guided side chain residue sampling and model selection. We obtained models that could better rationalize the different activity of active (agonist) versus inactive molecules with respect to starting models and also capture differences in activity related to minor structural differences. Therefore, we provide a model refinement protocol that can be applied to model the orthosteric binding site of ORs as well as that of GPCRs with low sequence identity to available templates.
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Self-citationUniversal Activation Index for Class A GPCRs
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Yu, Y., Ma, Z., Pacalon, J., Xu, L., Li, W., Belloir, C., Topin, J., Briand, L., Golebiowski, J., & Cong, X. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102331 · Cited by 28 · see citing works
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- Authors
- Nicoli, A., Haag, F., Marcinek, P., He, R., Kreißl, J., Stein, J., Marchetto, A., Dunkel, A., Hofmann, T., Krautwurst, D., & Di Pizio, A.
- Affiliations
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich (DE)
- Leibniz Institute for Food Systems Biology at the Technical University of Munich , ,
- Forschungszentrum Jülich (DE)
- Technical University of Munich (DE)
- Corresponding author
- Krautwurst, Dietmar
- ORCID
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-01-25
- Volume
- 63
- Issue
- 7
- Pages
- 2014-2029
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC10091413
- OpenAlex
- W4318023216
- Cited by
- 28
- References
- 135
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 7 · 2023: 5
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- RWTH Publications (RWTH Aachen)
- Funders
- Deutsche Forschungsgemeinschaft (PI 1672/3-1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- olfactory receptors, G protein-coupled receptors, orthosteric binding site, receptor modeling, induced fit docking, ensemble docking, AlphaFold structures, homology modeling, receptor mutants, model refinement
- MeSH terms
- Binding Sites, Humans, Ligands, Odorants, Receptors, Odorant, GTP-Binding Proteins, Receptors, G-Protein-Coupled
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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