Engineered odorant receptors illuminate the basis of odour discrimination – Nature
Buck, L. & Axel, R. A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell 65, 175–187 (1991).
Google Scholar
Glusman, G., Yanai, I., Rubin, I. & Lancet, D. The complete human olfactory subgenome. Genome Res. 11, 685–702 (2001).
Google Scholar
Ikegami, K. et al. Structural instability and divergence from conserved residues underlie intracellular retention of mammalian odorant receptors. Proc. Natl Acad. Sci. USA 117, 2957–2967 (2020).
Google Scholar
Malnic, B., Godfrey, P. A. & Buck, L. B. The human olfactory receptor gene family. Proc. Natl Acad Sci. USA 101, 2584–2589 (2004).
Google Scholar
Bjarnadóttir, T. K. et al. Comprehensive repertoire and phylogenetic analysis of the G protein-coupled receptors in human and mouse. Genomics 88, 263–273 (2006).
Google Scholar
Liberles, S. D. & Buck, L. B. A second class of chemosensory receptors in the olfactory epithelium. Nature 442, 645–650 (2006).
Google Scholar
Olender, T., Jones, T. E. M., Bruford, E. & Lancet, D. A unified nomenclature for vertebrate olfactory receptors. BMC Evol. Biol. 20, 42 (2020).
Google Scholar
Malnic, B., Hirono, J., Sato, T. & Buck, L. B. Combinatorial receptor codes for odors. Cell 96, 713–723 (1999).
Google Scholar
Saito, H., Chi, Q., Zhuang, H., Matsunami, H. & Mainland, J. D. Odor coding by a mammalian receptor repertoire. Sci. Signal. 2, ra9 (2009).
Google Scholar
Cichy, A., Shah, A., Dewan, A., Kaye, S. & Bozza, T. Genetic depletion of class I odorant receptors impacts perception of carboxylic acids. Curr. Biol. 29, 2687–2697.e4 (2019).
Google Scholar
Dewan, A., Pacifico, R., Zhan, R., Rinberg, D. & Bozza, T. Non-redundant coding of aversive odours in the main olfactory pathway. Nature 497, 486–489 (2013).
Google Scholar
Niimura, Y. On the origin and evolution of vertebrate olfactory receptor genes: comparative genome analysis among 23 chordate species. Genome Biol. Evol. 1, 34–44 (2009).
Google Scholar
Bear, D. M., Lassance, J.-M., Hoekstra, H. E. & Datta, S. R. The evolving neural and genetic architecture of vertebrate olfaction. Curr. Biol. 26, R1039–R1049 (2016).
Google Scholar
Freitag, J., Krieger, J., Strotmann, J. & Breer, H. Two classes of olfactory receptors in Xenopus laevis. Neuron 15, 1383–1392 (1995).
Google Scholar
Billesbølle, C. B. et al. Structural basis of odorant recognition by a human odorant receptor. Nature 615, 742–749 (2023).
Google Scholar
Guo, L. et al. Structural basis of amine odorant perception by a mammal olfactory receptor. Nature 618, 193–200 (2023).
Google Scholar
Shang, P. et al. Structural and signaling mechanisms of TAAR1 enabled preferential agonist design. Cell 186, 5347–5362.e24 (2023).
Google Scholar
Xu, Z. et al. Ligand recognition and G-protein coupling of trace amine receptor TAAR1. Nature 624, 672–681 (2023).
Google Scholar
Liu, H. et al. Recognition of methamphetamine and other amines by trace amine receptor TAAR1. Nature 624, 663–671 (2023).
Google Scholar
Gusach, A. et al. Molecular recognition of an odorant by the murine trace amine-associated receptor TAAR7f. Nat. Commun. 15, 7555 (2024).
Google Scholar
Lu, M., Echeverri, F. & Moyer, B. D. Endoplasmic reticulum retention, degradation, and aggregation of olfactory G-protein coupled receptors. Traffic 4, 416–433 (2003).
Google Scholar
Saito, H., Kubota, M., Roberts, R. W., Chi, Q. & Matsunami, H. RTP family members induce functional expression of mammalian odorant receptors. Cell 119, 679–691 (2004).
Google Scholar
Zhuang, H. & Matsunami, H. Evaluating cell-surface expression and measuring activation of mammalian odorant receptors in heterologous cells. Nat. Protoc. 3, 1402–1413 (2008).
Google Scholar
Noe, F. et al. IL-6-HaloTag® enables live-cell plasma membrane staining, flow cytometry, functional expression, and de-orphaning of recombinant odorant receptors. J. Biol. Methods 4, e81 (2017).
Google Scholar
Sternke, M., Tripp, K. W. & Barrick, D. Consensus sequence design as a general strategy to create hyperstable, biologically active proteins. Proc. Natl Acad. Sci. USA 116, 11275–11284 (2019).
Google Scholar
Desjarlais, J. R. & Berg, J. M. Use of a zinc-finger consensus sequence framework and specificity rules to design specific DNA binding proteins. Proc. Natl Acad. Sci. USA 90, 2256–2260 (1993).
Google Scholar
Porebski, B. T. & Buckle, A. M. Consensus protein design. Protein Eng. Des. Sel. 29, 245–251 (2016).
Google Scholar
Steipe, B., Schiller, B., Plückthun, A. & Steinbacher, S. Sequence statistics reliably predict stabilizing mutations in a protein domain. J. Mol. Biol. 240, 188–192 (1994).
Google Scholar
Lehmann, M. et al. From DNA sequence to improved functionality: using protein sequence comparisons to rapidly design a thermostable consensus phytase. Protein Eng. 13, 49–57 (2000).
Google Scholar
Choi, C. et al. Understanding the molecular mechanisms of odorant binding and activation of the human OR52 family. Nat. Commun. 14, 8105 (2023).
Google Scholar
Nehmé, R. et al. Mini-G proteins: novel tools for studying GPCRs in their active conformation. PLoS ONE 12, e0175642 (2017).
Google Scholar
Ballesteros, J. A. & Weinstein, H. in Methods in Neurosciences Vol. 25 (ed. Sealfon, S. C.) 366–428 (Academic Press, 1995).
de March, C. A., Kim, S.-K., Antonczak, S., Goddard, W. A. 3rd & Golebiowski, J. G protein-coupled odorant receptors: from sequence to structure. Protein Sci. 24, 1543–1548 (2015).
Google Scholar
Isberg, V. et al. Generic GPCR residue numbers—aligning topology maps while minding the gaps. Trends Pharmacol. Sci. 36, 22–31 (2015).
Google Scholar
de March, C. A. et al. Conserved residues control activation of mammalian G protein-coupled odorant receptors. J. Am. Chem. Soc. 137, 8611–8616 (2015).
Google Scholar
Pluznick, J. L. et al. Olfactory receptor responding to gut microbiota-derived signals plays a role in renin secretion and blood pressure regulation. Proc. Natl Acad. Sci. USA 110, 4410–4415 (2013).
Google Scholar
Shayya, H. J. et al. ER stress transforms random olfactory receptor choice into axon targeting precision. Cell 185, 3896–3912.e22 (2022).
Google Scholar
Mainland, J. D., Li, Y. R., Zhou, T., Liu, W. L. L. & Matsunami, H. Human olfactory receptor responses to odorants. Sci. Data 2, 150002 (2015).
Google Scholar
Kajiya, K. et al. Molecular bases of odor discrimination: reconstitution of olfactory receptors that recognize overlapping sets of odorants. J. Neurosci. 21, 6018–6025 (2001).
Google Scholar
Grosmaitre, X. et al. SR1, a mouse odorant receptor with an unusually broad response profile. J. Neurosci. 29, 14545–14552 (2009).
Google Scholar
Schmiedeberg, K. et al. Structural determinants of odorant recognition by the human olfactory receptors OR1A1 and OR1A2. J. Struct. Biol. 159, 400–412 (2007).
Google Scholar
Geithe, C., Noe, F., Kreissl, J. & Krautwurst, D. The broadly tuned odorant receptor OR1A1 is highly selective for 3-methyl-2,4-nonanedione, a key food odorant in aged wines, tea, and other foods. Chem. Senses 42, 181–193 (2017).
Google Scholar
Ma, N., Lee, S. & Vaidehi, N. Activation microswitches in adenosine receptor A2A function as rheostats in the cell membrane. Biochemistry 59, 4059–4071 (2020).
Google Scholar
Dror, R. O. et al. Activation mechanism of the β2-adrenergic receptor. Proc. Natl Acad. Sci. USA 108, 18684–18689 (2011).
Google Scholar
Lee, S., Nivedha, A. K., Tate, C. G. & Vaidehi, N. Dynamic role of the G protein in stabilizing the active state of the adenosine A2A receptor. Structure 27, 703–712.e3 (2019).
Google Scholar
Li, Q. et al. Non-classical amine recognition evolved in a large clade of olfactory receptors. eLife 4, e10441 (2015).
Google Scholar
Del Mármol, J., Yedlin, M. A. & Ruta, V. The structural basis of odorant recognition in insect olfactory receptors. Nature 597, 126–131 (2021).
Google Scholar
Butterwick, J. A. et al. Cryo-EM structure of the insect olfactory receptor Orco. Nature 560, 447–452 (2018).
Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Bender, B. J., Marlow, B. & Meiler, J. Improving homology modeling from low-sequence identity templates in Rosetta: a case study in GPCRs. PLoS Comput. Biol. 16, e1007597 (2020).
Google Scholar
Rutherford, S. L. & Lindquist, S. Hsp90 as a capacitor for morphological evolution. Nature 396, 336–342 (1998).
Google Scholar
Wyganowski, K. T., Kaltenbach, M. & Tokuriki, N. GroEL/ES buffering and compensatory mutations promote protein evolution by stabilizing folding intermediates. J. Mol. Biol. 425, 3403–3414 (2013).
Google Scholar
Agozzino, L. & Dill, K. A. Protein evolution speed depends on its stability and abundance and on chaperone concentrations. Proc. Natl Acad. Sci. USA 115, 9092–9097 (2018).
Google Scholar
Faust, B. et al. Autoantibody mimicry of hormone action at the thyrotropin receptor. Nature 609, 846–853 (2022).
Mastronarde, D. N. SerialEM: a program for automated tilt series acquisition on Tecnai microscopes using prediction of specimen position. Microsc. Microanal. 9, 1182–1183 (2003).
Google Scholar
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
Google Scholar
Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).
Google Scholar
Asarnow, D., Palovcak, E. & Cheng, Y. asarnow/pyem: UCSF Pyem v0.5. Zenodo https://doi.org/10.5281/zenodo.3576630 (2019).
Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).
Google Scholar
Scheres, S. H. W. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).
Google Scholar
Bushdid, C., de March, C. A., Matsunami, H. & Golebiowski, J. Numerical models and in vitro assays to study odorant receptors. Methods Mol. Biol. 1820, 77–93 (2018).
Google Scholar
Zhang, Y., Pan, Y., Matsunami, H. & Zhuang, H. Live-cell measurement of odorant receptor activation using a real-time cAMP assay. J. Vis. Exp. 128, 55831 (2017).
Berendsen, H. J. C., van der Spoel, D. & van Drunen, R. GROMACS: a message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91, 43–56 (1995).
Google Scholar
Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2017).
Google Scholar
Vanommeslaeghe, K. et al. CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31, 671–690 (2010).
Google Scholar
Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).
Google Scholar
Jo, S., Lim, J. B., Klauda, J. B. & Im, W. CHARMM-GUI Membrane Builder for mixed bilayers and its application to yeast membranes. Biophys. J. 97, 50–58 (2009).
Google Scholar
Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I. & Lomize, A. L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 40, D370–D376 (2012).
Google Scholar
Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: a new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981).
Google Scholar
Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: an N⋅log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993).
Google Scholar
Pagès, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: Efficient manipulation of biological strings. R package version 2.72.1 https://bioconductor.org/packages/Biostrings (2022).
Charif, D. & Lobry, J. R. in Structural Approaches to Sequence Evolution: Molecules, Networks, Populations (eds Bastolla, U. et al.) 207–232 (Springer Berlin Heidelberg, 2007).
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).
Google Scholar
Xu, S. et al. Ggtree: a serialized data object for visualization of a phylogenetic tree and annotation data. iMeta 1, e56 (2022).
Google Scholar
Crooks, G. E., Hon, G., Chandonia, J.-M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).
Google Scholar
Dang, S. et al. Cryo-EM structures of the TMEM16A calcium-activated chloride channel. Nature 552, 426–429 (2017).
Google Scholar
