Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Rhodopsin-bestrophin fusion proteins from unicellular algae form gigantic pentameric ion channels

Nature Structural & Molecular Biology volume 29, pages 592–603 (2022)Cite this article

Subjects

Abstract

Many organisms sense light using rhodopsins, photoreceptive proteins containing a retinal chromophore. Here we report the discovery, structure and biophysical characterization of bestrhodopsins, a microbial rhodopsin subfamily from marine unicellular algae, in which one rhodopsin domain of eight transmembrane helices or, more often, two such domains in tandem, are C-terminally fused to a bestrophin channel. Cryo-EM analysis of a rhodopsin-rhodopsin-bestrophin fusion revealed that it forms a pentameric megacomplex (~700 kDa) with five rhodopsin pseudodimers surrounding the channel in the center. Bestrhodopsins are metastable and undergo photoconversion between red- and green-absorbing or green- and UVA-absorbing forms in the different variants. The retinal chromophore, in a unique binding pocket, photoisomerizes from all-trans to 11-cis form. Heterologously expressed bestrhodopsin behaves as a light-modulated anion channel.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Bestrhodopsins: domain organization and evolution.
Fig. 2: Cryo-EM structure of the RRB bestrhodopsin.
Fig. 3: The architecture of Tara-RRB by domains.
Fig. 4: Tara-RRB retinal binding pocket.
Fig. 5: Photochemical characterization of Tara-RRB.
Fig. 6: Biophysical characterization of Kv-RRB.

Data availability

The collected bestrhodopsin and bestrhodopsin-related sequences as annotated genbank files, alignments in fasta format, metadata for searched data sources and species phylogeny data are available at https://doi.org/10.5281/zenodo.5119843. Metadata for the detected bestrhodopsin genes and details about the assemblies used in the search and for species phylogeny, as well annotated nucleotide sequences of the bestrhodopsin and bestrhodopsin-related genes, are provided in Supplementary Data Files 1–3. Optimized nucleotide sequences of bestrhodopsin genes used for expression are available from GenBank under accession numbers MZ740266–MZ740270. The cryo-EM density map has been deposited in the Electron Microscopy Data Bank (EMDB) under accession code EMD-13485, and model coordinates have been deposited in the Protein Data Bank (PDB) under accession no. 7PL9. All other data are available in the manuscript or in the Supplementary Information. Source data are provided with this paper.

Code availability

Code used for bioinformatic analysis is deposited in Github (https://github.com/BejaLab/RRB) and is available at https://doi.org/10.5281/zenodo.6409771.

References

  1. Spudich, J. L., Yang, C.-S., Jung, K.-H. & Spudich, E. N. Retinylidene proteins: structures and functions from archaea to humans. Annu. Rev. Cell Dev. Biol. 16, 365–392 (2000).

    Article  CAS  PubMed  Google Scholar 

  2. Ernst, O. P. et al. Microbial and animal rhodopsins: structures, functions, and molecular mechanisms. Chem. Rev. 114, 126–163 (2014).

    Article  CAS  PubMed  Google Scholar 

  3. Kandori, H. Retinal proteins: photochemistry and optogenetics. Bull. Chem. Soc. Jpn. 93, 76–85 (2020).

    Article  CAS  Google Scholar 

  4. Rozenberg, A., Inoue, K., Kandori, H. & Béjà, O. Microbial rhodopsins: the last two decades. Annu. Rev. Microbiol. 75, 427–447 (2021).

    Article  PubMed  CAS  Google Scholar 

  5. Wolf, S. & Grünewald, S. Sequence, structure and ligand binding evolution of rhodopsin-Like G protein-coupled receptors: a crystal structure-based phylogenetic analysis. PLoS ONE 10, e0123533 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Govorunova, E. G., Sineshchekov, O. A., Li, H. & Spudich, J. L. Microbial rhodopsins: diversity, mechanisms, and optogenetic applications. Annu. Rev. Biochem. 86, 845–872 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Deisseroth, K. & Hegemann, P. The form and function of channelrhodopsin. Science 357, eaan5544 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Kateriya, S., Nagel, G., Bamberg, E. & Hegemann, P. ‘Vision’ in single-celled algae. Physiology 19, 133–137 (2004).

    Article  CAS  Google Scholar 

  9. Mukherjee, S., Hegemann, P. & Broser, M. Enzymerhodopsins: novel photoregulated catalysts for optogenetics. Curr. Opin. Struct. Biol. 57, 118–126 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Tsunoda, S. P., Sugiura, M. & Kandori, H. in Optogenetics: Light-Sensing Proteins and Their Applications in Neuroscience and Beyond vol. 1293 (eds. Yawo, H., Kandori, H., Koizumi, A. & Kageyama, R.) 153–165 (Springer, 2021).

  11. Yizhar, O., Fenno, L., Zhang, F., Hegemann, P. & Deisseroth, K. Microbial opsins: a family of single-component tools for optical control of neural activity. Cold Spring Harb. Protoc. 2011, top102 (2011).

    Article  PubMed  Google Scholar 

  12. Xiao, Q., Hartzell, H. C. & Yu, K. Bestrophins and retinopathies. Pflüg. Arch. Eur. J. Physiol. 460, 559–569 (2010).

    Article  CAS  Google Scholar 

  13. Hartzell, H. C., Qu, Z., Yu, K., Xiao, Q. & Chien, L.-T. Molecular physiology of bestrophins: multifunctional membrane proteins linked to best disease and other retinopathies. Physiol. Rev. 88, 639–672 (2008).

    Article  CAS  PubMed  Google Scholar 

  14. Yang, T. et al. Structure and selectivity in bestrophin ion channels. Science 346, 355–359 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Roberts, S. K., Milnes, J. & Caddick, M. Characterisation of AnBEST1, a functional anion channel in the plasma membrane of the filamentous fungus Aspergillus nidulans. Fungal Genet. Biol. 48, 928–938 (2011).

    Article  CAS  PubMed  Google Scholar 

  16. Mukherjee, A. et al. Thylakoid localized bestrophin-like proteins are essential for the CO2 concentrating mechanism of Chlamydomonas reinhardtii. Proc. Natl Acad. Sci. USA 116, 16915–16920 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Herdean, A. et al. A voltage-dependent chloride channel fine-tunes photosynthesis in plants. Nat. Commun. 7, 11654 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Taylor, W. R. & Sadowski, M. I. n Evolution after Gene Duplication 133–162 (John Wiley & Sons, 2010).

  19. Gao, S. et al. Optogenetic manipulation of cGMP in cells and animals by the tightly light-regulated guanylyl-cyclase opsin CyclOp. Nat. Commun. 6, 8046 (2015).

    Article  PubMed  Google Scholar 

  20. Tian, Y., Gao, S., von der Heyde, E. L., Hallmann, A. & Nagel, G. Two-component cyclase opsins of green algae are ATP-dependent and light-inhibited guanylyl cyclases. BMC Biol. 16, 144 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ikuta, T. et al. Structural insights into the mechanism of rhodopsin phosphodiesterase. Nat. Commun. 11, 5605 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kane Dickson, V., Pedi, L. & Long, S. B. Structure and insights into the function of a Ca2+-activated Cl− channel. Nature 516, 213–218 (2014).

    Article  PubMed  CAS  Google Scholar 

  23. Chien, L.-T. & Hartzell, H. C. Drosophila bestrophin-1 chloride current is dually regulated by calcium and cell volume. J. Gen. Physiol. 130, 513–524 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hallegraeff, G., Enevoldsen, H. & Zingone, A. Global harmful algal bloom status reporting. Harmful Algae 102, 101992 (2021).

    Article  PubMed  Google Scholar 

  25. Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).

    Article  PubMed  CAS  Google Scholar 

  26. Penzkofer, A., Scheib, U., Stehfest, K. & Hegemann, P. Absorption and emission spectroscopic investigation of thermal dynamics and photo-dynamics of the rhodopsin domain of the rhodopsin-guanylyl cyclase from the nematophagous fungus Catenaria anguillulae. Int. J. Mol. Sci. 18, 2099 (2017).

    Article  PubMed Central  CAS  Google Scholar 

  27. Owji, A. P. et al. Structural and functional characterization of the bestrophin-2 anion channel. Nat. Struct. Mol. Biol. 27, 382–391 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Miller, A. N., Vaisey, G. & Long, S. B. Molecular mechanisms of gating in the calcium-activated chloride channel bestrophin. eLife 8, e43231 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bratanov, D. et al. Unique structure and function of viral rhodopsins. Nat. Commun. 10, 4939 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Hirschi, S., Kalbermatter, D., Ucurum, Z., Lemmin, T. & Fotiadis, D. Cryo-EM structure and dynamics of the green-light absorbing proteorhodopsin. Nat. Commun. 12, 4107 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Morizumi, T. et al. X-ray crystallographic structure and oligomerization of Gloeobacter rhodopsin. Sci. Rep. 9, 11283 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Kovalev, K. et al. Structure and mechanisms of sodium-pumping KR2 rhodopsin. Sci. Adv. 5, eaav2671 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Vaisey, G., Miller, A. N. & Long, S. B. Distinct regions that control ion selectivity and calcium-dependent activation in the bestrophin ion channel. Proc. Natl Acad. Sci. USA 113, E7399–E7408 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Broser, M. et al. NeoR, a near-infrared absorbing rhodopsin. Nat. Commun. 11, 5682 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hara, T. & Hara, R. Regeneration of squid retinochrome. Nature 219, 450–454 (1968).

    Article  CAS  PubMed  Google Scholar 

  36. Furutani, Y., Terakita, A., Shichida, Y. & Kandori, H. FTIR studies of the photoactivation processes in squid retinochrome. Biochemistry 44, 7988–7997 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Smith, S. O. et al. Vibrational analysis of the all-trans retinal protonated Schiff base. Biophys. J. 47, 653–664 (1985).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ehlenbeck, S., Gradmann, D., Braun, F.-J. & Hegemann, P. Evidence for a light-induced H+ conductance in the eye of the green alga Chlamydomonas reinhardtii. Biophys. J. 82, 740–751 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Rozenberg, A. et al. Lateral gene transfer of anion-conducting channelrhodopsins between green algae and giant viruses. Curr. Biol. 30, 4910–4920(2020).

    Article  CAS  PubMed  Google Scholar 

  40. Roenneberg, T. The complex circadian system of Gonyaulax polyedra. Physiol. Plant. 96, 733–737 (1996).

    Article  CAS  Google Scholar 

  41. Forward, R. B. Phototaxis by the dinoflagellate Gymnodinium splendens Lebour. J. Protozool. 21, 312–315 (1974).

    Article  PubMed  Google Scholar 

  42. Kandori, H. Polarized FTIR spectroscopy distinguishes peptide backbone changes in the M and N photointermediates of bacteriorhodopsin. J. Am. Chem. Soc. 120, 4546–4547 (1998).

    Article  CAS  Google Scholar 

  43. Carradec, Q. et al. A global ocean atlas of eukaryotic genes. Nat. Commun. 9, 373 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Keeling, P. J. et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 12, e1001889 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Johnson, L. K., Alexander, H. & Brown, C. T. Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. GigaScience 8, 1–12 (2019).

    Google Scholar 

  46. Leebens-Mack, J. H. et al. One thousand plant transcriptomes and the phylogenomics of green plants. Nature 574, 679–685 (2019).

    Article  CAS  Google Scholar 

  47. Philosof, A. et al. Novel abundant oceanic viruses of uncultured marine group II Euryarchaeota. Curr. Biol. 27, 1362–1368 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  PubMed  Google Scholar 

  50. Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hahn, C., Bachmann, L. & Chevreux, B. Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—a baiting and iterative mapping approach. Nucleic Acids Res. 41, e129(2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhang, H. et al. Spliced leader RNA trans-splicing in dinoflagellates. Proc. Natl Acad. Sci. USA 104, 4618–4623 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).

    Article  Google Scholar 

  56. Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).

    Article  CAS  PubMed  Google Scholar 

  57. Armenteros, J. J. A. et al. Detecting sequence signals in targeting peptides using deep learning. Life Sci. Alliance 2, e201900429 (2019).

    Article  Google Scholar 

  58. Katoh, K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Whelan, S., Allen, J. E., Blackburne, B. P. & Talavera, D. ModelOMatic: fast and automated model selection between RY, ncleotide, amino acid, and codon substitution models. Syst. Biol. 64, 42–55 (2015).

    Article  CAS  PubMed  Google Scholar 

  60. Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).

    Article  CAS  PubMed  Google Scholar 

  62. Bettisworth, B. & Stamatakis, A. Root Digger: a root placement program for phylogenetic trees. BMC Bioinformatics 22, 225 (2021).

  63. Pond, S. L. K., Frost, S. D. W. & Muse, S. V. HyPhy: hypothesis testing using phylogenies. Bioinformatics 21, 676–679 (2005).

    Article  CAS  PubMed  Google Scholar 

  64. Kosakovsky Pond, S. L., Posada, D., Gravenor, M. B., Woelk, C. H. & Frost, S. D. W. Automated phylogenetic detection of recombination using a genetic algorithm. Mol. Biol. Evol. 23, 1891–1901 (2006).

    Article  PubMed  Google Scholar 

  65. Sawyer, S. Statistical tests for detecting gene conversion. Mol. Biol. Evol. 6, 526–538 (1989).

    CAS  PubMed  Google Scholar 

  66. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

    Article  CAS  PubMed  Google Scholar 

  67. Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Eddy, S. R. Profile hidden Markov models. Bioinformatics 14, 755–763 (1998).

    Article  CAS  PubMed  Google Scholar 

  69. Dunn, C. D. SequenceBouncer: a method to remove outlier entries from a multiple sequence alignment. Preprint at bioRxiv https://doi.org/10.1101/2020.11.24.395459 (2020).

  70. Gruber, A., Rocap, G., Kroth, P. G., Armbrust, E. V. & Mock, T. Plastid proteome prediction for diatoms and other algae with secondary plastids of the red lineage. Plant J. 81, 519–528 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Huson, D. H. SplitsTree: analyzing and visualizing evolutionary data. Bioinformatics 14, 68–73 (1998).

    Article  CAS  PubMed  Google Scholar 

  72. Nettling, M. et al. DiffLogo: a comparative visualization of sequence motifs. BMC Bioinformatics 16, 387 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics 20, 473 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Janouškovec, J. et al. Major transitions in dinoflagellate evolution unveiled by phylotranscriptomics. Proc. Natl Acad. Sci. USA 114, E171–E180 (2017).

    Article  PubMed  CAS  Google Scholar 

  75. Kamikawa, R. et al. Plastid genome-based phylogeny pinpointed the origin of the green-colored plastid in the dinoflagellate Lepidodinium chlorophorum. Genome Biol. Evol. 7, 1133–1140 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Stephens, T. G., Ragan, M. A., Bhattacharya, D. & Chan, C. X. Core genes in diverse dinoflagellate lineages include a wealth of conserved dark genes with unknown functions. Sci. Rep. 8, 17175 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).

    Article  PubMed  CAS  Google Scholar 

  78. Mai, U. & Mirarab, S. TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees. BMC Genomics 19, 272 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Zhang, C., Rabiee, M., Sayyari, E. & Mirarab, S. ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinformatics 19, 153 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Binet, M., Gascuel, O., Scornavacca, C., P. Douzery, E. J. & Pardi, F. Fast and accurate branch lengths estimation for phylogenomic trees. BMC Bioinformatics 17, 23 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).

    Article  CAS  PubMed  Google Scholar 

  82. Lechner, M. et al. Proteinortho: detection of (co-)orthologs in large-scale analysis. BMC Bioinformatics 12, 124 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Erijman, A., Dantes, A., Bernheim, R., Shifman, J. M. & Peleg, Y. Transfer-PCR (TPCR): a highway for DNA cloning and protein engineering. J. Struct. Biol. 175, 171–177 (2011).

    Article  CAS  PubMed  Google Scholar 

  84. 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).

    Article  CAS  PubMed  Google Scholar 

  85. Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife 7, e42166 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Kucukelbir, A., Sigworth, F. J. & Tagare, H. D. Quantifying the local resolution of cryo-EM density maps. Nat. Methods 11, 63–65 (2014).

    Article  CAS  PubMed  Google Scholar 

  87. Tang, G. et al. EMAN2: an extensible image processing suite for electron microscopy. J. Struct. Biol. 157, 38–46 (2007).

    Article  CAS  PubMed  Google Scholar 

  88. Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article  CAS  PubMed  Google Scholar 

  90. Jones, T. A. Interactive electron-density map interpretation: from INTER to O. Acta Crystallogr. Sect. D. 60, 2115–2125 (2004).

    Article  CAS  Google Scholar 

  91. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. Sect. D. 66, 486–501 (2010).

    Article  CAS  Google Scholar 

  92. Moriarty, N. W., Grosse-Kunstleve, R. W. & Adams, P. D.Electronic Ligand Builder and Optimization Workbench (eLBOW): a tool for ligand coordinate and restraint generation. Acta Crystallogr. D. Biol. Crystallogr. 65, 1074–1080 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. Sect. Struct. Biol. 74, 531–544 (2018).

    Article  CAS  Google Scholar 

  94. Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).

    Article  CAS  PubMed  Google Scholar 

  95. Smart, O. S., Neduvelil, J. G., Wang, X., Wallace, B. A. & Sansom, M. S. P. HOLE: a program for the analysis of the pore dimensions of ion channel structural models. J. Mol. Graph. 14, 354–360 (1996).

    Article  CAS  PubMed  Google Scholar 

  96. Pavelka, A. et al. CAVER: algorithms for analyzing dynamics of tunnels in macromolecules. IEEE/ACM Trans. Comput. Biol. Bioinform. 13, 505–517 (2016).

    Article  PubMed  Google Scholar 

  97. Armougom, F. et al. Expresso: automatic incorporation of structural information in multiple sequence alignments using 3D-Coffee. Nucleic Acids Res. 34, W604–W608 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Kabsch, W. & Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577–2637 (1983).

    Article  CAS  PubMed  Google Scholar 

  99. Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. 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).

    Article  CAS  PubMed  Google Scholar 

  101. Inoue, K. et al. A light-driven sodium ion pump in marine bacteria. Nat. Commun. 4, 1678 (2013).

    Article  PubMed  CAS  Google Scholar 

  102. Trehan, A. et al. On retention of chromophore configuration of rhodopsin isomers derived from three dicis retinal isomers. Bioorg. Chem. 18, 30–40 (1990).

    Article  CAS  Google Scholar 

  103. Shihoya, W. et al. Crystal structure of heliorhodopsin. Nature 574, 132–136 (2019).

    Article  CAS  PubMed  Google Scholar 

  104. Hashimoto, M., Katayama, K., Furutani, Y. & Kandori, H. Zinc binding to heliorhodopsin. J. Phys. Chem. Lett. 11, 8604–8609 (2020).

    Article  CAS  PubMed  Google Scholar 

  105. Tian, C. et al. ff19SB: amino-ccid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J. Chem. Theory Comput. 16, 528–552 (2020).

    Article  PubMed  CAS  Google Scholar 

  106. Metz, S., Kästner, J., Sokol, A. A., Keal, T. W. & Sherwood, P. ChemShell—a modular software package for QM/MM simulations. Wiley Interdiscip. Rev. Comput. Mol. Sci. 4, 101–110 (2014).

  107. Neese, F. Software update: the ORCA program system, version 4.0. Wiley Interdiscip. Rev. Comput. Mol. Sci. 8, e1327 (2018).

  108. Schirmer, J. Beyond the random-phase approximation: A new approximation scheme for the polarization propagator. Phys. Rev. A 26, 2395–2416 (1982).

    Article  CAS  Google Scholar 

  109. Balasubramani, S. G. et al. TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations. J. Chem. Phys. 152, 184107 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Grimm, C., Vierock, J., Hegemann, P. & Wietek, J. Whole-cell patch-clamp recordings for electrophysiological determination of ion selectivity in channelrhodopsins. JoVE J. Vis. Exp. e55497 (2017).

  111. Longo, P. A., Kavran, J. M., Kim, M.-S. & Leahy, D. J. in Methods in Enzymology vol. 529 (ed. Lorsch, J.) 227–240 (Academic Press, 2013).

Download references

Acknowledgements

We thank all the research initiatives that produced sequencing data used in this study, D. Bleiberg and S. Larom from the Faculty of Biology at the Technion, N. Elad at the electron microscopy unit at the Weizmann Institute of Science, and the staff at the Research Instrument and Equipment Center, Kagawa University, for their technical support. This work was supported by the Israel Science Foundation (F.I.R.S.T. program no. 3592/19 to O. B. and a Research Center grant no. 3131/20 to O. B., I. S., and O. Y.), the Kimmelman center for Biomolecular Structure and Assembly (M. Sheves), Grants-in-Aid from the Japan Society for the Promotion of Science (JSPS) for Scientific Research (KAKENHI grant nos. 17H03007 and 20K21383 to K. I.; 20K21416 to T. N.; 18H03986 and 21H04969 to H. K.), Grant-in-Aid for Transformative Research areas (b) ‘Low-Energy Manipulation’ from MEXT, Japan (KAKENHI grant no. 20H05758 to K. I.), Grant-in-Aid for Scientific Research on Innovative Areas ‘Non-equilibrium-state molecular movies and their applications (Molecular Movies)’ from MEXT, Japan (KAKENHI grant no. 19H05784 to Y. Furutani) and the Japan Science and Technology Agency (JST), Japan, PRESTO (grant nos. JPMJPR1888 to T. N.; JPMJPR1903 to M. K.; and JPMJPR19G4 to K. K.), CREST (grant nos. JPMJCR1753 to H. K; and JPMJCR17N5 to Y. Furuani), Takeda Science Foundation (Y. Fujiwara), German Research Foundation (SPP1926 no. 425994138 to P. H.), the Zuckerman STEM Leadership Program (M. S.-B.), the Yeda-Sela-SABRA-WRC grant (M. S.-B.), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (No. 949364 to M. S.-B., and 693742 to P. H.) and the German research foundation DFG (SFB1315 to P. H.). P. H. is Hertie Professor for Biophysics and is supported by the Hertie Foundation, J. D. is incumbent of the Achar Research Fellow Chair in Electrophysiology, M. Sheves holds the Katzir-Makineni Chair in Chemistry, O. B. holds the Louis and Lyra Richmond Chair in Life Sciences, and M. S.-B. holds the Tauro Career Development Chair in Biomedical Research. Y. Furuani is supported by the Equipment Sharing Division in the Organization for Co-Creation Research and Social Contributions in Nagoya Institute of Technology.

Author information

Author notes

  1. These authors contributed equally: Andrey Rozenberg, Igor Kaczmarczyk, Donna Matzov, Johannes Vierock.

Authors and Affiliations

  1. Faculty of Biology, Technion — Israel Institute of Technology, Haifa, Israel

    Andrey Rozenberg, Ariel Chazan & Oded Béjà

  2. Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel

    Igor Kaczmarczyk, Donna Matzov & Moran Shalev-Benami

  3. Institute for Biology, Experimental Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany

    Johannes Vierock, Sandra Augustin & Peter Hegemann

  4. The Institute for Solid State Physics, The University of Tokyo, Kashiwa, Japan

    Takashi Nagata, Yuma Kawasaki, Masae Konno, Yujiro Nagasaka & Keiichi Inoue

  5. PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan

    Takashi Nagata, Kota Katayama & Masae Konno

  6. Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Showa-ku, Japan

    Masahiro Sugiura, Kota Katayama, Mako Aoyama, Yuji Furutani & Hideki Kandori

  7. OptoBioTechnology Research Center, Nagoya Institute of Technology, Showa-ku, Japan

    Kota Katayama, Yuji Furutani & Hideki Kandori

  8. Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, Rehovot, Israel

    Ishita Das & Mordechai Sheves

  9. Fritz Haber Center for Molecular Dynamics Research Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel

    Efrat Pahima, Jonathan Church, Suliman Adam, Veniamin A. Borin & Igor Schapiro

  10. Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel

    Jonas Wietek, Julien Dine & Ofer Yizhar

  11. Structural Proteomics Unit (SPU), Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot, Israel

    Yoav Peleg

  12. Laboratory of Molecular Physiology & Biophysics, Faculty of Medicine, Kagawa University, Miki-cho, Japan

    Akira Kawanabe & Yuichiro Fujiwara

Authors
  1. Andrey Rozenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Igor Kaczmarczyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Donna Matzov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Johannes Vierock
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Takashi Nagata
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Masahiro Sugiura
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kota Katayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yuma Kawasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Masae Konno
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yujiro Nagasaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Mako Aoyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ishita Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Efrat Pahima
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jonathan Church
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Suliman Adam
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Veniamin A. Borin
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ariel Chazan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Sandra Augustin
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Jonas Wietek
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Julien Dine
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Yoav Peleg
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Akira Kawanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Yuichiro Fujiwara
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Ofer Yizhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Mordechai Sheves
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Igor Schapiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Yuji Furutani
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Hideki Kandori
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Keiichi Inoue
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Peter Hegemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Oded Béjà
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Moran Shalev-Benami
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A. R. and O. B. discovered the bestrhodopsin fusions, and together with K. I., H. K., P. H. and M. S.-B. conceived the project and designed the experiments. O. B and M. S.-B. coordinated the project. A. R. and O. B. performed bioinformatic analyses; A. C. and Y. P. performed molecular biology; I. K., D. M. and M. S.-B. performed protein biochemistry and cryo-EM studies; J. V., S. Augustin, P. H., J. W., J. D., O. Y., A. K. and Y. Fujiwara performed electrophysiological experiments; T. N., M. K., Y. N., Y. K. and K. I. performed laser flash photolysis and HPLC analysis of retinal isomers; I. D. and M. Sheves performed absorption and circular dichroism spectroscopies; M. A., K. K., M. Sugiura, Y. Furutani, and H. K. performed time-resolved and low-temperature FTIR spectroscopy; E. P., J. C., S. Adam, V. A. B. and I. S. performed MD simulations, QM/MM optimization, excitation energy calculations and interpreted the results of the simulations. A. R., O. B., K. I., H. K., P. H., I. S. and M. S.-B. wrote the paper, which was critically revised and approved by all authors.

Corresponding authors

Correspondence to Oded Béjà or Moran Shalev-Benami.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Structural and Molecular Biology thanks Wayne Hendrickson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling editor: Florian Ullrich, in collaboration with the Nature Structural and Molecular Biology team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Phylogenetic position of the rhodopsin and bestrophin domains of bestrhodopsins.

a. Phylogeny of microbial rhodopsins. Phylogenetic analysis was performed for 50%-identity clusters of bestrhodopsin rhodopsin domains, bestrhodopsin-related dinoflagellate rhodopsins, chromerid 8TM rhodopsins, UniRef50 clusters of other 8TM rhodopsins, as well as characterized rhodopsin subfamilies as outgroups (in gray, heliorhodopsins and schizorhodopsins were excluded) (LG + F + R10 as the best-fit substitution model). 8TM rhodopsin clusters are colored by the taxonomic groups. Enzymatic and channeling domains are indicated for fusion rhodopsin fusions. Representatives with the three counterion positions occupied by carboxylate residues are indicated with stars. b, Phylogenetic tree of the bestrophin family. Phylogenetic relationships between UniRef50 clusters assigned to the Pfam family PF01062.23 (LG + F + R10 as the best-fit substitution model). Sequences with predicted primary chloroplast transit peptides (cTP) and heterokont bipartite chloroplast targeting peptides are indicated. Clusters are colored by the taxonomic groups and relatively taxonomically homogenic clades are highlighted with labels. Edges with UFboot support values ≥95 are indicated with dots.

Source data

Extended Data Fig. 2 Purification of Tara-RRB and cryo-EM flow chart.

a, Size exclusion chromatography (SEC) profile of Tara-RRB. Fractions corresponding to the main peak of pentamers (cyan) were combined and used for the structural studies. b, SDS page analysis of fractions corresponding to the Tara-RRB purification process. Fractions used for the structural studies are boxed. Single arrow indicates a monomeric fraction corresponding to a 134 kDa protein; higher oligomeric states (that remained intact during the SDS run) are indicated with a double arrow. M, S, F, E1-3 and I correspond to Marker, Solubilization, Flow-through, Elutions and Injected fractions, respectively. A full description of purification procedures is supplemented in the Extended Materials and Methods section. c, In the presence of all-trans retinal, purified Tara-RRB has a distinctive cyan color. d, Representative cryo-EM micrograph of the Tara-RRB sample. Scale bar diameter is 50 nm. e, Representative reference free 2D class averages of the Tara-RRB. The diameter of the circular mask is 25 nm. f, Processing flow chart of Tara-RRB cryo-EM data, including particle selection, 2D and 3D classifications, particle sorting, masking and final map reconstruction. Pixel size used for all processing steps is 0.86 Å. Data processing was done by combining cryo-SPARC 3.0.1 (ref. 84) and Relion 3.1 (ref. 85) software used for individual processing steps are indicated in blue. A C5 symmetry was implemented in most 3D steps, and is indicated in parentheses where applicable.

Extended Data Fig. 3 Snapshots of RRB map and model.

a, Selected snapshots of RRB coordinates in density. Maps with individual domains colored are presented in the middle with R1 in teal, R2 in purple and bestrophin channel in yellow. Localization within the relevant map segment is indicated by arrows pointing from the overall EM map. Domain names are indicated per view, with R1 and R2 for rhodopsins 1 and 2, BEST for bestrophin domain, TMD for transmembrane region of the bestrophin channel and ICD for the extra-membranal channel region. b, A view of retinal in density within the orthosteric binding pocket in R2. Retinal is in light green. Map contour level is 0.00352. c, Elongated densities observed in the interface between TMs 5 and 6 of R2 and the bestrophin channel. Densities correspond to CHS, GDN or to the retinal added throughout the purification.

Extended Data Fig. 4 Interactions between the rhodopsin components in RRB and the channel.

a-f, The connections between the rhodopsins and the channel in RRB are mediated through a network of electrostatic and hydrophobic interactions between the linkers connecting the two rhodopsin units (R1-R2), R2 and the channel and the ICL1 and ICL3, connecting TM1 and 2 in R1 and TM5 and 6 in R2, respectively. A snapshot of an RRB protomer with residues maintaining the rhodopsin-bestrophin connection is in (a). An overview in the context of full RRB is in (b). Residues participating in the interactions are highlighted in their distinct colors by domain, with R1 in teal, R2 in purple and the channel in yellow. c-f, Views of the interaction interface between the rhodopsins and the bestrophin channel. Panels represent close ups of regions boxed in (b). Residue identity and numbers are provided. Red dots highlight residues that are less than 4 Å away from one another and indicate close contacts. g, The bestrophin domain in RRB is similar to other reported bestrophin channels with the exception of the N-terminus domain. A comparison between a monomer from chicken bestrophin (cBEST1) and an RRB monomer is provided: RRB in yellow and cBEST1 in red (PDB ID: 6N23). The N-termini domains of the same unit and an adjacent protomer in RRB are highlighted in yellow. Boxed segment is enlarged in (h). h, Animal bestrophins depend on Ca2+ for activation. The loop coordinating Ca2+ binding in cBEST1 is enriched with acidic residues, and overlaps with a similarly positioned loop in RRB. The loop on RRB is sandwiched between two N-termini channel domains belonging to the same and to an adjacent protomer that are unique to RRB. cBEST1 is shown in red (PDB ID: 6N23) with Ca2+ ion shown as a gray sphere. RRB is shown in yellow with N-terminal domains from two independent protomers highlighted.

Extended Data Fig. 5 Sequence conservation and similarity in the helical stretch of the R1R2 and R2B linkers among different rhodopsin domains in bestrhodopsins.

a, NeighborNet network built on the basis of uncorrected distances between linker sequences. Asterisks indicate rhodopsin domains from fragmented sequences. b, Sequence logos and predicted secondary structures (above) and differential logos for linkers following rhodopsins from three groups (below): R1 and R2 in RRBs and R in RBs. Per-position secondary structure predictions are scaled by PSIPRED confidence scores. The observed helix boundaries are indicated for R1 as a red frame.

Source data

Extended Data Fig. 6 Comparison between RRB-bestrophin and other bestrophins.

a, The bestrophin domain in RRB is driving pentamer organization. An RRB-bestrophin monomer (yellow) in the context of a pentamer (gray) is presented. Helix numbers are indicated. The N-terminus segment of the RRB bestrophin is highlighted in light yellow. Bestrophin is shown from the side (upper) and top (lower). b, Comparison of RRB with bacterial (KpBest, PDB ID: 4WD8) and animal (Chicken — cBEST1, PDB ID 6N23; Bovine — bBEST2, PDB ID 6VX9) bestrophins. Residues localized within the channel conducting pore that are directed towards the pore and restrict ion pathway are indicated. Colors are by helix. c, Domain organization in bacterial bestrophins and nd bestrhodopsins (left) and in animal bestrophins (right). Colors same as in (b). Helix nomenclature is as adapted for KpBest14 (left) and for animal bestrophins33 (right), correspondingly. N-terminal extensions of animal bestrophins and bestrhodopsins are not shown for simplicity. d, Neck and apparatus residues among RBs, RRBs and bestrophins. The neck region in RRB is composed of three key hydrophobic residues: V955, F959 and F963 and is further extended by K970 that points toward the ion conducting pathway to form additional constraints. The hydrophobic triad is highly conserved across rhodopsins. K970 partially overlaps with H91 in bBEST. The aperture residues in RRB, M1075 and N1079, correspond to the aperture residues K208 and E212 in bBEST2. Helix α7 in Tara-RRB is shorter than in KpBest and thus entirely lacks the position that forms the aperture in KpBest and cBEST1 (I180 and V205, respectively).

Extended Data Fig. 7 Bestrhodopsin encompasses a fold similar to the divalent ion binding clasp in animal and bacterial rhodopsins.

a, Sequence alignment of residues within the ion binding clasps shown to bind Ca2+ and Zn2+ in animal bestrophins and in KpBest, respectively. Bestrhodopsins are grouped by clade and similarity, with alternative residues shown for variable positions. The sequence logo corresponds to the bestrhodopsin part of the alignment. Carboxylic residues that coordinate cation binding are marked. b, Structural context of homologous positions to the binding pocket in Tara-RRB and Kv-RRB compared with the Zn2+ binding cleft in KpBest (PDB ID: 4WD8) and the Ca2+ clasp in cBEST1 (PDB ID: 6N23). Residues that coordinate (or are predicted to coordinate) the divalent cations are in stick representation. Coordinates of Kv-RRB are adopted from a homology model generated with the Tara-RRB as a template. c-e, Binding of zinc ions to Tara-RRB monitored by ATR-FTIR spectroscopy. c, Difference FTIR spectra upon addition of Zn2+ (top) and Ca2+ (bottom) at a fixed concentration of 100 μM. d, Difference FTIR spectra at various Zn2+ concentrations from 0.5 to 500 μM. e, Typical IR band intensities of carboxylate COO− antisymmetric vibration at 1599-1550 cm−1 against Zn2+ concentration. Solid lines were obtained from curve fitting using the Hill equation, whose KD is determined to be 45 μM.

Source data

Extended Data Fig. 8 The absorption spectra of the intermediates in the photocycle and the photoreversibility of Tara-RRB.

a, Absorption spectra of the P556(/P(prim)) and P536 intermediates of Tara-RRB reconstructed from the transient absorption change. The spectra of P556(/P(prim)) and P536 intermediates were calculated by adding the absorption spectrum of the dark state of Tara-RRB to the species-associated-spectra obtained by the multi-exponential global fitting of the transient absorption change. The spectrum of P556 was very broad toward the longer-wavelength side due to a quasi-equilibrium with the P(prim)-intermediate. b, Absorption changes of Tara-RRB reversibly converted by red and green illuminations. Absorption increase representing the accumulation of the P536 intermediate was observed at 523 nm after the red-light illumination (> 640 nm), and subsequent illumination of green light (= 530 nm) rapidly recovered the initial state.

Source data

Extended Data Fig. 9 Temperature-dependent UV-visible and FTIR spectroscopies of Tara-RRB.

a, Difference UV-visible spectra upon illumination of RRB at >640 nm at each temperature from 77 K to 270 K (solid lines), followed by illumination at 540 nm light (broken lines). b, FTIR difference spectra upon illumination of RRB at >640 nm at each temperature from 77 K to 270 K measured in H2O (solid lines) and D2O (dotted line) hydrations, respectively. c, Comparison of FTIR difference spectra in the amide-I band region corresponding to backbone C=O stretch for RRB (P536 minus RRB), a light-driven proton-pump bacteriorhodopsin (N minus bR), and squid retinochrome (meta minus sRC) measured in H2O (black lines) and D2O (red lines) hydrations, respectively. d, FTIR difference spectra in the protonated carboxylate C=O stretch region for RRB illuminated at >640 nm at 250 K measured in H2O (black line) and D2O (red line), respectively. A negative peak and broad positive bands were observed at 1746 cm−1 and 1740–1700 cm−1, respectively, which show spectral downshift in D2O. Therefore, multiple carboxylates alter hydrogen bonds upon formation of the P536 intermediate.

Source data

Extended Data Fig. 10 Electrophysiological characterization of Kv-R1R2 and Kv-RRB.

a, Transient photocurrents of Kv-R1R2 in symmetric 110 mM NaCl at pH 7.2 and 0 mV following different illumination protocols with 405 nm, 525 nm and 680 nm light. Action spectra of early transient Kv-R1R2 currents at 0 mV after pre illumination with 405 nm (b), 525 nm (c) and 660 nm (d) (mean ± SD; n = 3/5/4 for 405 nm/525 nm/660 nm). e Light titration of Kv-RRB channel activation with 100 ms light pulses of 525 nm and 660 nm (top), and light titration of Kv-RRB channel inactivation with 100 ms light pulses of 405 nm (bottom). Representative photocurrents for 0.05 mW/mm2, 0.3 mW/mm2 and 5.3 mW/mm2 525 nm illumination (top) and 0.2 mW/mm2, 1.1 mW/mm2 and 23 mW/mm2 405 nm illumination. Current changes during channel opening and closing were normalized and plotted for different light intensities (middle, mean ± SD, n = 5/7/3 for 405 nm, 525 nm and 660 nm). f, Representative photocurrents of Kv-RRB with different extracellular ion solutions and the voltage protocol provided on top. Pipette solution contained 110 mM NaCl and pHi 7.2 with 300 nM free CaCl2 and 2 mM MgATP. g and h, Normalized photocurrents of Kv-RRB with different extracellular anions and cations at –80 mV (i) and +40 mV (j) (mean ± SD, n = 9/5/8/3/4 for NaGluc/NMGCl/NaSCN/NaI/NaBr). Applied voltages were corrected for liquid junction potentials during measurement.

Source data

Supplementary information

Supplementary Information

Supplementary Figures 1–13

Reporting Summary

Supplementary Data File 1

Sheet 'genes': bestrhodopsin and bestrhodosin-related genes collected; sheet 'searched data': dinoflagellates and other algae searched for the presence of bestrhodopsin genes

Supplementary Data File 2

Collected sequences of the bestrhodopsin genes with descriptions of data sources and annotated CDS regions

Supplementary Data 3

Collected sequences of bestrhopdopsin-related genes with annotated CDS regions

Source data

Source Data Fig. 1

Unrooted phylogenetic trees in newick format

Source Data Fig. 3

HOLE output files

Source Data Fig. 4

Numerical source data

Source Data Fig. 5

Numerical source data

Source Data Fig. 6

Numerical source data

Source Data Extended Data Fig. 1

Tree Files for Bestrophins and Rhodopsins

Source Data Extended Data Fig. 5

Sequence alignment and NeighborNet network in nexus format

Source Data Extended Data Fig. 7

Numerical source data

Source Data Extended Data Fig. 8

Numerical source data

Source Data Extended Data Fig. 9

Numerical source data

Source Data Extended Data Fig. 10

Numerical source data

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rozenberg, A., Kaczmarczyk, I., Matzov, D. et al. Rhodopsin-bestrophin fusion proteins from unicellular algae form gigantic pentameric ion channels. Nat Struct Mol Biol 29, 592–603 (2022). https://doi.org/10.1038/s41594-022-00783-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41594-022-00783-x

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing