Letter | Published:

Visualization of clustered protocadherin neuronal self-recognition complexes

Abstract

Neurite self-recognition and avoidance are fundamental properties of all nervous systems1. These processes facilitate dendritic arborization2,3, prevent formation of autapses4 and allow free interaction among non-self neurons1,2,4,5. Avoidance among self neurites is mediated by stochastic cell-surface expression of combinations of about 60 isoforms of α-, β- and γ-clustered protocadherin that provide mammalian neurons with single-cell identities1,2,4,5,6,7,8,9,10,11,12,13. Avoidance is observed between neurons that express identical protocadherin repertoires2,5, and single-isoform differences are sufficient to prevent self-recognition10. Protocadherins form isoform-promiscuous cis dimers and isoform-specific homophilic trans dimers10,14,15,16,17,18,19,20. Although these interactions have previously been characterized in isolation15,17,18,19,20, structures of full-length protocadherin ectodomains have not been determined, and how these two interfaces engage in self-recognition between neuronal surfaces remains unknown. Here we determine the molecular arrangement of full-length clustered protocadherin ectodomains in single-isoform self-recognition complexes, using X-ray crystallography and cryo-electron tomography. We determine the crystal structure of the clustered protocadherin γB4 ectodomain, which reveals a zipper-like lattice that is formed by alternating cis and trans interactions. Using cryo-electron tomography, we show that clustered protocadherin γB6 ectodomains tethered to liposomes spontaneously assemble into linear arrays at membrane contact sites, in a configuration that is consistent with the assembly observed in the crystal structure. These linear assemblies pack against each other as parallel arrays to form larger two-dimensional structures between membranes. Our results suggest that the formation of ordered linear assemblies by clustered protocadherins represents the initial self-recognition step in neuronal avoidance, and thus provide support for the isoform-mismatch chain-termination model of protocadherin-mediated self-recognition, which depends on these linear chains11.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

Crystallographic atomic coordinates and structure factors have been deposited in the Protein Data Bank with accession code PDB 6E6B. Single-particle and cPCDH–liposome (binned by four or two tomograms) datasets were deposited in the Electron Microscopy Data Bank (EMDB) with accession codes EMD-9197, EMD-9198, EMD-9199 and EMD-9200. Single particle data, unaligned tilt-series images, Appion–Protomo tilt-series alignment runs and aligned tilt-series stacks were deposited in the Electron Microscopy Pilot Image Archive (EMPIAR) with accession codes EMPIAR-10234, EMPIAR-10235, EMPIAR-10236, EMPIAR-10237 and EMPIAR-10238. Any other relevant data are available from the corresponding authors upon reasonable request.

Additional information

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

References

  1. 1.

    Zipursky, S. L. & Grueber, W. B. The molecular basis of self-avoidance. Annu. Rev. Neurosci. 36, 547–568 (2013).

  2. 2.

    Lefebvre, J. L., Kostadinov, D., Chen, W. V., Maniatis, T. & Sanes, J. R. Protocadherins mediate dendritic self-avoidance in the mammalian nervous system. Nature 488, 517–521 (2012).

  3. 3.

    Molumby, M. J., Keeler, A. B. & Weiner, J. A. Homophilic protocadherin cell–cell interactions promote dendrite complexity. Cell Reports 15, 1037–1050 (2016).

  4. 4.

    Kostadinov, D. & Sanes, J. R. Protocadherin-dependent dendritic self-avoidance regulates neural connectivity and circuit function. eLife 4, e08964 (2015).

  5. 5.

    Mountoufaris, G. et al. Multicluster Pcdh diversity is required for mouse olfactory neural circuit assembly. Science 356, 411–414 (2017).

  6. 6.

    Wu, Q. & Maniatis, T. A striking organization of a large family of human neural cadherin-like cell adhesion genes. Cell 97, 779–790 (1999).

  7. 7.

    Wu, Q. et al. Comparative DNA sequence analysis of mouse and human protocadherin gene clusters. Genome Res. 11, 389–404 (2001).

  8. 8.

    Zipursky, S. L. & Sanes, J. R. Chemoaffinity revisited: dscams, protocadherins, and neural circuit assembly. Cell 143, 343–353 (2010).

  9. 9.

    Yagi, T. Molecular codes for neuronal individuality and cell assembly in the brain. Front. Mol. Neurosci. 5, 45 (2012).

  10. 10.

    Thu, C. A. et al. Single-cell identity generated by combinatorial homophilic interactions between α, β, and γ protocadherins. Cell 158, 1045–1059 (2014).

  11. 11.

    Rubinstein, R., Goodman, K. M., Maniatis, T., Shapiro, L. & Honig, B. Structural origins of clustered protocadherin-mediated neuronal barcoding. Semin. Cell Dev. Biol. 69, 140–150 (2017).

  12. 12.

    Toyoda, S. et al. Developmental epigenetic modification regulates stochastic expression of clustered protocadherin genes, generating single neuron diversity. Neuron 82, 94–108 (2014).

  13. 13.

    Ing-Esteves, S. et al. Combinatorial effects of alpha- and gamma-protocadherins on neuronal survival and dendritic self-avoidance. J. Neurosci. 38, 2713–2729 (2018).

  14. 14.

    Schreiner, D. & Weiner, J. A. Combinatorial homophilic interaction between γ-protocadherin multimers greatly expands the molecular diversity of cell adhesion. Proc. Natl Acad. Sci. USA 107, 14893–14898 (2010).

  15. 15.

    Rubinstein, R. et al. Molecular logic of neuronal self-recognition through protocadherin domain interactions. Cell 163, 629–642 (2015).

  16. 16.

    Nicoludis, J. M. et al. Structure and sequence analyses of clustered protocadherins reveal antiparallel interactions that mediate homophilic specificity. Structure 23, 2087–2098 (2015).

  17. 17.

    Goodman, K. M. et al. Structural basis of diverse homophilic recognition by clustered α- and β-protocadherins. Neuron 90, 709–723 (2016).

  18. 18.

    Goodman, K. M. et al. γ-Protocadherin structural diversity and functional implications. eLife 5, e20930 (2016).

  19. 19.

    Nicoludis, J. M. et al. Antiparallel protocadherin homodimers use distinct affinity- and specificity-mediating regions in cadherin repeats 1-4. eLife 5, e18449 (2016).

  20. 20.

    Goodman, K. M. et al. Protocadherin cis-dimer architecture and recognition unit diversity. Proc. Natl Acad. Sci. USA 114, E9829–E9837 (2017).

  21. 21.

    Noble, A. J. et al. Routine single particle cryoEM sample and grid characterization by tomography. eLife 7, e34257 (2018).

  22. 22.

    Harrison, O. J. et al. The extracellular architecture of adherens junctions revealed by crystal structures of type I cadherins. Structure 19, 244–256 (2011).

  23. 23.

    Schalm, S. S., Ballif, B. A., Buchanan, S. M., Phillips, G. R. & Maniatis, T. Phosphorylation of protocadherin proteins by the receptor tyrosine kinase Ret. Proc. Natl Acad. Sci. USA 107, 13894–13899 (2010).

  24. 24.

    Suo, L., Lu, H., Ying, G., Capecchi, M. R. & Wu, Q. Protocadherin clusters and cell adhesion kinase regulate dendrite complexity through Rho GTPase. J. Mol. Cell Biol. 4, 362–376 (2012).

  25. 25.

    Keeler, A. B., Schreiner, D. & Weiner, J. A. Protein kinase C phosphorylation of a γ-protocadherin c-terminal lipid binding domain regulates focal adhesion kinase inhibition and dendrite arborization. J. Biol. Chem. 290, 20674–20686 (2015).

  26. 26.

    Mah, K. M. & Weiner, J. A. Regulation of Wnt signaling by protocadherins. Semin. Cell Dev. Biol. 69, 158–171 (2017).

  27. 27.

    Fan, L. et al. Alpha protocadherins and Pyk2 kinase regulate cortical neuron migration and cytoskeletal dynamics via Rac1 GTPase and WAVE complex in mice. eLife 7, e35242 (2018).

  28. 28.

    Haas, I. G., Frank, M., Véron, N. & Kemler, R. Presenilin-dependent processing and nuclear function of γ-protocadherins. J. Biol. Chem. 280, 9313–9319 (2005).

  29. 29.

    Petersen, T. N., Brunak, S., von Heijne, G. & Nielsen, H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat. Methods 8, 785–786 (2011).

  30. 30.

    Kabsch, W. XDS. Acta Crystallogr. D 66, 125–132 (2010).

  31. 31.

    Strong, M. et al. Toward the structural genomics of complexes: crystal structure of a PE/PPE protein complex from Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 103, 8060–8065 (2006).

  32. 32.

    McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).

  33. 33.

    Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

  34. 34.

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

  35. 35.

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

  36. 36.

    Jain, T., Sheehan, P., Crum, J., Carragher, B. & Potter, C. S. Spotiton: a prototype for an integrated inkjet dispense and vitrification system for cryo-TEM. J. Struct. Biol. 179, 68–75 (2012).

  37. 37.

    Dandey, V. P. et al. Spotiton: New features and applications. J. Struct. Biol. 202, 161–169 (2018).

  38. 38.

    Wei, H. et al. Optimizing “self-wicking” nanowire grids. J. Struct. Biol. 202, 170–174 (2018).

  39. 39.

    Razinkov, I. et al. A new method for vitrifying samples for cryoEM. J. Struct. Biol. 195, 190–198 (2016).

  40. 40.

    Suloway, C. et al. Automated molecular microscopy: the new Leginon system. J. Struct. Biol. 151, 41–60 (2005).

  41. 41.

    Suloway, C. et al. Fully automated, sequential tilt-series acquisition with Leginon. J. Struct. Biol. 167, 11–18 (2009).

  42. 42.

    Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017)

  43. 43.

    Noble, A. J. & Stagg, S. M. Automated batch fiducial-less tilt-series alignment in Appion using Protomo. J. Struct. Biol. 192, 270–278 (2015).

  44. 44.

    Lander, G. C. et al. Appion: an integrated, database-driven pipeline to facilitate EM image processing. J. Struct. Biol. 166, 95–102 (2009).

  45. 45.

    Winkler, H. & Taylor, K. A. Accurate marker-free alignment with simultaneous geometry determination and reconstruction of tilt series in electron tomography. Ultramicroscopy 106, 240–254 (2006).

  46. 46.

    Agulleiro, J. I. & Fernandez, J. J. Fast tomographic reconstruction on multicore computers. Bioinformatics 27, 582–583 (2011).

  47. 47.

    Agulleiro, J.-I. & Fernandez, J.-J. Tomo3D 2.0—exploitation of advanced vector extensions (AVX) for 3D reconstruction. J. Struct. Biol. 189, 147–152 (2015).

  48. 48.

    Grant, T. & Grigorieff, N. Measuring the optimal exposure for single particle cryo-EM using a 2.6 Å reconstruction of rotavirus VP6. eLife 4, e06980 (2015).

  49. 49.

    Castaño-Díez, D., Kudryashev, M., Arheit, M. & Stahlberg, H. Dynamo: a flexible, user-friendly development tool for subtomogram averaging of cryo-EM data in high-performance computing environments. J. Struct. Biol. 178, 139–151 (2012).

  50. 50.

    Castaño-Díez, D., Kudryashev, M. & Stahlberg, H. Dynamo Catalogue: geometrical tools and data management for particle picking in subtomogram averaging of cryo-electron tomograms. J. Struct. Biol. 197, 135–144 (2017).

  51. 51.

    Bepler T. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Preprint at https://arxiv.org/abs/1803.08207 (2018).

  52. 52.

    Kimanius, D., Forsberg, B. O., Scheres, S. H. W. & Lindahl, E. Accelerated cryo-EM structure determination with parallelisation using GPUs in RELION-2. eLife 5, e18722 (2016).

  53. 53.

    Chen, M. et al. Convolutional neural networks for automated annotation of cellular cryo-electron tomograms. Nat. Methods 14, 983–985 (2017).

  54. 54.

    Evans, P. R. & Murshdov, G. N. How good are my data and what is the resolution? Acta Crystallogr. D 69, 1204–1214 (2013).

  55. 55.

    Evans, P. Scaling and assessment of data quality. Acta Crystallogr. D 62, 72–82 (2006).

Download references

Acknowledgements

We thank D. Neau, S. Banerjee and S. Narayanasami for help with synchrotron data collection, conducted at the APS NE-CAT 24-ID-C beamline (supported by National Institutes of Health (NIH) P41GM103403). We acknowledge support from a National Science Foundation grant (MCB-1412472) to B.H., NIH grants R01MH114817 to T.M. and L.S., F32GM128303 to A.J.N. and R01GM081871 to B.B. Electron microscopy was performed at the Simons Electron Microscopy Center (SEMC) and National Resource for Automated Molecular Microscopy located at the New York Structural Biology Center, which is supported by grants from the Simons Foundation (SF349247), NYSTAR and NIH (GM103310 and OD019994) and from the Agouron Institute (F00316). We thank E. Eng and L.Y. Yen for technical support at SEMC.

Reviewer information

Nature thanks Rob Meijers and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

J.B., K.M.G., T.M., B.H. and L.S. designed experiments. J.B. performed liposome assays and all electron microscopy experiments. K.M.G. performed crystallography experiments. A.J.N. provided assistance with tomography imaging and with the reconstruction of the tomograms. S.M., F.B. and K.M.G. cloned, expressed and purified proteins. M.R. performed annotation of tomograms. V.P.D. prepared single-particle electron microscopy grids using Spotiton. T.B. and B.B. developed the neural-network particle picker. C.S.P. and B.C. oversaw the electron microscopy. B.H. and L.S. supervised the project. J.B. and K.M.G. prepared figures. J.B., K.M.G. and L.S. prepared the initial draft of the manuscript. J.B., K.M.G., A.J.N., T.M., C.S.P., B.C., B.H. and L.S. edited the manuscript.

Competing interests

The authors declare no competing interests.

Correspondence to Barry Honig or Lawrence Shapiro.

Extended data figures and tables

  1. Extended Data Fig. 1 X-ray diffraction anisotropy and electron density map quality for the low-resolution γB4EC1–6 crystal structure.

    a, UCLA Diffraction Anisotropy Server31 output showing the data strength as measured by F/σ along the a*, b* and c* axes. b, The diffraction limits along the a*, b* and c* axes determined by three different methods: F/σ from (a), and the correlation coefficient (CC) and I/σ limits calculated by Aimless54,55. c, Synthetic precession photographs of the X-ray diffraction in the k = 0 plane (left) and the l = 0 plane (right), showing the comparatively stronger or weaker diffraction. d, Examples of electron density images of the γB4EC1–6 crystal structure, highlighting the difference density observed for ligand molecules after placement of all protein domains and one round of rigid-body refinement. Left, difference density for a glycosylated asparagine residue (Asn513, chain B). Right, difference density for the three calcium ions coordinated between extracellular cadherin domains (EC2–EC3 chain B). 2Fo − Fc (blue) and Fo − Fc maps (green and red) are shown contoured at 1.0 and ± 3.0σ, respectively. e, Example of electron density image of the γB4EC1–6 crystal structure after refinement, showing the cis interface. The EC5–EC6 protomer is coloured pink, and the EC6-only protomer is coloured yellow. 2Fo − Fc (blue) and Fo − Fc maps (green and red) are shown contoured at 1.0 and ± 3.0σ, respectively.

  2. Extended Data Fig. 2 Comparison between the γB4EC1–6 crystal structure and cPCDH γB fragment structures reveals that the formation of the zipper assembly does not require large conformational changes.

    a, Structural superposition of the γB4EC1–6 cis dimer from the crystal structure (one protomer in slate ribbon, the other in green) with the γB7EC3–6 fragment cis-dimer structure (PDB 5V5X; pink ribbon), showing the overall similarity between the two structures (particularly in the EC5–6/EC6 cis-interacting regions). b, Structural superposition of the γB4EC1–6 trans dimer from the crystal structure (slate and green ribbon) with the γB2EC1–5 fragment trans-dimer structure (PDB 5T9T; gold ribbon), showing the overall similarity between the trans dimers.

  3. Extended Data Fig. 3 Particle selection and subtomogram averaging of cPCDH γB6 complexes in solution.

    a, Representative tomographic slice that shows the orientation of γB6EC1–6 complexes in vitreous ice. Note that front views are predominant, and represent a preferred orientation. Axis scale is in pixels. b, Complexes in the ice are selected as dipole sets (blue sticks). For each particle ‘north’, ‘centre’ and ‘south’ points are marked as blue, cyan and red spheres, respectively. Axis scale is in pixels. c, Sub-volumes of pre-oriented particles were extracted from tomograms, and sub-tomogram averaged; projections of the final iteration after convergence are shown on the right.

  4. Extended Data Fig. 4 Two-dimensional cryo-electron microscopy of γB6EC1–6 in solution.

    a, Representative grid atlas of a grid prepared using Spotiton. Orange box highlights the path of sample deposition. b, Representative micrograph of γB6EC1–6 in vitreous ice. Individual extracellular cadherin domains are distinguishable within the ellipsoid particles. Orange boxes indicate representative particles. c, Two-dimensional class averages, calculated using Relion, show highly preferred orientation of γB6EC1–6 in the ice. Five separate class averages are shown.

  5. Extended Data Fig. 5 Structural comparisons of the dimer-of-dimers model from single-particle cryo-EM with crystallographic cis and trans dimers.

    a, Crystallographic cis dimers of γB7EC3–6 (blue ribbon) were aligned with the dimer-of-dimers model (space fill, colours as shown in Fig. 1) over the EC5–EC6 cis-dimer regions derived from γB7EC3–6 (black bars). The EC4–EC5 linker region appears to accommodate a high degree of structural variation. b, Crystallographic γB2EC1–5 trans dimers (blue ribbon) were aligned with the manually positioned EC1–EC2 and EC3–EC4 dimer fragments (black bars) in the dimer-of-dimers density. Deviations derive from differences in rotation and bend at the EC2–EC3 and EC3–EC4 linker regions within the antiparallel EC1–EC4 trans dimers. c, Comparison of the EC4–EC5 interdomain deflection angles between the dimer-of-dimers model (left) and the crystallographic γB7EC3–6 cis dimer (right), highlighting the variations between them. Individual extracellular cadherin domains were defined as axes in UCSF Chimera and are shown as cylinders. All interdomain deflection angles are listed in Extended Data Table 2. d, The dimer-of-dimers model was assembled by rigid-body-fitting into the cryo-ET density of four-domain trans (EC1–EC2 and EC3–EC4) and cis (EC5–EC6 and EC5–EC6) units from the γB2EC1–5 and γB7EC3–6 crystal structures, respectively. Deflection and rotational angles between these docked units in the final dimer-of-dimers model (left) compared with those in the γB2EC1–5 trans dimer (right), highlighting the conformational change required within the EC1–EC4 trans interaction to facilitate formation of the dimer-of-dimers. e, Deflection and rotational angles between EC5–EC6 and EC5–EC6 cis-interaction and the EC3–EC4 and EC1–EC2 trans-interaction units in the repeating unit of the crystallographic γB4EC1–6 zipper array, for comparison to the dimer-of-dimers model.

  6. Extended Data Fig. 6 Data collection strategy for assessing protein assemblies formed by cPCDHs between liposomes.

    a, Grid view of protein–liposome aggregates (dark shadows) deposited on lacey carbon grids, 300 copper mesh. b, Hole view of the boxed area shown in a. Protein–liposome aggregates can be seen as dark shadows. Tilt-series collection of liposome aggregates over lacey carbon holes in thin ice (orange square). White crosses represent additional data collection sites; the cyan cross represents the focus target. c, Tilt image collected at the region highlighted in b. A single layer of liposomes coated in cPCDH density (black arrowhead), liposomes stacked on top of each other (white arrowhead) and—in addition—thick layers of stacked liposomes (asterisks) are visible in the image. Note that membranes at liposome contact sites appear to be parallel, and cPCDH density appears to be ordered. See Supplementary Video 2 for the reconstructed tomogram.

  7. Extended Data Fig. 7 cPCDH zippers from the γB4EC1–6 crystal structure match the ordered linear arrays observed for γB6EC1–6 on membranes.

    a, Tomographic slice through a reconstructed tomogram of adherent γB6EC1–6-coated liposomes. The region of tomographic slices that is shown in close-up views in c and d is highlighted by an orange box. b, Molecular surface views of the γB4EC1–6 crystal lattice arrangement in three orientations. Each protomer is coloured in a different colour. c, Tomographic slices spanning 143 Å into the depth of the tomogram, one linear array that progresses into the plane of the tomogram is indicated by cyan arrowheads. Grey arrowheads indicate lipid bilayers. d, Crystallographic γB4EC1–6 zipper consisting of five consecutive cis dimers placed into the cryo-ET density of the marked γB6EC1–6 array (cyan arrowheads) observed between membranes. Compare the density and structure fit between c and d. Protomers coloured as in b. Scale bars, 350 Å.

  8. Extended Data Fig. 8 Automated tomogram annotation of cPCDH density and membranes.

    a, Training and annotation of protein density and lipid bilayers. Examples of representative 2D-positive (top two panels) and -negative (bottom) annotations are shown. Left and middle, regions of interest on a tomographic slice (left) and manual annotation (middle) identify positive (white particles on black background) features. Right, output after the training. A representative negative example is shown (bottom), in which no features are annotated by the trained neural network. b, Annotated tomographic slice. cPCDH density is shown in cyan, membranes in yellow. Orange arrowheads indicate single protomers to highlight examples of domain-level resolution of annotation. Scale bars, 350 Å.

  9. Extended Data Table 1 X-ray crystallography data collection and refinement statistics
  10. Extended Data Table 2 Interdomain angles

Supplementary information

  1. Reporting Summary

  2. Video 1 Reconstructed Tomogram of γB6EC1–6 dimer-of-dimers in vitreous ice.

    Ectodomains of γB6EC1-6 adopt ellipsoidal shaped conformations in vitreous ice and individual domains are resolved. Scale bar: 350 Å. Similar results were observed in n=7 independent experiments.

  3. Video 2 Reconstructed Tomogram of wild-type γB6EC1–6 on liposomes reveals ordered density at membrane contact sites.

    Liposome membranes are parallel at contact sites and protein density appears to be ordered in the intermembrane space. Ellipsoidal ‘front’, zipper-like ‘side’ and grid-like ‘top’ views are clearly distinguishable in the tomogram. Compare to tomographic slice shown in Fig. 3c. Scale bar: 100 nm. Ordered assemblies were consistently observed in n=11 independent experiments.

  4. Video 3 Reconstructed Tomogram of an energy-filtered tilt-series of wild-type γB6EC1–6 on liposomes provides domain level resolution of protein density.

    Contiguous assemblies extending through the volume of the tomogram formed by alternating cis and trans interactions. Scale bar: 100 nm. Ordered assembly shown here is representative of n=7 independent experiments of energy-filtered tilt-series collection.

  5. Video 4 Reconstructed Tomogram of an energy-filtered tilt-series of γB6EC1–6 cis-mutant V563D on liposomes reveals loss of ordered assembly.

    While liposomes still adhere to each other and trans dimers can be observed at membrane contact sites, ordered front, side and top views seen in wild-type tomograms are not observed. Scale bar: 100nm. Loss of function was observed in n=4 independent tiltseries.

  6. Video 5 Annotated tomogram reveals cPCDH ectodomains to assemble on membrane surfaces into molecular zippers.

    Linear zippers of the γB4EC1-6 crystal were fit into density maps of a well-resolved region of a reconstructed tomogram of wild-type γB6EC1-6 on liposomes (Supplementary Video 3). Ten independent crystallographic zippers (n=10) were docked into the segmented maps. Pcdh zippers are packed in close proximity at membrane contact sites and propagate in parallel allowing each to extend unimpeded. Scale bar: 350 Å.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark
Fig. 1: Crystal structure of the cPCDH γB4 ectodomain reveals a zipper-like assembly.
Fig. 2: cPCDH γB6 ectodomains in solution assemble as a dimer-of-dimers through cis and trans interfaces.
Fig. 3: cPCDH γB6 forms continuous ordered assemblies between liposome membranes.
Fig. 4: cPCDH γB6 forms extended parallel zipper arrays on membranes, consistent with the chain-termination model.
Extended Data Fig. 1: X-ray diffraction anisotropy and electron density map quality for the low-resolution γB4EC1–6 crystal structure.
Extended Data Fig. 2: Comparison between the γB4EC1–6 crystal structure and cPCDH γB fragment structures reveals that the formation of the zipper assembly does not require large conformational changes.
Extended Data Fig. 3: Particle selection and subtomogram averaging of cPCDH γB6 complexes in solution.
Extended Data Fig. 4: Two-dimensional cryo-electron microscopy of γB6EC1–6 in solution.
Extended Data Fig. 5: Structural comparisons of the dimer-of-dimers model from single-particle cryo-EM with crystallographic cis and trans dimers.
Extended Data Fig. 6: Data collection strategy for assessing protein assemblies formed by cPCDHs between liposomes.
Extended Data Fig. 7: cPCDH zippers from the γB4EC1–6 crystal structure match the ordered linear arrays observed for γB6EC1–6 on membranes.
Extended Data Fig. 8: Automated tomogram annotation of cPCDH density and membranes.

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.