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Cited by 2Protein Structure and DynamicsT-cell and B-cell ImmunologyLipid Membrane Structure and Behavior

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Dynamics and activation of membrane-bound B cell receptor assembly

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Do, Hung N., Zhao, Mingfei, Alam, S. Munir, Gnanakaran, S.·2025·Communications Biology
DOI 10.1038/s42003-025-07478-1PMC PMC11825855

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Abstract

B-cell receptor (BCR) complexes are expressed on the surface of a B-cell and are critical in antigen recognition and modulating the adaptive immune response. Even though the relevance of antibodies has been known for almost a hundred years, the antigen-dependent activation mechanism of B-cells has remained elusive. Several models have been proposed for BCR activation, including cross-linking, conformation-induced oligomerization, and dissociation activation models. Recently, the first cryo-EM structures of the human B-cell antigen receptor of the IgM and IgG isotypes have been published that validates the asymmetric organization of the BCR complex. Here, we carry out extensive molecular dynamics simulations to probe the conformational changes upon antigen binding and the influence of the membrane lipids. We identify two critical dynamical events that could be associated with antigen-dependent activation of BCR. First, antigen binding causes increased flexibility in regions distal to the antigen binding site. Second, antigen binding alters the rearrangement of IgM transmembrane helices, including the relative interaction of Igα/Igβ that mediates intracellular signaling. Furthermore, these transmembrane rearrangements lead to changes in localized lipid composition. Our work indirectly supports the conformational-change induced models of BCR activation and contributes to the understanding of the antigen-dependent activation mechanism of BCRs.

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Fig. 1 Simulation systems of the model B cell receptor (BCR) complexes. a The CH31 BCR in the complex membrane. b The CH31 BCR bound by a monomer of HIV-1 envelope protein (HIV-1 Env) in the complex membrane. The Fab heavy chains are colored red, Fab light chains are colored blue, Fc domains are colored cyan, transmembrane helices of BCR are colored green, Igα is colored magenta, Igβ is colored brown, and the HIV-1 Env is colored gray. The phosphatidylcholine (POPC) molecules are colored light blue, phosphatidylethanolamine (POPE) are colored light orange, palmitoyl sphingomyelin (PSM) are colored light pink, diacylglycerols (DAGL) are colored pale green, cholesterols (CHOL) are colored wheat, and ceramides (CER3) are colored pale cyan.
Fig. 1 Simulation systems of the model B cell receptor (BCR) complexes. a The CH31 BCR in the complex membrane. b The CH31 BCR bound by a monomer of HIV-1 envelope protein (HIV-1 Env) in the complex membrane. The Fab heavy chains are colored red, Fab light chains are colored blue, Fc domains are colored cyan, transmembrane helices of BCR are colored green, Igα is colored magenta, Igβ is colored brown, and the HIV-1 Env is colored gray. The phosphatidylcholine (POPC) molecules are colored light blue, phosphatidylethanolamine (POPE) are colored light orange, palmitoyl sphingomyelin (PSM) are colored light pink, diacylglycerols (DAGL) are colored pale green, cholesterols (CHOL) are colored wheat, and ceramides (CER3) are colored pale cyan.CC BY-NC-ND · PubMed Central
Fig. 2 Changes in the system flexibility as measured by the changes in root-mean- square fluctuations (∆RMSF) upon antigen binding to the BCR. The important domains that showed significant dynamic changes upon antigen binding in the BCR in (a) are zoomed in (b, c). A color scale of blue (-3.0)—white (0.0)—red (3.0) is used to show the magnitudes of ∆RMSFs. The HIV-1 Env is colored gray.
Fig. 2 Changes in the system flexibility as measured by the changes in root-mean- square fluctuations (∆RMSF) upon antigen binding to the BCR. The important domains that showed significant dynamic changes upon antigen binding in the BCR in (a) are zoomed in (b, c). A color scale of blue (-3.0)—white (0.0)—red (3.0) is used to show the magnitudes of ∆RMSFs. The HIV-1 Env is colored gray.CC BY-NC-ND · PubMed Central
Fig. 3 Effects of antigen binding to the BCR on the global tilt angles of the transmembrane helices (including TM1, TM2, and the Igα/ Igβ heterodimer) normal to membrane (z-axis) as calculated from the MD simulations of the BCR complexes. a The global tilt angle was defined as the angle formed by the transmembrane helices and the z- axis (which was perpendicular to the membrane plane). b The averages and standard deviations of the global tilt angles of the transmembrane helices calculated from the MD simulations of the BCR complexes. The bars for the BCR without and with antigen bound are colored blue and orange, respectively. The error bars represent the standard deviations. The p-values were calculated using the unpaired student’s t-test with the numbers of samples being the numbers of simulation replicas (N = 5) and shown on top of the bar graphs. The average tilt angles for each simulation replica are included as red and green dots for the BCR without and with antigen bound, respectively. c The distributions of the global tilt angles of the transmembrane helices calculated from the MD simulations of the BCR complexes shown as boxplots. The antigen was found to mostly interact with the second IgM subunit (containing TM2, marked by *).
Fig. 3 Effects of antigen binding to the BCR on the global tilt angles of the transmembrane helices (including TM1, TM2, and the Igα/ Igβ heterodimer) normal to membrane (z-axis) as calculated from the MD simulations of the BCR complexes. a The global tilt angle was defined as the angle formed by the transmembrane helices and the z- axis (which was perpendicular to the membrane plane). b The averages and standard deviations of the global tilt angles of the transmembrane helices calculated from the MD simulations of the BCR complexes. The bars for the BCR without and with antigen bound are colored blue and orange, respectively. The error bars represent the standard deviations. The p-values were calculated using the unpaired student’s t-test with the numbers of samples being the numbers of simulation replicas (N = 5) and shown on top of the bar graphs. The average tilt angles for each simulation replica are included as red and green dots for the BCR without and with antigen bound, respectively. c The distributions of the global tilt angles of the transmembrane helices calculated from the MD simulations of the BCR complexes shown as boxplots. The antigen was found to mostly interact with the second IgM subunit (containing TM2, marked by *).CC BY-NC-ND · PubMed Central
Fig. 4 Changes in residue contacts between Igα and Igβ upon binding of the antigen to the membrane bound BCR. a Changes in residue contact frequencies between Igα and Igβ upon HIV-1 Env binding to the BCR. A contact definition of ≤8.0 Å distance between Cα atoms was used. A color scale of blue (-0.2)—white (0)—red (0.2) was used to show the magnitudes of changes in residue contact frequencies. b Representative reduced contacts upon antigen binding in the BCR. c Representative increased contacts upon antigen binding in the BCR. The structural residue contacts colored blue were those that showed decreases in contact frequencies upon antigen binding, whereas those colored red showed increased in contact frequencies upon antigen binding.
Fig. 4 Changes in residue contacts between Igα and Igβ upon binding of the antigen to the membrane bound BCR. a Changes in residue contact frequencies between Igα and Igβ upon HIV-1 Env binding to the BCR. A contact definition of ≤8.0 Å distance between Cα atoms was used. A color scale of blue (-0.2)—white (0)—red (0.2) was used to show the magnitudes of changes in residue contact frequencies. b Representative reduced contacts upon antigen binding in the BCR. c Representative increased contacts upon antigen binding in the BCR. The structural residue contacts colored blue were those that showed decreases in contact frequencies upon antigen binding, whereas those colored red showed increased in contact frequencies upon antigen binding.CC BY-NC-ND · PubMed Central
Fig. 5 Reorganization of the local lipid environments upon antigen binding to the BCR in the complex membrane. a All lipid molecules within 4Å distance of the membrane- peripheral region and membrane-bound helices of the BCR are considered. The numbers of surrounding lipid molecules during the last 100 ns of the AA (b) and CG MD simulations (c) in the complex membrane are shown. The bars for the BCR without and with antigen bound are colored blue and orange, respectively. The error bars represent the standard deviations. The p-values were calculated using the unpaired student’s t-test with the numbers of samples being the numbers of simulation replicas (N = 5) and shown on top of the bar graphs. The average numbers of surrounding lipid molecules for each simulation replica are included as red and green dots for the BCR without and with antigen bound, respectively.
Fig. 5 Reorganization of the local lipid environments upon antigen binding to the BCR in the complex membrane. a All lipid molecules within 4Å distance of the membrane- peripheral region and membrane-bound helices of the BCR are considered. The numbers of surrounding lipid molecules during the last 100 ns of the AA (b) and CG MD simulations (c) in the complex membrane are shown. The bars for the BCR without and with antigen bound are colored blue and orange, respectively. The error bars represent the standard deviations. The p-values were calculated using the unpaired student’s t-test with the numbers of samples being the numbers of simulation replicas (N = 5) and shown on top of the bar graphs. The average numbers of surrounding lipid molecules for each simulation replica are included as red and green dots for the BCR without and with antigen bound, respectively.CC BY-NC-ND · PubMed Central

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Research works printed but not linked: 59

  1. Robinson, R. Distinct B Cell Receptor Functions Are Determined by Phosphorylation. PLOS Biol. 4, e231 (2006). · DOI
  2. Harwood, N. E. & Batista, F. D. Early events in B cell activation. Annu. Rev. Immunol. 28, 185–210 (2010). · DOI
  3. Su, Q. et al. Cryo-EM structure of the human IgM B cell receptor. Science 377, 875–880 (2022). · DOI
  4. Treanor, B. B-cell receptor: from resting state to activate. Immunology 136, 21–27 (2012). · DOI
  5. Shelby, S. A., Castello-Serrano, I., Wisser, K. C., Levental, I. & Veatch, S. L. Membrane phase separation drives responsive assembly of receptor signaling domains. Nat. Chem. Biol. 19, 750–758 (2023). · DOI
  6. Harwood, N. E. & Batista, F. D. New insights into the early molecular events underlying B cell activation. Immunity 28, 609–619 (2008). · DOI
  7. Tolar, P. & Pierce, S. K. Unveiling the B cell receptor structure. Science 377, 819–820 (2022). · DOI
  8. Thyagarajan, R., Arunkumar, N. & Song, W. Polyvalent Antigens Stabilize B Cell Antigen Receptor Surface Signaling Microdomains. J. Immunol. 170, 6099–6106 (2003). · DOI
  9. Minguet, S., Dopfer, E.-P. & Schamel, W. W. A. Low-valency, but not monovalent, antigens trigger the B-cell antigen receptor (BCR). Int. Immunol. 22, 205–212 (2010). · DOI
  10. Kim, Y.-M. et al. Monovalent ligation of the B cell receptor induces receptor activation but fails to promote antigen presentation. PNAS 103, 3327–3332 (2006). · DOI
  11. Reth, M., Wienands, J. & Schamel, W. W. An unsolved problem of the clonal selection theory and the model of an oligomeric B-cell antigen receptor. Immunological Rev. 176, 10–18 (2000). · DOI
  12. Tolar, P. & Pierce, S. K. A conformation-induced oligomerization model for B cell receptor microclustering and signaling. Curr. Top. Microbiol. Immunol. 340, 155–169 (2010).
  13. Yang, J. & Reth, M. Oligomeric organization of the B-cell antigen receptor on resting cells. Nature 467, 465–469 (2010). · DOI
  14. Yang, J. & Reth, M. The dissociation activation model of B cell antigen receptor triggering. FEBS Lett. 584, 4872–4877 (2010). · DOI
  15. Yang, J. & Reth, M. Receptor Dissociation and B-Cell Activation. Curr. Top. Microbiol. Immunol. 393, 27–43 (2016).
  16. Huse, K. et al. Mechanism of CD79A and CD79B Support for IgM+ B Cell Fitness through B Cell Receptor Surface Expression. J. Immunol. 209, 2042–2053 (2022). · DOI
  17. Schamel, W. W. & Reth, M. Monomeric and oligomeric complexes of the B cell antigen receptor. Immunity 13, 5–14 (2000). · DOI
  18. Tolar, P., Sohn, H. W. & Pierce, S. K. The initiation of antigen-induced B cell antigen receptor signaling viewed in living cells by fluorescence resonance energy transfer. Nat. Immunol. 6, 1168–1176 (2005). · DOI
  19. Dong, Y. et al. Structural principles of B cell antigen receptor assembly. Nature 612, 156–161 (2022). · DOI · Paper page
  20. Bonsignori, M. et al. Two Distinct Broadly Neutralizing Antibody Specificities of Different Clonal Lineages in a Single HIV-1-Infected Donor: Implications for Vaccine Design. J. Virol. 86, 4688–4692 (2012). · DOI
  21. Bonsignori, M. et al. Inference of the HIV-1 VRC01 Antibody Lineage Unmutated Common Ancestor Reveals Alternative Pathways to Overcome a Key Glycan Barrier. Immunity 49, 1162–1174.e1168 (2018). · DOI
  22. Kwon, H.-Y. et al. Lipid-Oriented Live-Cell Distinction of B and T Lymphocytes. J. Am. Chem. Soc. 143, 5836–5844 (2021). · DOI
  23. Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018). · DOI
  24. Liu, Q. et al. Improvement of antibody functionality by structure-guided paratope engraftment. Nat. Commun. 10, 721 (2019). · DOI
  25. Yan, Y., Tao, H., He, J. & Huang, S.-Y. The HDOCK server for integrated protein–protein docking. Nat. Protoc. 15, 1829–1852 (2020). · DOI
  26. Bansal, M., Kumar, S. & Velavan, R. HELANAL: a program to characterize helix geometry in proteins. J. Biomolecular Struct. Dyn. 17, 811–819 (2000). · DOI
  27. Michaud-Agrawal, N., Denning, E. J., Woolf, T. B. & Beckstein, O. MDAnalysis: A toolkit for the analysis of molecular dynamics simulations. J. Computational Chem. 32, 2319–2327 (2011). · DOI
  28. Gowers, R. J. et al. MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations. Proceedings of the 15th Python in Science Conference, 98-105 (2016). · DOI
  29. Reth, M. Discovering immunoreceptor coupling and organization motifs. Front. Immunol. 14, 1253412 (2023). · DOI
  30. Hossain, M. A. et al. B cells expressing IgM B cell receptors of HIV-1 neutralizing antibodies discriminate antigen affinities by sensing binding association rates. Cell Rep. 39, 111021 (2022). · DOI
  31. Veneziano, R. et al. Role of nanoscale antigen organization on B-cell activation probed using DNA origami. Nat. Nanotechnol. 15, 716–723 (2020). · DOI
  32. Kläsener, K., Maity, P. C., Hobeika, E., Yang, J. & Reth, M. B cell activation involves nanoscale receptor reorganizations and inside-out signaling by Syk. eLife 3, e02069 (2014). · DOI
  33. Smith, P. & Lorenz, C. D. LiPyphilic: A Python Toolkit for the Analysis of Lipid Membrane Simulations. J. Chem. Theory Comput. 17, 5907–5919 (2021). · DOI
  34. Chavent, M. et al. Methodologies for the analysis of instantaneous lipid diffusion in MD simulations of large membrane systems. Faraday Discuss. 169, 455–475 (2014). · DOI
  35. Ingólfsson, H. I. et al. The power of coarse graining in biomolecular simulations. WIREs Computational Mol. Sci. 4, 225–248 (2013). · DOI
  36. Alessandri, R. et al. Pitfalls of the Martini Model. J. Chem. Theory Comput. 15, 5448–5460 (2019). · DOI
  37. Marrink, S. J. et al. Computational Modeling of Realistic Cell Membranes. Chem. Rev. 119, 6184–6226 (2019). · DOI
  38. Consortium, T. U. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 51, D523–D531 (2023). · DOI · Paper page
  39. Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2022). · DOI · Paper page
  40. Jo, S., Kim, T., Iyer, V. & Im, W. CHARMM-GUI: A Web-based Graphical User Interface for CHARMM. J. Computational Chem. 29, 1859–1865 (2008). · DOI · Paper page
  41. Wu, E. et al. CHARMM-GUI Membrane Builder Toward Realistic Biological Membrane Simulations. J. Computational Chem. 35, 1997–2004 (2014). · DOI
  42. Jo, S., Lim, J., Klauda, J. & Im, W. CHARMM-GUI Membrane Builder for Mixed Bilayers and Its Application to Yeast Membranes. Biophysical J. 97, 50–58 (2009). · DOI
  43. Lee, J. et al. CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations using the CHARMM36 Additive Force Field. J. Chem. Theory Comput. 12, 405–413 (2016). · DOI
  44. Huang, J. et al. CHARMM36m: an improved force field for folded and intrinscially disordered proteins. Nat. Methods 14, 71–73 (2017). · DOI
  45. Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: A linear constraint solver for molecular simulations. J. Computational Chem. 18, 1463–1472 (1998). · DOI
  46. Essmann, U. et al. A Smooth Particle Mesh Ewald Method. J. Chem. Phys. 103 (1995). · DOI
  47. Grubmuller, H., Heller, H., Windemuth, A. & Schulten, K. Generalized Verlet Algorithm for Efficient Molecular Dynamics Simulations with Long-Range Interactions. Mol. Simul. 6, 121–142 (1991). · DOI
  48. Evans, D. J. & Holian, B. L. The Nose-Hoover thermostat. J. Chem. Phys. 83, 4069–4074 (1985). · DOI
  49. Parrinello, M. & Rahman, A. Polymorphic Transitions in Single Crystals: A New Molecular Dynamics Method. J. Appl. Phys. 52, 7182–7190 (1981). · DOI
  50. Kroon, P. C. et al. Martinize2 and Vermouth: Unified Framework for Topology Generation. eLife (2023). https://doi.org/10.7554/eLife.90627.1 · DOI
  51. Wassenaar, T. A., Pluhackova, K., Böckmann, R. A., Marrink, S. J. & Tieleman, D. P. Going Backward: A Flexible Geometric Approach to Reverse Transformation from Coarse Grained to Atomistic Models. J. Chem. Theory Comput. 10, 676–690 (2014). · DOI
  52. Herzog, F. A., Braun, L., Schoen, I. & Vogel, V. Improved Side Chain Dynamics in MARTINI Simulations of Protein–Lipid Interfaces. J. Chem. Theory Comput. 12, 2446–2458 (2016). · DOI
  53. Periole, X., Cavalli, M., Marrink, S.-J. & Ceruso, M. A. Combining an Elastic Network With a Coarse-Grained Molecular Force Field: Structure, Dynamics, and Intermolecular Recognition. J. Chem. Theory Comput. 5 https://doi.org/10.1021/ct9002114 (2009). · DOI
  54. Wassenaar, T. A., Ingolfsson, H. I., Bockmann, R. A., Tieleman, D. P. & Marrink, S. J. Computational Lipidomics with insane: A Versatile Tool for Generating Custom Membranes for Molecular Simulations. J. Chem. Theory Comput. 11, 2144–2155 (2015). · DOI · Paper page
  55. Tironi, I. G., Sperb, R., Smith, P. E. & Gunsteren, W. F. A generalized reaction field method for molecular dynamics simulations. J. Chem. Phys. 102, 5451–5459 (1995). · DOI
  56. Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126, 014101 (2007). · DOI
  57. Bernetti, M. & Bussi, G. Pressure control using stochastic cell rescaling. J. Chem. Phys. 153, 114107 (2020). · DOI
  58. Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1-2, 19–25 (2015). · DOI · Paper page
  59. Roe, D. R. & Cheatham, I. T. E. PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 9, 3084–3095 (2013). · DOI

Metadata

Authors
Do, H. N., Zhao, M., Alam, S. M., & Gnanakaran, S.
Affiliations
  • Los Alamos National Laboratory (US)
  • Duke University (US)
Corresponding author
Gnanakaran, S.
ORCID
Venue
Communications Biology
Publisher
Springer Science and Business Media LLC
Published
2025-02-13
Volume
8
Issue
1
Pages
226-226
ISSN
2399-3642 (ISSN-L)
DOI
10.1038/s42003-025-07478-1
PMC
PMC11825855
OpenAlex
W4407535508
Cited by
2
References
3
Citations by year
2026: 1
FWCI
0.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Communications Biology (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, cc-by-nc-nd)
  • PubMed Central (submittedVersion, cc-by-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (U54-AI170752-01)
  • DOE | National Nuclear Security Administration (89233218CNA000001)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Lipid Membrane Structure and Behavior
Keywords
B cell receptor, antigen binding, conformational change, IgM, IgG, membrane lipids, intracellular signaling, molecular dynamics simulation

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