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Molecular Dynamics Simulations of Immune Receptors and Ligands

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The Immune Synapse

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2654))

Abstract

Molecular dynamics simulations of immune receptor and ligand proteins in their native membrane environment allow to determine the orientational and structural variability of the proteins and protein complexes. The simulations complement the static, “membrane-free” structural information obtained from cryo-EM structures of transmembrane proteins in detergent micelles or from crystal structures of extracellular protein domains. Here we describe how to set up and perform simulations of transmembrane receptors, ligands, and receptor-ligand complexes.

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References

  1. Choudhuri K, Wiseman D, Brown MH, Gould K, van der Merwe PA (2005) T-cell receptor triggering is critically dependent on the dimensions of its peptide-MHC ligand. Nature 436:578–582. https://doi.org/10.1038/nature03843

    Article  CAS  PubMed  Google Scholar 

  2. Milstein O, Tseng S-Y, Starr T, Llodra J, Nans A, Liu M, Wild MK, van der Merwe PA, Stokes DL, Reisner Y, Dustin ML (2008) Nanoscale increases in CD2-CD48-mediated intermembrane spacing decrease adhesion and reorganize the immunological synapse. J Biol Chem 283:34414–34422

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Chang VT, Fernandes RA, Ganzinger KA, Lee SF, Siebold C, McColl J, Jonsson P, Palayret M, Harlos K, Coles CH, Jones EY, Lui Y, Huang E, Gilbert RJC, Klenerman D, Aricescu AR, Davis SJ (2016) Initiation of T cell signaling by CD45 segregation at ‘close contacts’. Nat Immunol 17(5):574–582. https://doi.org/10.1038/ni.3392

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Choudhuri K, Dustin ML (2010) Signaling microdomains in T cells. FEBS Lett 584:4823–4831

    Article  CAS  PubMed  Google Scholar 

  5. Wang R, Natarajan K, Margulies DH (2009) Structural basis of the CD8αβ/MHC class I interaction: focused recognition orients CD8β to a T cell proximal position. J Immunol 183(4):2554–2564. https://doi.org/10.4049/jimmunol.0901276

    Article  CAS  PubMed  Google Scholar 

  6. Pandey PR, Różycki B, Lipowsky R, Weikl TR (2021) Structural variability and concerted motions of the T cell receptor – CD3 complex. elife 10:67195. https://doi.org/10.7554/eLife.67195.sa2

    Article  Google Scholar 

  7. Davis SJ, van der Merwe PA (2006) The kinetic-segregation model: TCR triggering and beyond. Nat Immunol 7:803–809. https://doi.org/10.1038/ni1369

    Article  CAS  PubMed  Google Scholar 

  8. Bachmann MF, Barner M, Kopf M (1999) CD2 sets quantitative thresholds in T cell activation. J Exp Med 190(10):1383–1392. https://doi.org/10.1084/jem.190.10.1383

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Różycki B, Weikl TR (2021) Cooperative stabilization of close-contact zones leads to sensitivity and selectivity in T-cell recognition. Cell 10(5):1023. https://doi.org/10.3390/cells10051023

    Article  CAS  Google Scholar 

  10. Schmid EM, Bakalar MH, Choudhuri K, Weichsel J, Ann HS, Geissler PL, Dustin ML, Fletcher DA (2016) Size-dependent protein segregation at membrane interfaces. Nat Phys 12(7):704

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Case DA, Aktulga HM, Belfon K, Ben-Shalom IY, Berryman JT, Brozell SR, Cerutti DS, Cheatham TE III, Cisneros GA, Cruzeiro VWD, Darden TA, Duke RE, Giambasu G, Gilson MK, Gohlke H, Goetz AW, Harris R, Izadi S, Izmailov SA, Kasavajhala K, Kaymak MC, King E, Kovalenko A, Kurtzman T, Lee TS, LeGrand S, Li P, Lin C, Liu J, Luchko T, Luo R, Machado M, Man V, Manathunga M, Merz KM, Miao Y, Mikhailovskii O, Monard G, Nguyen H, O’Hearn KA, Onufriev A, Pan F, Pantano S, Qi R, Rahnamoun A, Roe DR, Roitberg A, Sagui C, Schott-Verdugo S, Shajan A, Shen J, Simmerling CL, Skrynnikov NR, Smith J, Swails J, Walker RC, Wang J, Wang J, Wei H, Wolf RM, Wu X, Xiong Y, Xue Y, York DM, Zhao S, Kollman PA (2022) Amber 2022. University of California, San Francisco

    Google Scholar 

  12. Brooks BR, Brooks CL III, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner AR, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor RW, Post CB, Pu JZ, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York DM, Karplus M (2009) Charmm: the biomolecular simulation program. J Comput Chem 30(10):1545–1614. https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.21287. https://doi.org/10.1002/jcc.21287

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Abraham MJ, Murtola T, Schulz R, Pall S, Smith JC, Hess B, Lindahl E (2015) Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  14. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26(16):1781–1802. https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.20289. https://doi.org/10.1002/jcc.20289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Eastman P, Friedrichs MS, Chodera JD, Radmer RJ, Bruns CM, Ku JP, Beauchamp KA, Lane TJ, Wang L-P, Shukla D, Tye T, Houston M, Stich T, Klein C, Shirts MR, Pande VS (2013) OpenMM 4: a reusable, extensible, hardware independent library for high performance molecular simulation. J Chem Theory Comput 9(1):461–469. https://doi.org/10.1021/ct300857j. PMID: 23316124

    Article  CAS  PubMed  Google Scholar 

  16. Kutzner C, Pall S, Fechner M, Esztermann A, de Groot BL, Grubmuller H (2019) More bang for your buck: improved use of gpu nodes for gromacs 2018. J Comput Chem 40(27):2418–2431. https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.26011. https://doi.org/10.1002/jcc.26011

    Article  CAS  PubMed  Google Scholar 

  17. Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang L-P, Simmonett AC, Harrigan MP, Stern CD, Wiewiora RP, Brooks BR, Pande VS (2017) OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13(7):1–17. https://doi.org/10.1371/journal.pcbi.1005659

    Article  CAS  Google Scholar 

  18. Robustelli P, Piana S, Shaw DE (2018) Developing a molecular dynamics force field for both folded and disordered protein states. Proc Natl Acad Sci U S A 115(21):4758–4766. https://doi.org/10.1073/pnas.1800690115

    Article  CAS  Google Scholar 

  19. Souza PCT, Alessandri R, Barnoud J, Thallmair S, Faustino I, Grunewald F, Patmanidis I, Abdizadeh H, Bruininks BMH, Wassenaar TA, Kroon PC, Melcr J, Nieto V, Corradi V, Khan HM, Domanski J, Javanainen M, Martinez-Seara H, Reuter N, Best RB, Vattulainen I, Monticelli L, Periole X, Tieleman DP, de Vries AH, Marrink SJ (2021) Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat Methods 18(4):382–388. https://doi.org/10.1038/s41592-021-01098-3

    Article  CAS  PubMed  Google Scholar 

  20. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589. https://doi.org/10.1038/s41586-021-03819-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zheng W, Zhang C, Li Y, Pearce R, Bell EW, Zhang Y (2021) Folding nonhomologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Rep Methods 1(3):100014. https://doi.org/10.1016/j.crmeth.2021.100014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hopf TA, Colwell LJ, Sheridan R, Rost B, Sander C, Marks DS (2012) Three-dimensional structures of membrane proteins from genomic sequencing. Cell 149(7):1607–1621. https://doi.org/10.1016/j.cell.2012.04.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Dong D, Zheng L, Lin J, Zhang B, Zhu Y, Li N, Xie S, Wang Y, Gao N, Huang Z (2019) Structural basis of assembly of the human T cell receptor-CD3 complex. Nature 573(7775):546. https://doi.org/10.1038/s41586-019-1537-0

    Article  CAS  PubMed  Google Scholar 

  24. Susac L, Vuong MT, Thomas C, von Bulow S, O’Brien-Ball C, Santos AM, Fernandes RA, Hummer G, Tampe R, Davis SJ (2022) Structure of a fully assembled tumor-specific T cell receptor ligated by pMHC. Cell 185(17):3201–321319. https://doi.org/10.1016/j.cell.2022.07.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Haselwandter CA, Guo YR, Fu Z, MacKinnon R (2022) Elastic properties and shape of the piezo dome underlying its mechanosensory function. Proc Natl Acad Sci 119(40):10–10732208034119. https://doi.org/10.1073/pnas.2208034119

    Article  CAS  Google Scholar 

  26. Weikl TR (2022) A protein curvature for sensing touch. Proc Natl Acad Sci U S A 119:2214536119

    Article  Google Scholar 

  27. Fiser A, Do RKG, Šali A (2000) Modeling of loops in protein structures. Protein Sci 9(9):1753–1773. https://onlinelibrary.wiley.com/doi/pdf/10.1110/ps.9.9.1753. https://doi.org/10.1110/ps.9.9.1753

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Søndergaard CR, Olsson MHM, Rostkowski M, Jensen JH (2011) Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pKa values. J Chem Theory Comput 7(7):2284–2295. https://doi.org/10.1021/ct200133y

    Article  CAS  PubMed  Google Scholar 

  29. Anandakrishnan R, Aguilar B, Onufriev AV (2012) H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res 40(Web Server issue):537–541. https://doi.org/10.1093/nar/gks375

    Article  CAS  Google Scholar 

  30. Jo S, Kim T, Iyer VG, Im W (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29(11):1859–1865. https://doi.org/10.1002/jcc.20945

    Article  CAS  PubMed  Google Scholar 

  31. Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, Wei S, Buckner J, Jeong JC, Qi Y, Jo S, Pande VS, Case DA, Brooks CL, MacKerell AD, Klauda JB, Im W (2016) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput 12(1):405–413. https://doi.org/10.1021/acs.jctc.5b00935. PMID: 26631602

    Article  CAS  PubMed  Google Scholar 

  32. Lomize MA, Lomize AL, Pogozheva ID, Mosberg HI (2006) OPM: orientations of proteins in membranes database. Bioinformatics 22(5):623–625. https://academic.oup.com/bioinformatics/articlepdf/22/5/623/537412/btk023.pdf. https://doi.org/10.1093/bioinformatics/btk023

    Article  CAS  PubMed  Google Scholar 

  33. Jo S, Kim T, Im W (2007) Automated builder and database of protein/membrane complexes for molecular dynamics simulations. PLoS One 2(9):1–9. https://doi.org/10.1371/journal.pone.0000880

    Article  CAS  Google Scholar 

  34. Hopkins CW, Le Grand S, Walker RC, Roitberg AE (2015) Long-time-step molecular dynamics through hydrogen mass repartitioning. J Chem Theory Comput 11(4):1864–1874. https://doi.org/10.1021/ct5010406

    Article  CAS  PubMed  Google Scholar 

  35. Salomon-Ferrer R, Goetz AW, Poole D, Le Grand S, Walker RC (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J Chem Theory Comput 9(9):3878–3888. https://doi.org/10.1021/ct400314y

    Article  CAS  PubMed  Google Scholar 

  36. Plattner N, Doerr S, De Fabritiis G, Noe F (2017) Complete protein-protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nat Chem 9(10):1005–1011

    Article  CAS  PubMed  Google Scholar 

  37. Chakrabarti KS, Olsson S, Pratihar S, Giller K, Overkamp K, Lee KO, Gapsys V, Ryu K-S, de Groot BL, Noe F, Becker S, Lee D, Weikl TR, Griesinger C (2022) A litmus test for classifying recognition mechanisms of transiently binding proteins. Nat Commun 13(1):3792. https://doi.org/10.1038/s41467-022-31374-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Hu J, Lipowsky R, Weikl TR (2013) Binding constants of membrane-anchored receptors and ligands depend strongly on the nanoscale roughness of membranes. Proc Natl Acad Sci U S A 110(38):15283–15288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hu J, Xu G-K, Lipowsky R, Weikl TR (2015) Binding kinetics of membrane-anchored receptors and ligands: molecular dynamics simulations and theory. J Chem Phys 143:243137

    Article  PubMed  Google Scholar 

  40. Xu G-K, Hu J, Lipowsky R, Weikl TR (2015) Binding constants of membrane-anchored receptors and ligands: a general theory corroborated Monte Carlo simulations. J Chem Phys 143:243136

    Article  PubMed  Google Scholar 

  41. Steinkuhler J, Rozycki B, Alvey C, Lipowsky R, Weikl TR, Dimova R, Discher DE (2019) Membrane fluctuations and acidosis regulate cooperative binding of ‘marker of self’ protein CD47 with the macrophage checkpoint receptor SIRPα. J Cell Sci 132:216770

    Google Scholar 

  42. Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC, Buch R, Fiorin G, Henin J, Jiang W, McGreevy R, Melo MCR, Radak BK, Skeel RD, Singharoy A, Wang Y, Roux B, Aksimentiev A, Luthey-Schulten Z, Kale LV, Schulten K, Chipot C, Tajkhorshid E (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 153(4):044130. https://doi.org/10.1063/5.0014475

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kim YC, Hummer G (2008) Coarse-grained models for simulations of multiprotein complexes: application to ubiquitin binding. J Mol Biol 375(5):1416–1433

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

B.R. acknowledges the supported from the National Science Center of Poland via grant no 2021/40/Q/NZ1/00017.

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Authors and Affiliations

  1. Max Planck Institute of Colloids and Interfaces, Potsdam, Germany

    Prithvi R. Pandey & Thomas R. Weikl

  2. Institute of Physics of the Polish Academy of Sciences, Warszawa, Poland

    Bartosz Rózycki

Authors
  1. Prithvi R. Pandey
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  2. Bartosz Rózycki
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  3. Thomas R. Weikl
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Corresponding author

Correspondence to Thomas R. Weikl .

Editor information

Editors and Affiliations

  1. Department of Life Sciences, University of Siena, Siena, Italy

    Cosima T. Baldari

  2. Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK

    Michael L. Dustin

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Pandey, P.R., Rózycki, B., Weikl, T.R. (2023). Molecular Dynamics Simulations of Immune Receptors and Ligands. In: Baldari, C.T., Dustin, M.L. (eds) The Immune Synapse. Methods in Molecular Biology, vol 2654. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3135-5_4

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  • DOI: https://doi.org/10.1007/978-1-0716-3135-5_4

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3134-8

  • Online ISBN: 978-1-0716-3135-5

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