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Influence of model resolution on geometric simulations of antibody aggregation

Published online by Cambridge University Press:  13 May 2016

Kasra Manavi ,
Bruna Jacobson ,
Brittany Hoard  and
Lydia Tapia
Kasra Manavi
Affiliation:
Department of Computer Science, University of New Mexico, 87131 Albuquerque, New Mexico. Emails: kazaz@cs.unm.edu, bjacobson@cs.unm.edu, bhoard@cs.unm.edu
Bruna Jacobson
Affiliation:
Department of Computer Science, University of New Mexico, 87131 Albuquerque, New Mexico. Emails: kazaz@cs.unm.edu, bjacobson@cs.unm.edu, bhoard@cs.unm.edu
Brittany Hoard
Affiliation:
Department of Computer Science, University of New Mexico, 87131 Albuquerque, New Mexico. Emails: kazaz@cs.unm.edu, bjacobson@cs.unm.edu, bhoard@cs.unm.edu
Lydia Tapia*
Affiliation:
Department of Computer Science, University of New Mexico, 87131 Albuquerque, New Mexico. Emails: kazaz@cs.unm.edu, bjacobson@cs.unm.edu, bhoard@cs.unm.edu
*
*Corresponding author. E-mail: tapia@cs.unm.edu
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Summary

It is estimated that allergies afflict up to 40% of the world's population. A primary mediator for allergies is the aggregation of antigens and IgE antibodies bound to cell-surface receptors, FcεRI. Antibody/antigen aggregate formation causes stimulation of mast cells and basophils, initiating cellular degranulation and releasing immune mediators which produce an allergic or anaphylactic response. Understanding the shape and structure of these aggregates can provide critical insights into the allergic response. We have previously developed methods to geometrically model, simulate and analyze antibody aggregation inspired by rigid body robotic motion simulations. Our technique handles the large size and number of molecules involved in aggregation, providing an advantage over traditional simulations such as molecular dynamics (MD) and coarse-grained energetic models. In this paper, we study the impact of model resolution on simulations of geometric structures using both our previously developed Monte Carlo simulation and a novel application of rule-based modeling. These methods complement each other, the former providing explicit geometric detail and the latter providing a generic representation where multiple resolutions can be captured. Our exploration is focused on two antigens, a man-made antigen with three binding sites, DF3, and a common shrimp allergen (antigen), Pen a 1. We find that impact of resolution is minimal for DF3, a small globular antigen, but has a larger impact on Pen a 1, a rod-shaped molecule. The volume reduction caused by the loss in resolution allows more binding site accessibility, which can be quantified using a rule-based model with implicit geometric input. Clustering analysis of our simulation shows good correlation when compared with available experimental results. Moreover, collisions in all-atom reconstructions are negligible, at around 0.2% at 90% reduction.

Keywords

Antibody AggregationResolution StudyRule-Based Modeling
Type
Articles
Information
Robotica , Volume 34 , Issue 8 , August 2016 , pp. 1754 - 1776
Copyright
Copyright © Cambridge University Press 2016 

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References

1. Al-Bluwi, I. et al., “Modeling protein conformational transitions by a combination of coarse-grained normal mode analysis and robotics-inspired methods,” BMC Struct. Biol. 13 (1), S2 (2013).Google Scholar
2. Andrews, N. L. et al., “Actin restricts FcεRI diffusion and facilitates antigeninduced receptor immobilisation,” Nature Cell Biol. 10 (8), 955–963 (2008).CrossRefGoogle Scholar
3. Andrews, N. L. et al., “Small, mobile FcεRI receptor aggregates are signaling competent,” Immunity 31 (3), 469–479 (2009).Google Scholar
4. Atilgan, A. R. et al., “Anisotropy of fluctuation dynamics of proteins with an elastic network model,” Biophys. J. 80 (1), 505–515 (2001).Google Scholar
5. Ayuso, R., Lehrer, S. B. and Reese, G., “Identification of continuous, allergenic regions of the major shrimp allergen pen a 1 (Tropomyosin),” Int. Arch. Allergy Immun. 127 (1), 27–37 (2002).Google Scholar
6. Baaden, M. and Marrink, S. J., “Coarse-grain modelling of proteinprotein interactions,” Curr. Opin. Struct. Biol. 23 (6), 878–886 (2013).Google Scholar
7. Bahar, I. and Rader, A. J., “Coarse-grained normal mode analysis in structural biology,” Curr. Opin. Struct. Biol. 15 (5), 586–592 (2005).Google Scholar
8. Bayazit, O. B., Song, G. and Amato, N. M., “Ligand Binding with OBPRM and Haptic User Input: Enhancing Automatic Motion Planning with Virtual Touch,” International Conference on Robotics and Automation (ICRA), IEEE, New York, NY (2001) pp. 954–959.Google Scholar
9. Blinov, M. L. et al., “BioNetGen: Software for rule-based modeling of signal transduction based on the interactions of molecular domains,” Bioinformatics 20 (17), 3289–3291 (2004).Google Scholar
10. Bratko, D. et al., “Molecular simulation of protein aggregation,” Biotechnol. Bioeng. 96 (1), 1–8 (2007).CrossRefGoogle ScholarPubMed
11. Cignoni, P., Montani, C. and Scopigno, R., “A comparison of mesh simplification algorithms,” Comput. Graph. 22 (1), 37–54 (1998).Google Scholar
12. Chandler, D. E., Strümpfer, J., Sener, M., Scheuring, S. and Schulten, K., “Light harvesting by lamellar chromatophores in Rhodospirillum photometricum,” Biophysical journal 106 (11), 2503–2510 (2014).Google Scholar
13. Cortes, J. et al., “Simulating ligand-induced conformational changes in proteins using a mechanical disassembly method,” Phys. Chem. Chem. Phys. 12, 8268–8276 (2010).Google Scholar
14. Danos, V. and Laneve, C., “Formal molecular biology,” Theor. Comput. Sci. 325 (1), 69–110 (2004).Google Scholar
15. Day, L. A., Sturtevant, J. M. and Singer, S. J., “The kinetics of the reactions between antibodies to the 2,4 dinitrophenyl group and specific haptens. Ann. New York Acad. Sci. 103 (2), 611–625 (1963). ISSN: .CrossRefGoogle Scholar
16. Espinoza, F. A. et al., “Using hierarchical clustering and dendrograms to quantify the clustering of membrane proteins,” Bull. Math. Biol. 74 (1), 190–211 (2012).CrossRefGoogle ScholarPubMed
17. Gambin, Y. et al., “Lateral mobility of proteins in liquid membranes revisited,” Proc. Natl. Acad. Sci. USA 103 (7), 2098–2102 (2006).Google Scholar
18. Goddard, T. D., Huang, C. C. and Ferrin, T. E., “Software extensions to UCSF chimera for interactive visualization of large molecular assemblies,” Structure 13 (3), 473–482 (2005).Google Scholar
19. Goldstein, B. and Perelson, A. S., “Equilibrium theory for the clustering of bivalent cell surface receptors by trivalent ligands. Application to histamine release from basophils,” Biophys. J. 45 (6), 1109–1123 (1984).Google Scholar
20. Gruenert, G. et al., “Rule-based spatial modeling with diffusing, geometrically constrained molecules,” BMC Bioinform. 11, 307 (2010).Google Scholar
21. Halperin, I. et al., “Principles of docking: An overview of search algorithms and a guide to scoring functions,” Proteins: Struct. Funct. Bioinformat. 47 (4), 409–443 (2002).Google Scholar
22. Hashmi, I. and Shehu, A., “HopDock: A probabilistic search algorithm for decoy sampling in protein-protein docking,” Proteome Sci. 11 (Suppl 1), S6 (2013). ISSN: .CrossRefGoogle ScholarPubMed
23. Hlavacek, W. S., Posner, R. G. and Perelson, A. S., “Steric effects on multivalent ligand-receptor binding: Exclusion of ligand sites by bound cell surface receptors,” Biophys. J. 76 (6), 3031–3043 (1999).Google Scholar
24. Hopkins, B. and Skellam, J. G., “A new method for determining the type of distribution of plant individuals,” Ann. Botany 18 (70), 213–227 (1954).Google Scholar
25. Huang, P.-S., Love, J. J. and Mayo, S. L., “A de novo designed proteinprotein interface,” Protein Sci. 16 (12), 2770–2774 (2007).Google Scholar
26. Huang, Y.-F. et al., “Nanoparticlemediated IgEreceptor aggregation and signaling in RBL mast cells,” J. Am. Chem. Soc. 131 (47), 17328–17334 (2009).Google Scholar
27. Ivanciuc, O., Schein, C. H. and Braun, W., “SDAP: Database and computational tools for allergenic proteins,” Nucleic Acids Res. 31 (1), 359–362 (2003).Google Scholar
28. Jha, R. K. et al., “Computational design of a PAK1 binding protein,” J. Mol. Biol. 400 (2), 257–270 (2010).Google Scholar
29. Kim, Y. C. and Hummer, G., “Coarse-grained models for simulations of multiprotein complexes: Application to ubiquitin binding,” J. Mol. Biol. 375 (5), 1416–1433 (2008).Google Scholar
30. King, N. P. et al., “Computational design of self-assembling protein nanomaterials with atomic level accuracy,” Science 336 (6085), 1171–1174 (2012).Google Scholar
31. Klein, M. L. and Shinoda, W., “Large-scale molecular dynamics simulations of self-assembling systems,” Science 321 (5890), 798–800 (2008).Google Scholar
32. Knight, J. D. et al., “Single molecule diffusion of membrane-bound proteins: Window into lipid contacts and bilayer dynamics,” Biophys. J. 99 (9), 2879–2887 (2010). ISSN: .CrossRefGoogle ScholarPubMed
33. Lai, Y.-T., King, N. P. and Yeates, T. O., “Principles for designing ordered protein assemblies,” Trends Cell Biol. 22 (12), 653–661 (2012).Google Scholar
34. Li, Y. and Huang, Q., “Influence of protein self-association on complex coacervation with polysaccharide: A Monte Carlo study,” J. Phys. Chem. B 117 (9), 2615–2624 (2013).CrossRefGoogle ScholarPubMed
35. Li, Y. et al., “Monte Carlo simulation on complex formation of proteins and polysaccharides,” J. Phys. Chem. B 116 (10), 3045–3053 (2012).CrossRefGoogle ScholarPubMed
36. Lillemeier, B. F. et al., “Plasma membrane-associated proteins are clustered into islands attached to the cytoskeleton,” Proc. Natl. Acad. Sci. 103 (50), 18992–18997 (2006).Google Scholar
37. Lingwood, D. and Simons, K., “Lipid rafts as a membrane-organizing principle,” Science 327 (5961), 46–50 (2010).Google Scholar
38. Mahajan, A. et al., “Optimal aggregation of FcεRI with a structurally defined trivalent ligand overrides negative regulation driven by phosphatases,” ACS Chem. Biol. 9 (7), 1508–1519 (2014).CrossRefGoogle ScholarPubMed
39. Manavi, K., Kuntz, A. and Tapia, L., “Geometrical Insights into the Process of Antibody Aggregation,” AAAI Workshop on Artificial Intelligence and Robotics Methods in Computational Biology (AIRMCB), AAAI Press, Menlo Park, CA (2013) pp. 26–31.Google Scholar
40. Manavi, K., Wilson, B. S. and Tapia, L., “Simulation and Analysis of Antibody Aggregation on Cell Surfaces using Motion Planning and Graph Analysis,” Proceeding of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB), ACM, New York, NY (2012) pp. 458–465.Google Scholar
41. Maus, C., Rybacki, S. and Uhrmacher, A. M., “Rule-based multilevel modeling of cell biological systems,” BMC Syst. Biol. 5, 166 (2011).CrossRefGoogle ScholarPubMed
42. Autodesk Maya (2014) URL: http://www.autodesk.com/.Google Scholar
43. Monine, M. I. et al., “Modeling multivalent ligand-receptor interactions with steric constraints on configurations of cell-surface receptor aggregates,” Biophys. J. 98 (1), 48–56 (2010).CrossRefGoogle ScholarPubMed
44. Nicolau, D. V., Hancock, J. F. and Burrage, K., “Sources of anomalous diffusion on cell membranes: A Monte Carlo study,” Biophys. J. 92 (6), 1975–1987 (2006).Google Scholar
45. Peng, L. X. et al., “Aggregation properties of a polymeric anticancer therapeutic: a coarse-grained modeling study,” J. Chem. Inform. Model. 51 (12), 3030–3035 (2011).Google Scholar
46. Perilla, J. R. et al., “Molecular dynamics simulations of large macromolecular complexes,” Curr. Opin. Struct. Biol. 31, 64–74 (2015).Google Scholar
47. Periole, X. et al., “Combining an elastic network with a coarse-grained molecular force field: Structure, dynamics, and intermolecular recognition,” J. Chem. Theory Comput. 5 (9), 2531–2543 (2009).Google Scholar
48. Pike, L. J., “Rafts defined: A report on the Keystone symposium on lipid rafts and cell function,” J. Lipid Res. 47 (7), 1597–1598 (2006).Google Scholar
49. Posner, R. G. et al., “Simultaneous cross-linking by two nontriggering bivalent ligands causes synergistic signaling of IgE FcεRI complexes,” J. Immunology 155 (7), 3601–3609 (1995).Google Scholar
50. Rahman, N. A. et al., “Rotational dynamics of type I Fc epsilon receptors on individually-selected rat mast cells studied by polarized fluorescence depletion,” Biophys. J. 61 (2), 334–346 (1992).Google Scholar
51. Reese, G. et al., “Reduced allergenic potency of VR9-1, a mutant of the major shrimp allergen Pen a 1 (Tropomyosin),” J. Immunology 175 (12), 8354–8364 (2005).Google Scholar
52. Rivera, J. and Gilfillan, A. M., “Molecular regulation of mast cell activation,” J. Allergy Clin. Immunology 117 (6), 1214–1225 (2006).Google Scholar
53. Saunders, M. G. and Voth, G. A., “Coarse-graining of multiprotein assemblies,” Curr. Opin. Struct. Biol. 22 (2), 144–150 (2012).Google Scholar
54. Schuyler, A. D. and Chirikjian, G. S., “Normal mode analysis of proteins: A comparison of rigid cluster modes with C (alpha) coarse graining,” J. Mol. Graph. Modelling 22 (3), 183–193 (2004).Google Scholar
55. Sil, D. et al., “Trivalent ligands with rigid DNA spacers reveal structural requirements for IgE receptor signaling in RBL mast cells,” ACS Chem. Biol. 2 (10), 674–684 (2007).Google Scholar
56. Smith, A. M. et al., “RuleBender: Integrated modeling, simulation and visualization for rule-based intracellular biochemistry,” BMC Bioinformat. 13 (8), S3 (2012).Google Scholar
57. Wilson, B. S., Oliver, J. M. and Lidke, D. S., “Spatio-temporal signaling in mast cells,” Adv. Exp. Med. Biol. 716, 91–106 (2011).Google Scholar
58. Wolfe, K. C. et al., “Multiscale modeling of double-helical DNA and RNA: A unification through lie groups,” J. Phys. Chem. B 116 (29), 8556–8572 (2012).Google Scholar
59. Xu, K. et al., “Kinetics of multivalent antigen DNP-BSA binding to IgE-FcεRI in relationship to the stimulated tyrosine phosphorylation of FcεRI,” J. Immunology 160 (7), 3225–3235 (1998).Google Scholar
60. Yang, J. et al., “Kinetic Monte Carlo method for rule-based modeling of biochemical networks,” Phys. Rev. E 78 (3), 031910 (2008).Google Scholar
61. Zhang, J. et al., “Characterizing the topography of membrane receptors and signaling molecules from spatial patterns obtained using nanometer-scale electrondense probes and electron microscopy,” Micron 37 (1), 14–34 (2006).Google Scholar
62. Zhang, L., Lu, D. and Liu, Z., “How native proteins aggregate in solution: A dynamic Monte Carlo simulation,” Biophys. Chem. 133, 71–80 (2008).CrossRefGoogle ScholarPubMed
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