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Information-Driven Antibody–Antigen Modelling with HADDOCK

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Computer-Aided Antibody Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2552))

Abstract

In the recent years, therapeutic use of antibodies has seen a huge growth, "due to their inherent proprieties and technological advances in the methods used to study and characterize them. Effective design and engineering of antibodies for therapeutic purposes are heavily dependent on knowledge of the structural principles that regulate antibody–antigen interactions. Several experimental techniques such as X-ray crystallography, cryo-electron microscopy, NMR, or mutagenesis analysis can be applied, but these are usually expensive and time-consuming. Therefore computational approaches like molecular docking may offer a valuable alternative for the characterization of antibody–antigen complexes.

Here we describe a protocol for the prediction of the 3D structure of antibody–antigen complexes using the integrative modelling platform HADDOCK. The protocol consists of (1) the identification of the antibody residues belonging to the hypervariable loops which are known to be crucial for the binding and can be used to guide the docking and (2) the detailed steps to perform docking with the HADDOCK 2.4 webserver following different strategies depending on the availability of information about epitope residues.

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References

  1. Narciso JET, Uy IDC, Cabang AB et al (2011) Analysis of the antibody structure based on high-resolution crystallographic studies. New Biotechnol 28:435–447. https://doi.org/10.1016/j.nbt.2011.03.012

    Article  CAS  Google Scholar 

  2. Novotný J, Bruccoleri R, Newell J et al (1983) Molecular anatomy of the antibody binding site. J Biol Chem 258:14433–14437

    Article  PubMed  Google Scholar 

  3. Sela-Culang I, Kunik V, Ofran Y (2013) The structural basis of antibody-antigen recognition. Front Immunol 4:302. https://doi.org/10.3389/fimmu.2013.00302

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. MacCallum RM, Martin ACR, Thornton JM (1996) Antibody-antigen interactions: contact analysis and binding site topography. J Mol Biol 262:732–745. https://doi.org/10.1006/jmbi.1996.0548

    Article  CAS  PubMed  Google Scholar 

  5. Kaplon H, Reichert JM (2019) Antibodies to watch in 2019. MAbs 11:219–238. https://doi.org/10.1080/19420862.2018.1556465

    Article  CAS  PubMed  Google Scholar 

  6. Morea V, Lesk AM, Tramontano A (2000) Antibody modeling: implications for engineering and design. Methods 20:267–279. https://doi.org/10.1006/meth.1999.0921

    Article  CAS  PubMed  Google Scholar 

  7. Norman RA, Ambrosetti F, Bonvin AMJJ et al (2019) Computational approaches to therapeutic antibody design: established methods and emerging trends. Brief Bioinform 21(5):1549–1567. https://doi.org/10.1093/bib/bbz095

    Article  PubMed Central  Google Scholar 

  8. Moreira IS, Fernandes PA, Ramos MJ (2010) Protein-protein docking dealing with the unknown. J Comput Chem 31:317–342. https://doi.org/10.1002/jcc.21276

    Article  CAS  PubMed  Google Scholar 

  9. Rodrigues JPGLM, Bonvin AMJJ (2014) Integrative computational modeling of protein interactions. FEBS J 281:1988–2003

    Article  CAS  PubMed  Google Scholar 

  10. Kozakov D, Hall DR, Xia B et al (2017) The ClusPro web server for protein-protein docking. Nat Protoc 12:255–278. https://doi.org/10.1038/nprot.2016.169

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Dominguez C, Boelens R, Bonvin AMJJ (2003) HADDOCK: a protein−protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125:1731–1737. https://doi.org/10.1021/ja026939x

    Article  CAS  PubMed  Google Scholar 

  12. Jiménez-García B, Roel-Touris J, Romero-Durana M et al (2018) LightDock: a new multi-scale approach to protein-protein docking. Bioinformatics 34:49–55. https://doi.org/10.1093/bioinformatics/btx555

    Article  CAS  PubMed  Google Scholar 

  13. Chen R, Weng Z (2002) Docking unbound proteins using shape complementarity, desolvation, and electrostatics. Proteins Struct Funct Genet 47:281–294. https://doi.org/10.1002/prot.10092

    Article  CAS  PubMed  Google Scholar 

  14. Ambrosetti F, Jiménez-García B, Roel-Touris J, Bonvin AMJJ (2020) Modeling antibody-antigen complexes by information-driven docking. Structure 28:119–129, e2. https://doi.org/10.1016/j.str.2019.10.011

    Article  CAS  PubMed  Google Scholar 

  15. Melquiond ASJ, Bonvin AMJJ (2010) Data-driven docking: using external information to spark the biomolecular rendez-vous. In: Protein-protein complexes: analysis, modeling and drug design

    Google Scholar 

  16. Karaca E, Bonvin AMJJ (2013) Advances in integrative modeling of biomolecular complexes. Methods 59(3):372–381

    Article  CAS  PubMed  Google Scholar 

  17. Lim XX, Chandramohan A, Lim XYE et al (2017) Epitope and paratope mapping reveals temperature-dependent alterations in the dengue-antibody interface. Structure 25:1391–1402, e3. https://doi.org/10.1016/j.str.2017.07.007

    Article  PubMed  Google Scholar 

  18. Fontayne A, De Maeyer B, De Maeyer M et al (2007) Paratope and epitope mapping of the antithrombotic antibody 6B4 in complex with platelet glycoprotein Ibα. J Biol Chem 282:23517–23524. https://doi.org/10.1074/jbc.M701826200

    Article  CAS  PubMed  Google Scholar 

  19. de Vries SJ, Bonvin AMJJ (2011) CPORT: a consensus Interface predictor and its performance in prediction-driven docking with HADDOCK. PLoS One 6:e17695. https://doi.org/10.1371/journal.pone.0017695

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hopf TA, Schärfe CPI, Rodrigues JPGLM et al (2014) Sequence co-evolution gives 3D contacts and structures of protein complexes. elife 3:e03430. https://doi.org/10.7554/eLife.03430

    Article  PubMed Central  Google Scholar 

  21. Ambrosetti F, Olsen TH, Olimpieri PP et al (2020) proABC-2: PRediction of antibody contacts v2 and its application to information-driven docking. bioRxiv. https://doi.org/10.1101/2020.03.18.967828

  22. Liberis E, Velickovic P, Sormanni P et al (2018) Parapred: antibody paratope prediction using convolutional and recurrent neural networks. Bioinformatics 34:2944–2950. https://doi.org/10.1093/bioinformatics/bty305

    Article  CAS  PubMed  Google Scholar 

  23. Krawczyk K, Baker T, Shi J, Deane CM (2013) Antibody i-patch prediction of the antibody binding site improves rigid local antibody-antigen docking. Protein Eng Des Sel 26:621–629. https://doi.org/10.1093/protein/gzt043

    Article  CAS  PubMed  Google Scholar 

  24. Kunik V, Ashkenazi S, Ofran Y (2012) Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure. Nucleic Acids Res 40:W521–W524. https://doi.org/10.1093/nar/gks480

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sela-Culang I, Ashkenazi S, Peters B, Ofran Y (2015) PEASE: predicting B-cell epitopes utilizing antibody sequence. Bioinformatics 31:1313–1315. https://doi.org/10.1093/bioinformatics/btu790

    Article  PubMed  Google Scholar 

  26. Krawczyk K, Liu X, Baker T et al (2014) Improving B-cell epitope prediction and its application to global antibody-antigen docking. Bioinformatics 30:2288–2294. https://doi.org/10.1093/bioinformatics/btu190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Jespersen MC, Peters B, Nielsen M, Marcatili P (2017) BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res 45:W24–W29. https://doi.org/10.1093/nar/gkx346

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Qi T, Qiu T, Zhang Q et al (2014) SEPPA 2.0—more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen. Nucleic Acids Res 42:W59–W63. https://doi.org/10.1093/nar/gku395

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Liang S, Zheng D, Standley DM et al (2010) EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server results. BMC Bioinformatics 11:381. https://doi.org/10.1186/1471-2105-11-381

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kringelum JV, Lundegaard C, Lund O, Nielsen M (2012) Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol 8:e1002829. https://doi.org/10.1371/journal.pcbi.1002829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Rubinstein ND, Mayrose I, Martz E, Pupko T (2009) Epitopia: a web-server for predicting B-cell epitopes. BMC Bioinformatics 10:287. https://doi.org/10.1186/1471-2105-10-287

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ansari HR, Raghava GP (2010) Identification of conformational B-cell epitopes in an antigen from its primary sequence. Immunome Res 6:6. https://doi.org/10.1186/1745-7580-6-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fernández-Recio J, Totrov M, Abagyan R (2004) Identification of protein-protein interaction sites from docking energy landscapes. J Mol Biol 335(3):843–865. https://doi.org/10.1016/j.jmb.2003.10.069

    Article  CAS  PubMed  Google Scholar 

  34. Rodrigues JPGLM, Trellet M, Schmitz C et al (2012) Clustering biomolecular complexes by residue contacts similarity. Proteins 80:1810–1817. https://doi.org/10.1002/prot.24078

    Article  CAS  PubMed  Google Scholar 

  35. Dunbar J, Deane CM (2016) ANARCI: antigen receptor numbering and receptor classification. Bioinformatics 32:298–300. https://doi.org/10.1093/bioinformatics/btv552

    Article  CAS  PubMed  Google Scholar 

  36. Eddy SR (2011) Accelerated profile HMM searches. PLoS Comput Biol 7(10):e1002195. https://doi.org/10.1371/journal.pcbi.1002195

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Raschka S (2017) BioPandas: working with molecular structures in pandas DataFrames. J Open Source Softw 2(14):279. https://doi.org/10.21105/joss.00279

    Article  Google Scholar 

  38. Rodrigues J, Teixeira JMC, Trellet M, et al (2020) haddocking/pdb-tools: Bug Fix Release. https://doi.org/10.5281/ZENODO.3608327

  39. Berman HM, Battistuz T, Bhat TN et al (2002) The Protein Data Bank. Acta Crystallogr Sect D Biol Crystallogr 58:899–907. https://doi.org/10.1107/S0907444902003451

    Article  CAS  Google Scholar 

  40. Méndez R, Leplae R, De Maria L, Wodak SJ (2003) Assessment of blind predictions of protein-protein interactions: current status of docking methods. Proteins Struct Funct Genet 52:51–67. https://doi.org/10.1002/prot.10393

    Article  CAS  PubMed  Google Scholar 

  41. Davis IW, Leaver-Fay A, Chen VB et al (2007) MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 35(suppl_2):W375–W383. https://doi.org/10.1093/nar/gkm216

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work is supported by the European Union Horizon 2020 BioExcel (grant # 675728 and 823830), EOSC-hub (grant # 777536) and EGI-ACE (grant # 101017567) projects.

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Authors and Affiliations

  1. Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Faculty of Science – Chemistry, Utrecht University, Utrecht, The Netherlands

    Francesco Ambrosetti, Zuzana Jandova & Alexandre M. J. J. Bonvin

Authors
  1. Francesco Ambrosetti
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  2. Zuzana Jandova
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  3. Alexandre M. J. J. Bonvin
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Corresponding author

Correspondence to Alexandre M. J. J. Bonvin .

Editor information

Editors and Affiliations

  1. School of Engineering, The University of Tokyo, Tokyo, Japan

    Kouhei Tsumoto

  2. School of Engineering, The University of Tokyo, Tokyo, Japan

    Daisuke Kuroda

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Ambrosetti, F., Jandova, Z., Bonvin, A.M.J.J. (2023). Information-Driven Antibody–Antigen Modelling with HADDOCK. In: Tsumoto, K., Kuroda, D. (eds) Computer-Aided Antibody Design. Methods in Molecular Biology, vol 2552. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2609-2_14

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  • DOI: https://doi.org/10.1007/978-1-0716-2609-2_14

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2608-5

  • Online ISBN: 978-1-0716-2609-2

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