Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

The ClusPro AbEMap web server for the prediction of antibody epitopes

Nature Protocols volume 18, pages 1814–1840 (2023)Cite this article

Subjects

Abstract

Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein–protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody–antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server’s capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45–90 min depending on the size of the proteins.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Outline of the AbEMap protocol using an antigen structure and an antibody sequence as inputs, examples of complex structures generated by PIPER, four examples of results and comparisons to other servers.
Fig. 2: Epitope-mapping performance of four different servers tested on 28 unbound-unbound antibody–antigen complexes in the benchmark set BM5.
Fig. 3: Examples of AbEMap’s applications.
Fig. 4: Comparing AbEMap’s performance on X-ray and homology-modeled antibodies as inputs for 21 new antibody–antigen targets in the benchmark set BM5.5 with EpiPred’s predictions based on X-ray structure inputs alone.
Fig. 5: AbeMap initial job submit page.
Fig. 6: AbeMap job submit page after selecting the ‘Use PDB’ option for the antibody.
Fig. 7: AbEMap status page.
Fig. 8: AbeMap job results page.
Fig. 9: AbeMap job results scores page.
Fig. 10: Results of epitope mapping for two lysozyme-antibody complexes using each of the three epitope-mapping modes in AbEMap (X-ray structure, model or sequence based).
Fig. 11: Mapping the epitopes for complexes 2W9E and 3MXW.

Similar content being viewed by others

Data availability

Data for all cases tested in the benchmark set used can be found in the following figshare link: https://doi.org/10.6084/m9.figshare.c.6295842.v1. Detailed results and direct links for the server results are shown in Anticipated results.

Code availability

AbeMap is available as a server at https://abemap.cluspro.org/ free of charge for non-commercial applications. The server can be used without registration, but in that case, the results will be publicly accessible. The advantage of registering is that the job does not show up on the website, but this option is available only to users with educational or governmental email addresses. The server provides options to view the results online, but protein visualization tools allow for more convenient analyses. We use and recommend PyMOL, which was used to demonstrate the analysis of results in this protocol.

References

  1. Montgomery, R. A., Cozzi, E., West, L. J. & Warren, D. S. Humoral immunity and antibody-mediated rejection in solid organ transplantation. Semin. Immunol. 23, 224–234 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Sela-Culang, I., Kunik, V. & Ofran, Y. The structural basis of antibody-antigen recognition. Front. Immunol. 4, 302 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Danilov, S. M. et al. Fine epitope mapping of monoclonal antibody 5F1 reveals anticatalytic activity toward the N domain of human angiotensin-converting enzyme. Biochemistry 46, 9019–9031 (2007).

    Article  CAS  PubMed  Google Scholar 

  4. Sela-Culang, I. et al. Using a combined computational-experimental approach to predict antibody-specific B cell epitopes. Structure 22, 646–657 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. Ehrhardt, S. A. et al. Polyclonal and convergent antibody response to Ebola virus vaccine rVSV-ZEBOV. Nat. Med. 25, 1589–1600 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Goldstein, L. D. et al. Massively parallel single-cell B-cell receptor sequencing enables rapid discovery of diverse antigen-reactive antibodies. Commun. Biol. 2, 304 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Horns, F., Dekker, C. L. & Quake, S. R. Memory B cell activation, broad anti-influenza antibodies, and bystander activation revealed by single-cell transcriptomics. Cell Rep. 30, 905–913.e6 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kozlova, E. E. G. et al. Computational B-cell epitope identification and production of neutralizing murine antibodies against Atroxlysin-I. Sci. Rep. 8, 14904 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hua, C. K. et al. Computationally-driven identification of antibody epitopes. Elife 6, e29023 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Qi, T. et al. 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 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sun, J. et al. SEPPA: a computational server for spatial epitope prediction of protein antigens. Nucleic Acids Res. 37, W612–W616 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhou, C. et al. SEPPA 3.0-enhanced spatial epitope prediction enabling glycoprotein antigens. Nucleic Acids Res. 47, W388–W394 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sweredoski, M. J. & Baldi, P. PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure. Bioinformatics 24, 1459–1460 (2008).

    Article  CAS  PubMed  Google Scholar 

  14. Rubinstein, N. D., Mayrose, I., Martz, E. & Pupko, T. Epitopia: a web-server for predicting B-cell epitopes. BMC Bioinforma. 10, 287 (2009).

    Article  Google Scholar 

  15. Kulkarni-Kale, U., Bhosle, S. & Kolaskar, A. S. CEP: a conformational epitope prediction server. Nucleic Acids Res. 33, W168–W171 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hopp, T. P. & Woods, K. R. Prediction of protein antigenic determinants from amino acid sequences. Proc. Natl Acad. Sci. USA 78, 3824–3828 (1981).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jespersen, M. C., Peters, B., Nielsen, M. & Marcatili, P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 45, W24–W29 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Potocnakova, L., Bhide, M. & Pulzova, L. B. An introduction to B-cell epitope mapping and in silico epitope prediction. J. Immunol. Res. 2016, 6760830 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Holmes, M. A., Buss, T. N. & Foote, J. Conformational correction mechanisms aiding antigen recognition by a humanized antibody. J. Exp. Med. 187, 479–485 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Li, Y., Li, H., Smith-Gill, S. J. & Mariuzza, R. A. Three-dimensional structures of the free and antigen-bound Fab from monoclonal antilysozyme antibody HyHEL-63. Biochemistry 39, 6296–6309 (2000).

    Article  CAS  PubMed  Google Scholar 

  21. Stanfield, R. L., Dooley, H., Verdino, P., Flajnik, M. F. & Wilson, I. A. Maturation of shark single-domain (IgNAR) antibodies: evidence for induced-fit binding. J. Mol. Biol. 367, 358–372 (2007).

    Article  CAS  PubMed  Google Scholar 

  22. Braden, B. C. et al. Three-dimensional structures of the free and the antigen-complexed Fab from monoclonal anti-lysozyme antibody D44.1. J. Mol. Biol. 243, 767–781 (1994).

    Article  CAS  PubMed  Google Scholar 

  23. Halperin, I., Ma, B., Wolfson, H. & Nussinov, R. Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 47, 409–443 (2002).

    Article  CAS  PubMed  Google Scholar 

  24. Comeau, S. R., Gatchell, D. W., Vajda, S. & Camacho, C. J. ClusPro: a fully automated algorithm for protein-protein docking. Nucleic Acids Res. 32, W96–W99 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Comeau, S. R., Gatchell, D. W., Vajda, S. & Camacho, C. J. ClusPro: an automated docking and discrimination method for the prediction of protein complexes. Bioinformatics 20, 45–50 (2004).

    Article  CAS  PubMed  Google Scholar 

  26. Kozakov, D. et al. The ClusPro web server for protein-protein docking. Nat. Protoc. 12, 255–278 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kozakov, D., Brenke, R., Comeau, S. R. & Vajda, S. PIPER: an FFT-based protein docking program with pairwise potentials. Proteins 65, 392–406 (2006).

    Article  CAS  PubMed  Google Scholar 

  28. Brenke, R. et al. Application of asymmetric statistical potentials to antibody-protein docking. Bioinformatics 28, 2608–2614 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Guest, J. D. et al. An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants. Structure 29, 606–621.e5 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Krawczyk, K., Liu, X., Baker, T., Shi, J. & Deane, C. M. Improving B-cell epitope prediction and its application to global antibody-antigen docking. Bioinformatics 30, 2288–2294 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Krawczyk, K., Baker, T., Shi, J. & Deane, C. M. Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking. Protein Eng. Des. Sel. 26, 621–629 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Sikora, M. et al. Computational epitope map of SARS-CoV-2 spike protein. PLoS Comput. Biol. 17, e1008790 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Marks, C. & Deane, C. M. How repertoire data are changing antibody science. J. Biol. Chem. 295, 9823–9837 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Vajda, S., Porter, K. A. & Kozakov, D. Progress toward improved understanding of antibody maturation. Curr. Opin. Struct. Biol. 67, 226–231 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Porter, K. A. et al. Template-based modeling by ClusPro in CASP13 and the potential for using co-evolutionary information in docking. Proteins 87, 1241–1248 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Padhorny, D. et al. Protein-protein docking by fast generalized Fourier transforms on 5D rotational manifolds. Proc. Natl Acad. Sci. USA 113, E4286–E4293 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ngan, C. H. et al. FTSite: high accuracy detection of ligand binding sites on unbound protein structures. Bioinformatics 28, 286–287 (2012).

    Article  CAS  PubMed  Google Scholar 

  38. Desta, I. T. et al. Mapping of antibody epitopes based on docking and homology modeling. Proteins 91, 171–182 (2023).

    Article  CAS  PubMed  Google Scholar 

  39. Jumper, J. et al. Applying and improving AlphaFold at CASP14. Proteins 89, 1711–1721 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at https://www.biorxiv.org/content/10.1101/2021.10.04.463034v2 (2021).

  43. Ghani, U. et al. Improved docking of protein models by a combination of Alphafold2 and ClusPro. Preprint at https://www.biorxiv.org/content/10.1101/2021.09.07.459290v1 (2021).

  44. Ko, J. & Lee, J. Can AlphaFold2 predict protein-peptide complex structures accurately? Preprint at https://www.biorxiv.org/content/10.1101/2021.07.27.453972v1.full (2021).

  45. Mirdita, M., Ovchinnikov, S. & Steinegger, M. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Desta, I. T., Porter, K. A., Xia, B., Kozakov, D. & Vajda, S. Performance and its limits in rigid body protein-protein docking. Structure 28, 1071–1081.e3 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Prot. Sci. 86, 2.9.1–2.9.37 (2016).

    Google Scholar 

  48. Katchalski-Katzir, E. et al. Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc. Natl Acad. Sci. USA 89, 2195–2199 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Lindemann, S. R., Yershova, A. & LaValle, S. M. Incremental grid sampling strategies in robotics. In Algorithmic Foundations of Robotics VI (eds Erdmann, M., Overmars, M., Hsu, D., & van der Stappen, F.) 313–328 (Springer Berlin, Heidelberg, 2005).

  50. Chuang, G. Y., Kozakov, D., Brenke, R., Comeau, S. R. & Vajda, S. DARS (Decoys As the Reference State) potentials for protein-protein docking. Biophys. J. 95, 4217–4227 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lee, B. & Richards, F. M. The interpretation of protein structures: estimation of static accessibility. J. Mol. Biol. 55, 379–400 (1971).

    Article  CAS  PubMed  Google Scholar 

  52. Vreven, T. et al. Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2. J. Mol. Biol. 427, 3031–3041 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Fox, N. K., Brenner, S. E. & Chandonia, J. M. SCOPe: structural classification of proteins—extended, integrating SCOP and ASTRAL data and classification of new structures. Nucleic Acids Res. 42, D304–D309 (2014).

    Article  CAS  PubMed  Google Scholar 

  54. Akbar, R. et al. A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding. Cell Rep. 34, 108856 (2021).

    Article  CAS  PubMed  Google Scholar 

  55. Salamanca Viloria, J., Allega, M. F., Lambrughi, M. & Papaleo, E. An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass. Sci. Rep. 7, 2838 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Stave, J. W. & Lindpaintner, K. Antibody and antigen contact residues define epitope and paratope size and structure. J. Immunol. 191, 1428–1435 (2013).

    Article  CAS  PubMed  Google Scholar 

  57. Pittala, S. & Bailey-Kellogg, C. Learning context-aware structural representations to predict antigen and antibody binding interfaces. Bioinformatics 36, 3996–4003 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Sivasubramanian, A., Sircar, A., Chaudhury, S. & Gray, J. J. Toward high-resolution homology modeling of antibody Fv regions and application to antibody-antigen docking. Proteins 74, 497–514 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Padhorny, D. et al. ClusPro in rounds 38 to 45 of CAPRI: toward combining template-based methods with free docking. Proteins 88, 1082–1090 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Weitzner, B. D. et al. Modeling and docking of antibody structures with Rosetta. Nat. Protoc. 12, 401–416 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lepore, R., Olimpieri, P. P., Messih, M. A. & Tramontano, A. PIGSPro: prediction of immunoGlobulin structures v2. Nucleic Acids Res. 45, W17–W23 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Klausen, M. S., Anderson, M. V., Jespersen, M. C., Nielsen, M. & Marcatili, P. LYRA, a webserver for lymphocyte receptor structural modeling. Nucleic Acids Res. 43, W349–W355 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Schritt, D. et al. Repertoire Builder: high-throughput structural modeling of B and T cell receptors. Mol. Syst. Des. Eng. 4, 761–768 (2019).

    Article  CAS  Google Scholar 

  64. Karami, Y. et al. DaReUS-Loop: a web server to model multiple loops in homology models. Nucleic Acids Res. 47, W423–W428 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Dunbar, J. et al. SAbPred: a structure-based antibody prediction server. Nucleic Acids Res. 44, W474–W478 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Marks, C. & Deane, C. M. Antibody H3 structure prediction. Comput. Struct. Biotechnol. J. 15, 222–231 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Lensink, M. F. et al. Blind prediction of homo- and hetero-protein complexes: the CASP13-CAPRI experiment. Proteins 87, 1200–1221 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ruffolo, J. A., Guerra, C., Mahajan, S. P., Sulam, J. & Gray, J. J. Geometric potentials from deep learning improve prediction of CDR H3 loop structures. Bioinformatics 36, i268–i275 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Jespersen, M. C., Mahajan, S., Peters, B., Nielsen, M. & Marcatili, P. Antibody specific B-Cell epitope predictions: leveraging information from antibody-antigen protein complexes. Front. Immunol. 10, 298 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Antonyuk, S. V. et al. Crystal structure of human prion protein bound to a therapeutic antibody. Proc. Natl Acad. Sci. USA 106, 2554–2558 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Maun, H. R. et al. Hedgehog pathway antagonist 5E1 binds hedgehog at the pseudo-active site. J. Biol. Chem. 285, 26570–26580 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This investigation was supported by grants DBI 1759277 and AF 1645512 from the National Science Foundation and R35GM118078, R21GM127952 and RM1135136 from the National Institute of General Medical Sciences.

Author information

Author notes

  1. These authors contributed equally: Israel T. Desta, Sergei Kotelnikov.

Authors and Affiliations

  1. Department of Biomedical Engineering, Boston University, Boston, MA, USA

    Israel T. Desta, Usman Ghani, Dmitri Beglov & Sandor Vajda

  2. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA

    Sergei Kotelnikov, George Jones & Dima Kozakov

  3. Innopolis University, Innopolis, Russia

    Mikhail Abyzov & Yaroslav Kholodov

  4. Department of Genome Informatics, Osaka University, Osaka, Japan

    Daron M. Standley

  5. Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan

    Daron M. Standley

Authors
  1. Israel T. Desta
    View author publications

    Search author on:PubMed Google Scholar

  2. Sergei Kotelnikov
    View author publications

    Search author on:PubMed Google Scholar

  3. George Jones
    View author publications

    Search author on:PubMed Google Scholar

  4. Usman Ghani
    View author publications

    Search author on:PubMed Google Scholar

  5. Mikhail Abyzov
    View author publications

    Search author on:PubMed Google Scholar

  6. Yaroslav Kholodov
    View author publications

    Search author on:PubMed Google Scholar

  7. Daron M. Standley
    View author publications

    Search author on:PubMed Google Scholar

  8. Dmitri Beglov
    View author publications

    Search author on:PubMed Google Scholar

  9. Sandor Vajda
    View author publications

    Search author on:PubMed Google Scholar

  10. Dima Kozakov
    View author publications

    Search author on:PubMed Google Scholar

Contributions

I.T.D., S.K., D.K., D.B. and Y.K. developed the methodology. I.T.D., S.K. and G.J. developed the server. I.T.D., U.G., M.A. and D.B. performed the experiments. I.T.D., S.K. and S.V. prepared the manuscript. D.K. and D.M.S. reviewed the paper.

Corresponding authors

Correspondence to Sandor Vajda or Dima Kozakov.

Ethics declarations

Competing interests

The PIPER docking program, on which the ClusPro AbEMap server is based, has been licensed by Boston University to Acpharis Inc. Acpharis, in turn, offers commercial sublicenses of PIPER. D.K. and S.V. consult for Acpharis and own stock in the company, and D.B. is the acting CEO of the company. However, both the ClusPro and AbEMap servers are free for non-commercial use.

Peer review

Peer review information

Nature Protocols thanks Pritam Kumar Panda, Mohammad Mostafa Pourseif and Bruno Villoutreix for their contribution to the peer review of this work.

Additional information

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

Related links

Key references using this protocol

Desta, I. T. et al. Proteins 91, 171–182 (2023): https://doi.org/10.1002/prot.26420

Porter, K. A. et al. Proteins 87, 1241–1248 (2019): https://doi.org/10.1002/prot.25808

Kozakov, D. et al. Nat. Protoc. 12, 255–278 (2017): https://doi.org/10.1038/nprot.2016.169

Padhorny, D. et al. Proc. Natl Acad. Sci. USA 113, E4286–E4293 (2016): https://doi.org/10.1073/pnas.1603929113

Ngan, C. H. et al. Bioinformatics 28, 286–287 (2012): https://doi.org/10.1093/bioinformatics/btr651

Key data used in this protocol

Desta, I. T. et al. Proteins 91, 171–182 (2023): https://doi.org/10.1002/prot.26420

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Tables 1–3

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Desta, I.T., Kotelnikov, S., Jones, G. et al. The ClusPro AbEMap web server for the prediction of antibody epitopes. Nat Protoc 18, 1814–1840 (2023). https://doi.org/10.1038/s41596-023-00826-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41596-023-00826-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing