Skip to main content
SpringerLink
Log in

High-Throughput Antibody Structure Modeling and Design Using ABodyBuilder

  • Protocol
  • First Online:
Computational Methods in Protein Evolution

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1851))

Abstract

Antibodies are proteins of the adaptive immune system; they can be designed to bind almost any molecule, and are increasingly being used as biotherapeutics. Experimental antibody design is an expensive and time-consuming process, and computational antibody design methods can now be used to help develop new therapeutics and diagnostics. Within the design pipeline, accurate antibody structure modeling is essential, as it provides the basis for antibody-antigen docking, binding affinity prediction, and estimating thermal stability. Ideally, models should be rapidly generated, allowing the exploration of the breadth of antibody space. This allows methods to replicate the natural processes of antibody diversification (e.g., V(D)J recombination and somatic hypermutation), and cope with large volumes of data that are typical of next-generation sequencing datasets. Here we describe ABodyBuilder and PEARS, algorithms that build and mutate antibody model structures. These methods take ~30 s to generate a model antibody structure.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Georgiou G, Ippolito GC, Beausang J, Busse CE, Wardemann H, Quake SR (2014) The promise and challenge of high-throughput sequencing of the antibody repertoire. Nat Biotechnol 32:158–168

    Article  CAS  Google Scholar 

  2. Dunbar J, Krawczyk K, Leem J, Baker T, Fuchs A, Georges G, Shi J, Deane CM (2014) SAbDab: the structural antibody database. Nucleic Acids Res 42:D1140–D1146

    Article  CAS  Google Scholar 

  3. Chames P, Van Regenmortel M, Weiss E, Baty D (2009) Therapeutic antibodies: successes, limitations and hopes for the future. Br J Pharmacol 157:220–233

    Article  CAS  Google Scholar 

  4. Kuroda D, Shirai H, Jacobson MP, Nakamura H (2012) Computer-aided antibody design. Protein Eng Des Sel 25:507–521

    Article  CAS  Google Scholar 

  5. Reichert JM (2017) Antibodies to watch in 2017. MAbs 9:167–181

    Article  CAS  Google Scholar 

  6. Weiner GJ (2015) Building better monoclonal antibody-based therapeutics. Nat Rev Cancer 15:361–370

    Article  CAS  Google Scholar 

  7. Schroeder HW, Cavacini L (2010) Structure and function of immunoglobulins. J Allergy Clin Immunol 125:41–52

    Article  Google Scholar 

  8. Chothia C, Lesk A (1987) Canonical structures for the hypervariable regions of immunoglobulins. J Mol Biol 196:901–917

    Article  CAS  Google Scholar 

  9. North B, Lehmann A, Dunbrack RL (2011) A new clustering of antibody CDR loop conformations. J Mol Biol 406:228–256

    Article  CAS  Google Scholar 

  10. Nowak J, Baker T, Georges G, Kelm S, Klostermann S, Shi J, Sridharan S, Deane CM (2016) Length-independent structural similarities enrich the antibody CDR canonical class model. MAbs 8:751–760

    Article  CAS  Google Scholar 

  11. Dunbar J, Fuchs A, Shi J, Deane CM (2013) ABangle: Characterising the VH-VL orientation in antibodies. Protein Eng Des Sel 26:611–620

    Article  CAS  Google Scholar 

  12. Foote J, Winter G (1992) Antibody framework residues affecting the conformation of the hypervariable loops. J Mol Biol 224:487–499

    Article  CAS  Google Scholar 

  13. McCafferty J, Griffiths AD, Winter G, Chiswell DJ (1990) Phage antibodies: filamentous phage displaying antibody variable domains. Nature 348:552–554

    Article  CAS  Google Scholar 

  14. Lee E-C, Liang Q, Ali H, Bayliss L, Beasley A, Bloomfield-Gerdes T, Bonoli L, Brown R, Campbell J, Carpenter A, Chalk S, Davis A, England N, Fane-Dremucheva A, Franz B, Germaschewski V, Holmes H, Holmes S, Kirby I, Kosmac M, Legent A, Lui H, Manin A, O'Leary S, Paterson J, Sciarrillo R, Speak A, Spensberger D, Tuffery L, Waddell N, Wang W, Wells S, Wong V, Wood A, Owen MJ, Friedrich GA, Bradley A (2014) Complete humanization of the mouse immunoglobulin loci enables efficient therapeutic antibody discovery. Nat Biotech 32:356–363

    Article  CAS  Google Scholar 

  15. Liu X, Taylor RD, Griffin L, Coker S-F, Adams R, Ceska T, Shi J, Lawson ADG, Baker T (2017) Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping. Sci Rep 7:41306

    Article  CAS  Google Scholar 

  16. Lippow SM, Wittrup KD, Tidor B (2007) Computational design of antibody-affinity improvement beyond in vivo maturation. Nat Biotechnol 25:1171–1176

    Article  CAS  Google Scholar 

  17. Choi Y, Hua C, Sentman CL, Ackerman ME, Bailey-Kellogg C (2015) Antibody humanization by structure-based computational protein design. MAbs 7:1045–1057

    Article  CAS  Google Scholar 

  18. Miklos AE, Kluwe C, Der BS, Pai S, Sircar A, Hughes RA, Berrondo M, Xu J, Codrea V, Buckley PE, Calm AM, Welsh HS, Warner CR, Zacharko MA, Carney JP, Gray JJ, Georgiou G, Kuhlman B, Ellington AD (2012) Structure-based design of supercharged, highly thermoresistant antibodies. Chem Biol 19:449–455

    Article  CAS  Google Scholar 

  19. Olimpieri PP, Marcatili P, Tramontano A (2015) Tabhu: tools for antibody humanization. Bioinformatics 31:434–435

    Article  CAS  Google Scholar 

  20. Lewis SM, Wu X, Pustilnik A, Sereno A, Huang F, Rick HL, Guntas G, Leaver-Fay A, Smith EM, Ho C, Hansen-Estruch C, Chamberlain AK, Truhlar SM, Conner EM, Atwell S, Kuhlman B, Demarest SJ (2014) Generation of bispecific IgG antibodies by structure-based design of an orthogonal Fab interface. Nat Biotechnol 32:191–198

    Article  CAS  Google Scholar 

  21. Dunbar J, Knapp B, Fuchs A, Shi J, Deane CM (2014) Examining variable domain orientations in antigen receptors gives insight into TCR-like antibody design. PLoS Comput Biol 10:1–10

    Article  Google Scholar 

  22. Lapidoth GD, Baran D, Pszolla GM, Norn C, Alon A, Tyka MD, Fleishman SJ (2015) AbDesign: an algorithm for combinatorial backbone design guided by natural conformations and sequences. Proteins 83:1385–1406

    Article  CAS  Google Scholar 

  23. Li T, Pantazes RJ, Maranas CD (2014) OptMAVEn – a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes. PLoS One 9:1–17

    Google Scholar 

  24. Leem J, Dunbar J, Georges G, Shi J, Deane CM (2016) ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation. MAbs 8:1259–1268

    Article  CAS  Google Scholar 

  25. Marcatili P, Olimpieri PP, Chailyan A, Tramontano A (2014) Antibody structural modeling with prediction of immunoglobulin structure (PIGS). Nat Protoc 9:2771–2783

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  27. 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

    Article  CAS  Google Scholar 

  28. Weitzner BD, Jeliazkov JR, Lyskov S, Marze N, Kuroda D, Frick R, Adolf-Bryfogle J, Biswas N, Dunbrack RL Jr, Gray JJ (2017) Modeling and docking of antibody structures with Rosetta. Nat Protoc 12:401–416

    Article  CAS  Google Scholar 

  29. Huang P-S, Boyken SE, Baker D (2016) The coming of age of de novo protein design. Nature 537:320–327

    Article  CAS  Google Scholar 

  30. Khoury GA, Smadbeck J, Kieslich CA, Floudas CA (2014) Protein folding and de novo protein design for biotechnological applications. Trends Biotechnol 32:99–109

    Article  CAS  Google Scholar 

  31. Dunbar J, Deane CM (2016) ANARCI: antigen receptor numbering and receptor classification. Bioinformatics 32:298–300

    CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  33. Krivov GG, Shapovalov MV, Dunbrack RL (2009) Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77:778–795

    Article  CAS  Google Scholar 

  34. Nagata K, Randall A, Baldi P (2012) SIDEpro: a novel machine learning approach for the fast and accurate prediction of side-chain conformations. Proteins 80:142–153

    Article  CAS  Google Scholar 

  35. Almagro JC, Teplyakov A, Luo J, Sweet RW, Kodangattil S, Hernandez-Guzman F, Gilliland GL (2014) Second antibody modeling assessment (AMA-II). Proteins 82:1553–1562

    Article  CAS  Google Scholar 

  36. Choi Y, Deane CM (2011) Predicting antibody complementarity determining region structures without classification. Mol BioSyst 7:3327–3334

    Article  CAS  Google Scholar 

  37. Finn JA, Koehler Leman J, Willis JR, Cisneros A, Crowe JE, Meiler J (2016) Improving loop modeling of the antibody complementarity-determining region 3 using knowledge-based restraints. PLoS One 11:e0154811

    Article  Google Scholar 

  38. Marks C, Nowak J, Klostermann S, Georges G, Dunbar J, Shi J, Kelm S, Deane CM (2017) Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction. Bioinformatics 33:1346–1353

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Messih MA, Lepore R, Marcatili P, Tramontano A (2014) Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies. Bioinformatics 30:2733–2740

    Article  CAS  Google Scholar 

  40. Bujotzek A, Dunbar J, Lipsmeier F, Schäfer W, Antes I, Deane CM, Georges G (2015a) Prediction of VH-VL domain orientation for antibody variable domain modeling. Proteins 83:681–695

    Article  CAS  Google Scholar 

  41. Marze NA, Lyskov S, Gray JJ (2016) Improved prediction of antibody VL-VH orientation. Protein Eng Des Sel 29:409–418

    Article  CAS  Google Scholar 

  42. Yamashita K, Ikeda K, Amada K, Liang S, Tsuchiya Y, Nakamura H, Shirai H, Standley DM (2014) Kotai antibody builder: automated high-resolution structural modeling of antibodies. Bioinformatics 30:3279–3280

    Article  CAS  Google Scholar 

  43. Bujotzek A, Fuchs A, Qu C, Benz J, Klostermann S, Antes I, Georges G (2015b) MoFvAb: modeling the Fv region of antibodies. MAbs 7:838–852

    Article  CAS  Google Scholar 

  44. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242

    Article  CAS  Google Scholar 

  45. Maier JKX, Labute P (2014) Assessment of fully automated antibody homology modeling protocols in molecular operating environment. Proteins 82:1599–1610

    Article  CAS  Google Scholar 

  46. Choi Y, Deane CM (2010) FREAD revisited: accurate loop structure prediction using a database search algorithm. Proteins 78:1431–1440

    CAS  PubMed  Google Scholar 

  47. Deane CM, Blundell TL (2001) CODA: a combined algorithm for predicting the structurally variable regions of protein models. Protein Sci 10:599–612

    Article  CAS  Google Scholar 

  48. Šali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815

    Article  Google Scholar 

  49. Adolf-Bryfogle J, Xu Q, North B, Lehmann A, Dunbrack RL Jr (2015) PyIgClassify: a database of antibody CDR structural classifications. Nucleic Acids Res 43:D432–D438

    Article  CAS  Google Scholar 

  50. Berrondo M, Kaufmann S, Berrondo M (2014) Automated aufbau of antibody structures from given sequences using Macromoltek's SmrtMolAntibody. Proteins 82:1636–1645

    Article  CAS  Google Scholar 

  51. Zhu K, Day T, Warshaviak D, Murrett C, Friesner R, Pearlman D (2014) Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction. Proteins 82:1646–1655

    Article  CAS  Google Scholar 

  52. Jarasch A, Koll H, Regula JT, Bader M, Papadimitriou A, Kettenberger H (2015) Developability assessment during the selection of novel therapeutic antibodies. J Pharm Sci 104:1885–1898

    Article  CAS  Google Scholar 

  53. Shapovalov MV, Dunbrack RL (2011) A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure 19:844–858

    Article  CAS  Google Scholar 

  54. Towse C-L, Rysavy S, Vulovic I, Daggett V (2016) New dynamic rotamer libraries: data-driven analysis of side-chain conformational propensities. Structure 24:187–199

    Article  CAS  Google Scholar 

  55. Lovell SC, Word JM, Richardson JS, Richardson DC (2000) The penultimate rotamer library. Proteins 40:389–408

    Article  CAS  Google Scholar 

  56. Chinea G, Padron G, Hooft RWW, Sander C, Vriend G (1995) The use of position-specific rotamers in model building by homology. Proteins 23:415–421

    Article  CAS  Google Scholar 

  57. Lefranc M-P, Pommié C, Ruiz M, Giudicelli V, Foulquier E, Truong L, Thouvenin-Contet V, Lefranc G (2003) IMGT unique numbering for immunoglobulin and T cell receptor variable domains and Ig superfamily V-like domains. Dev Comp Immunol 27:55–77

    Article  CAS  Google Scholar 

  58. Kabat EA, Wu TT, Bilofsky H, Reid-Miller M, Perry HM (1983) Sequences of proteins of immunological interest, 3rd edn. National Institutes of Health, Bethesda

    Google Scholar 

  59. Lefranc M-P (2014) Immunoglobulin and T cell receptor genes: IMGT and the birth and rise of Immunoinformatics. Front Immunol 5:22

    Article  Google Scholar 

  60. Desmet J, Maeyer MD, Hazes B, Lasters I (1992) The dead-end elimination theorem and its use in protein side-chain positioning. Nature 356:539–542

    Article  CAS  Google Scholar 

  61. Miao Z, Cao Y, Jiang T (2011) RASP: rapid modeling of protein side chain conformations. Bioinformatics 27:3117–3122

    Article  CAS  Google Scholar 

  62. Biasini M (2015) pv: v1.8.1

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistics, University of Oxford, Oxford, UK

    Jinwoo Leem & Charlotte M. Deane

Authors
  1. Jinwoo Leem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charlotte M. Deane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Charlotte M. Deane .

Editor information

Editors and Affiliations

  1. GlaxoSmithKline, Cellzome – a GSK company Meyerhofstrasse 1, Heidelberg, Baden-Württemberg, Germany

    Tobias Sikosek

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Leem, J., Deane, C.M. (2019). High-Throughput Antibody Structure Modeling and Design Using ABodyBuilder. In: Sikosek, T. (eds) Computational Methods in Protein Evolution. Methods in Molecular Biology, vol 1851. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8736-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8736-8_21

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8735-1

  • Online ISBN: 978-1-4939-8736-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation