Modeling and docking of antibody structures with Rosetta

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Abstract

We describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the VL–VH relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody–antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody–antigen docking. Tasks can be completed in under a day by using public supercomputers.

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Figure 1: A schematic of the modeling protocols (full flowcharts for Rosetta Antibody and Rosetta SnugDock are available in the original publications).
Figure 2: Example output of plot_LHOC.

Change history

  • 14 December 2017

    In the version of this article initially published, Box 4 was omitted. The corrected article now includes Box 4. This error has been corrected in the HTML and PDF versions of the article.

References

  1. 1

    Georgiou, G. et al. The promise and challenge of high-throughput sequencing of the antibody repertoire. Nat. Biotechnol. 32, 158–168 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2

    Reichert, J.M. Antibodies to watch in 2016. MAbs 8, 197–204 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  3. 3

    Correia, B.E. et al. Proof of principle for epitope-focused vaccine design. Nature 507, 201–6 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4

    Al-Lazikani, B., Lesk, A.M. & Chothia, C. Standard conformations for the canonical structures of immunoglobulins. J. Mol. Biol. 273, 927–948 (1997).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  5. 5

    Weitzner, B.D., Kuroda, D., Marze, N., Xu, J. & Gray, J.J. Blind prediction performance of RosettaAntibody 3.0: grafting, relaxation, kinematic loop modeling, and full CDR optimization. Proteins 82, 1611–1623 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6

    Almagro, J.C. et al. Second antibody modeling assessment (AMA-II). Proteins 82, 1553–1562 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  7. 7

    Bujotzek, A. et al. Prediction of VH-VL domain orientation for antibody variable domain modeling. Proteins 83, 681–695 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. 8

    Sircar, A. & Gray, J.J. SnugDock: paratope structural optimization during antibody-antigen docking compensates for errors in antibody homology models. PLoS Comput. Biol. 6, e1000644 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. 9

    Alzari, P.M., Lascombe, M.B. & Poljak, R.J. Three-dimensional structure of antibodies. Annu. Rev. Immunol. 6, 555–580 (1988).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  10. 10

    Kunik, V. & Ofran, Y. The indistinguishability of epitopes from protein surface is explained by the distinct binding preferences of each of the six antigen-binding loops. Protein Eng. Des. Sel. 26, 599–609 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. 11

    Ponomarenko, J.V. & Bourne, P.E. Antibody-protein interactions: benchmark datasets and prediction tools evaluation. BMC Struct. Biol. 7, 64 (2007).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. 12

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

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  13. 13

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14

    Chen, R., Li, L. & Weng, Z. ZDOCK: an initial-stage protein-docking algorithm. Proteins 52, 80–87 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  15. 15

    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).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  16. 16

    Sircar, A., Chaudhury, S., Kilambi, K.P., Berrondo, M. & Gray, J.J. A generalized approach to sampling backbone conformations with RosettaDock for CAPRI rounds 13-19. Proteins 78, 3115–3123 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17

    Méndez, R., Leplae, R., Lensink, M.F. & Wodak, S.J. Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures. Proteins 60, 150–169 (2005).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  18. 18

    O'Meara, M.J. & Leaver-Fay, A. et al. Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta. J. Chem. Theory Comput. 11, 609–622 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19

    Coutsias, E.A., Seok, C., Jacobson, M.P. & Dill, K.A. A kinematic view of loop closure. J. Comput. Chem. 25, 510–528 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  20. 20

    Mandell, D.J., Coutsias, E.A. & Kortemme, T. Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling. Nat. Methods 6, 551–552 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21

    Stein, A. & Kortemme, T. Improvements to robotics-inspired conformational sampling in Rosetta. PLoS One 8, e63090 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22

    Raveh, B., London, N. & Schueler-Furman, O. Sub-angstrom modeling of complexes between flexible peptides and globular proteins. Proteins 78, 2029–2040 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    London, N., Raveh, B., Cohen, E., Fathi, G. & Schueler-Furman, O. Rosetta FlexPepDock web server - high resolution modeling of peptide-protein interactions. Nucleic Acids Res. 39, W249–W253 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24

    Meiler, J. & Baker, D. ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility. Proteins 65, 538–548 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  25. 25

    Johnson, G. & Wu, T.T. Kabat database and its applications: 30 years after the first variability plot. Nucleic Acids Res. 28, 214–8 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26

    Nowak, J. et al. Length-independent structural similarities enrich the antibody CDR canonical class model. MAbs 8, 751–60 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27

    Marze, N.A., Lyskov, S. & Gray, J.J. Improved prediction of antibody VL–VH orientation. Protein Eng. Des. Sel. 29, 409–418 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28

    Canutescu, A.A. & Dunbrack, R.L. Cyclic coordinate descent: a robotics algorithm for protein loop closure. Protein Sci. 12, 963–972 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29

    Wang, C., Bradley, P. & Baker, D. Protein–protein docking with backbone flexibility. J. Mol. Biol. 373, 503–519 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. 30

    Bradley, P., Misura, K.M.S. & Baker, D. Toward high-resolution de Novo structure prediction for small proteins. Science 309, 1868–1871 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  31. 31

    Misura, K.M.S. & Baker, D. Progress and challenges in high-resolution refinement of protein structure models. Proteins 59, 15–29 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. 32

    Weitzner, B.D. et al. The origin of CDR H3 structural diversity. Structure 23, 302–311 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33

    Weitzner, B.D. & Gray, J.J. Accurate structure prediction of CDR H3 loops enabled by a novel structure-based C-terminal constraint. J. Immunol. 198, 505–515 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  34. 34

    Gray, J.J. et al. Protein–protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J. Mol. Biol. 331, 281–299 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  35. 35

    Chaudhury, S. & Gray, J.J. Conformer selection and induced fit in flexible backbone protein–protein docking using computational and NMR ensembles. J. Mol. Biol. 381, 1068–1087 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36

    Kuroda, D. & Gray, J.J. Shape complementarity and hydrogen bond preferences in protein-protein interfaces: implications for antibody modeling and protein-protein docking. Bioinformatics 32, 2451–2456 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37

    Nivon, L.G., Moretti, R. & Baker, D. A Pareto-optimal refinement method for protein design scaffolds. PLoS One 8, e59004 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38

    Sivasubramanian, A., Chao, G., Pressler, H.M., Wittrup, K.D. & Gray, J.J. Structural model of the mAb 806-EGFR complex using computational docking followed by computational and experimental mutagenesis. Structure 14, 401–414 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  39. 39

    Simonelli, L. et al. Rapid structural characterization of human antibody-antigen complexes through experimentally validated computational docking. J. Mol. Biol. 396, 1491–1507 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  40. 40

    Blech, M. et al. Molecular structure of human GM-CSF in complex with a disease-associated anti-human GM-CSF autoantibody and its potential biological implications. Biochem. J. 447, 205–215 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 41

    Thornburg, N.J. et al. Human antibodies that neutralize respiratory droplet transmissible H5N1 infuenza viruses. J. Clin. Invest. 123, 4405–4409 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42

    Ó Conchúir, S. et al. A web resource for standardized benchmark datasets, metrics, and rosetta protocols for macromolecular modeling and design. PLoS One 10, e0130433 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  43. 43

    Zemlin, M. et al. Expressed murine and human CDR-H3 intervals of equal length exhibit distinct repertoires that differ in their amino acid composition and predicted range of structures. J. Mol. Biol. 334, 733–749 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  44. 44

    Sela-Culang, I., Alon, S. & Ofran, Y. A systematic comparison of free and bound antibodies reveals binding-related conformational changes. J. Immunol. 189, 4890–4899 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  45. 45

    Kuroda, D. & Gray, J.J. Pushing the backbone in protein-protein docking. Structure 24, 1821–1829 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46

    Yamashita, K. et al. Kotai antibody builder: automated high-resolution structural modeling of antibodies. Bioinformatics 30, 3279–3280 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47

    Shirai, H. et al. High-resolution modeling of antibody structures by a combination of bioinformatics, expert knowledge, and molecular simulations. Proteins 82, 1624–1635 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. 48

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

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  49. 49

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50

    Schrödinger, L. The PyMOL Molecular Graphics System. https://www.pymol.org/ (2015).

  51. 51

    Pettersen, E.F. et al. UCSF chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    CAS  Article  Google Scholar 

  52. 52

    Chen, V.B., Davis, I.W. & Richardson, D.C. KiNG (Kinemage, Next Generation): a versatile interactive molecular and scientific visualization program. Protein Sci. 18, 2403–2409 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53

    Lyskov, S. et al. Serverification of molecular modeling applications: the Rosetta online server that includes everyone (ROSIE). PLoS One 8, e63906 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54

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

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  55. 55

    Adolf-Bryfogle, J., Xu, Q., North, B., Lehmann, A. & Dunbrack, R.L. PyIgClassify: a database of antibody CDR structural classifications. Nucleic Acids Res. 43, D432–8 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  56. 56

    Abhinandan, K.R. & Martin, A.C.R. Analysis and improvements to Kabat and structurally correct numbering of antibody variable domains. Mol. Immunol. 45, 3832–3839 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  57. 57

    Honegger, A. & Plückthun, A. Yet another numbering scheme for immunoglobulin variable domains: an automatic modeling and analysis tool. J. Mol. Biol. 309, 657–70 (2001).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  58. 58

    Lefranc, M.-P. et al. IMGT unique numbering for immunoglobulin and T cell receptor variable domains and Ig superfamily V-like domains. Dev. Comp. Immunol. 27, 55–77 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  59. 59

    Kabat, E.A., Wu, T.Te., Foeller, C., Perry, H.M. & Gottesman, K.S. Sequences of Proteins of Immunological Interest (National Institutes of Health, 1991).

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Acknowledgements

The authors thank A. Sivasubramanian, A. Sircar and S. Chaudhury for their development of the original RosettaAntibody, SnugDock and EnsembleDock methods. J. Xu refactored the antibody code. We also thank the members of the RosettaCommons for the continued development of the Rosetta Software Suite. ROSIE simulations were carried out, in part, within the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant no. ACI-1053575. B.D.W., N.M., J.R.J., R.L.D. and J.J.G. are supported by National Institutes of Health grant no. R01 GM078221. S.L. is supported by National Institutes of Health grant no. R01 GM73151. D.K. is supported by the DARPA Antibody Technology Program (grant no. HR-0011-10-1-0052) and the Japan Society for the Promotion of Science (grant no. 15H06606). R.F. is supported by the South-Eastern Norway Regional Health Authority (grant no. 850703-6051-39788). J.A.-B. and R.L.D. are supported by National Institutes of Health grant nos. R01 GM111819 and R01 GM084453.

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Contributions

B.D.W., N.M., S.L., D.K., J.R.J., J.A.-B., R.L.D. and J.J.G. developed the current version of RosettaAntibody. S.L. developed ROSIE and implemented the RosettaAntibody and SnugDock server applications. B.D.W. implemented SnugDock in Rosetta 3, and J.R.J. benchmarked SnugDock's performance. R.F. and N.B. wrote the procedure, codified the manual intervention steps developed by B.D.W., N.M. and D.K., and recorded timing information. B.D.W., J.R.J., N.M., S.L., D.K., R.F., J.A.-B., N.B., R.L.D. and J.J.G. wrote the manuscript.

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Correspondence to Jeffrey J Gray.

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The authors declare no competing financial interests.

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Weitzner, B., Jeliazkov, J., Lyskov, S. et al. Modeling and docking of antibody structures with Rosetta. Nat Protoc 12, 401–416 (2017). https://doi.org/10.1038/nprot.2016.180

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