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.

  • Article
  • Published:

Computational optimization of antibody humanness and stability by systematic energy-based ranking

Nature Biomedical Engineering volume 8, pages 30–44 (2024)Cite this article

Subjects

Abstract

Conventional methods for humanizing animal-derived antibodies involve grafting their complementarity-determining regions onto homologous human framework regions. However, this process can substantially lower antibody stability and antigen-binding affinity, and requires iterative mutational fine-tuning to recover the original antibody properties. Here we report a computational method for the systematic grafting of animal complementarity-determining regions onto thousands of human frameworks. The method, which we named CUMAb (for computational human antibody design; available at http://CUMAb.weizmann.ac.il), starts from an experimental or model antibody structure and uses Rosetta atomistic simulations to select designs by energy and structural integrity. CUMAb-designed humanized versions of five antibodies exhibited similar affinities to those of the parental animal antibodies, with some designs showing marked improvement in stability. We also show that (1) non-homologous frameworks are often preferred to highest-homology frameworks, and (2) several CUMAb designs that differ by dozens of mutations and that use different human frameworks are functionally equivalent.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Key steps in energy-based antibody humanization using CUMAb.
Fig. 2: Humanization of an anti-QSOX1 antibody.
Fig. 3: Humanization of an anti-human AXL antibody based on an AlphaFold model structure.
Fig. 4: Humanization of two anti-mucin16 antibodies based on model structures.
Fig. 5: SDR grafting of an anti-HEWL antibody.
Fig. 6: Structural determinants of successful humanization by non-homologous frameworks.

Similar content being viewed by others

Data availability

All data generated and analysed during the study are available within the paper and its Supplementary Information, except for human antibody germline sequences, which were taken from the IMGT reference database (https://www.imgt.org/vquest/refseqh.html), and sequences for FDA-approved humanized antibodies, which were taken from the TheraSAbDab database (http://opig.stats.ox.ac.uk/webapps/newsabdab/therasabdab/search). The crystal structure of hαQSOX1.4 is available through the Protein Data Bank (PDB; https://www.rcsb.org), with accession ID 8AON. The crystal structures of mαQSOX1 and mαHEWL are available through the PDB, with accession IDs 4IJ3 and 1MLC, respectively. Source data are provided with this paper.

Code availability

RosettaScripts59 xml files for running CUMAb and code for generating humanized designs can be found at https://github.com/Fleishman-Lab/CUMAb, with detailed explanations.

References

  1. Raybould, M. I. J. et al. Thera-SAbDab: the therapeutic structural antibody database. Nucleic Acids Res. 48, D383–D388 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Schroff, R. W., Foon, K. A., Beatty, S. M., Oldham, R. K. & Morgan, A. C. Jr Human anti-murine immunoglobulin responses in patients receiving monoclonal antibody therapy. Cancer Res. 45, 879–885 (1985).

  3. Shawler, D. L., Bartholomew, R. M., Smith, L. M. & Dillman, R. O. Human immune response to multiple injections of murine monoclonal IgG. J. Immunol. 135, 1530–1535 (1985).

    Article  CAS  PubMed  Google Scholar 

  4. Hwang, W. Y. K. & Foote, J. Immunogenicity of engineered antibodies. Methods 36, 3–10 (2005).

    Article  CAS  PubMed  Google Scholar 

  5. Kuramochi, T., Igawa, T., Tsunoda, H. & Hattori, K. in Human Monoclonal Antibodies: Methods and Protocols (ed. Steinitz, M.) 213–230 (Springer, 2019).

  6. Saldanha, J. W., Martin, A. C. & Léger, O. J. A single backmutation in the human kIV framework of a previously unsuccessfully humanized antibody restores the binding activity and increases the secretion in cos cells. Mol. Immunol. 36, 709–719 (1999).

    Article  CAS  PubMed  Google Scholar 

  7. Jones, P. T., Dear, P. H., Foote, J., Neuberger, M. S. & Winter, G. Replacing the complementarity-determining regions in a human antibody with those from a mouse. Nature 321, 522–525 (1986).

    Article  CAS  PubMed  Google Scholar 

  8. Baca, M., Presta, L. G., O’Connor, S. J. & Wells, J. A. Antibody humanization using monovalent phage display. J. Biol. Chem. 272, 10678–10684 (1997).

    Article  CAS  PubMed  Google Scholar 

  9. Lazar, G. A., Desjarlais, J. R., Jacinto, J., Karki, S. & Hammond, P. W. A molecular immunology approach to antibody humanization and functional optimization. Mol. Immunol. 44, 1986–1998 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Padlan, E. A. A possible procedure for reducing the immunogenicity of antibody variable domains while preserving their ligand-binding properties. Mol. Immunol. 28, 489–498 (1991).

    Article  CAS  PubMed  Google Scholar 

  11. Choi, Y., Hua, C., Sentman, C. L., Ackerman, M. E. & Bailey-Kellogg, C. Antibody humanization by structure-based computational protein design. MAbs 7, 1045–1057 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kipriyanov, S. M., Moldenhauer, G., Martin, A. C., Kupriyanova, O. A. & Little, M. Two amino acid mutations in an anti-human CD3 single chain Fv antibody fragment that affect the yield on bacterial secretion but not the affinity. Protein Eng. 10, 445–453 (1997).

    Article  CAS  PubMed  Google Scholar 

  13. Safdari, Y., Farajnia, S., Asgharzadeh, M. & Khalili, M. Antibody humanization methods—a review and update. Biotechnol. Genet. Eng. Rev. 29, 175–186 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. Warszawski, S. et al. Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces. PLoS Comput. Biol. 15, e1007207 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Shire, S. J., Shahrokh, Z. & Liu, J. Challenges in the development of high protein concentration formulations. J. Pharm. Sci. 93, 1390–1402 (2004).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  17. Makabe, K. et al. Thermodynamic consequences of mutations in vernier zone residues of a humanized anti-human epidermal growth factor receptor murine antibody, 528. J. Biol. Chem. 283, 1156–1166 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Janeway, C. A., Travers, P., Walport, M. & Shlomchik, M. J. Immunobiology: The Immune System in Health and Disease (Garland Science, 2005).

  19. Giudicelli, V., Chaume, D. & Lefranc, M.-P. IMGT/GENE-DB: a comprehensive database for human and mouse immunoglobulin and T cell receptor genes. Nucleic Acids Res. 33, D256–D261 (2005).

    Article  CAS  PubMed  Google Scholar 

  20. MacCallum, R. M., Martin, A. C. & Thornton, J. M. Antibody-antigen interactions: contact analysis and binding site topography. J. Mol. Biol. 262, 732–745 (1996).

    Article  CAS  PubMed  Google Scholar 

  21. Dondelinger, M. et al. Understanding the significance and implications of antibody numbering and antigen-binding surface/residue definition. Front. Immunol. 9, 2278 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. O’Meara, M. J. et al. A combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta. J. Chem. Theory Comput. 11, 609–622 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Grossman, I., Alon, A., Ilani, T. & Fass, D. An inhibitory antibody blocks the first step in the dithiol/disulfide relay mechanism of the enzyme QSOX1. J. Mol. Biol. 425, 4366–4378 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. Feldman, T. et al. Inhibition of fibroblast secreted QSOX1 perturbs extracellular matrix in the tumor microenvironment and decreases tumor growth and metastasis in murine cancer models. Oncotarget 11, 386–398 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Marks, C., Hummer, A. M., Chin, M. & Deane, C. M. Humanization of antibodies using a machine learning approach on large-scale repertoire data. Bioinformatics 37, 4041–4047 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Prihoda, D. et al. BioPhi: a platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning. MAbs 14, 2020203 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Starr, C. G. et al. Ultradilute measurements of self-association for the identification of antibodies with favorable high-concentration solution properties. Mol. Pharm. 18, 2744–2753 (2021).

    Article  CAS  PubMed  Google Scholar 

  29. Makowski, E. K., Wu, L., Desai, A. A. & Tessier, P. M. Highly sensitive detection of antibody nonspecific interactions using flow cytometry. MAbs 13, 1951426 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Xu, Y. et al. Addressing polyspecificity of antibodies selected from an in vitro yeast presentation system: a FACS-based, high-throughput selection and analytical tool. Protein Eng. Des. Sel. 26, 663–670 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Kelly, R. L. et al. Chaperone proteins as single component reagents to assess antibody nonspecificity. MAbs 9, 1036–1040 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kelly, R. L. et al. High throughput cross-interaction measures for human IgG1 antibodies correlate with clearance rates in mice. MAbs 7, 770–777 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kingsbury, J. S. et al. A single molecular descriptor to predict solution behavior of therapeutic antibodies. Sci. Adv. 6, eabb0372 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  35. Sircar, A., Kim, E. T. & Gray, J. J. RosettaAntibody: antibody variable region homology modeling server. Nucleic Acids Res. 37, W474–W479 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 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 

  37. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Norn, C. H., Lapidoth, G. & Fleishman, S. J. High-accuracy modeling of antibody structures by a search for minimum-energy recombination of backbone fragments. Proteins 85, 30–38 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Kodali, P., Schoeder, C. T., Schmitz, S., Crowe, J. E. Jr & Meiler, J. RosettaCM for antibodies with very long HCDR3s and low template availability. Proteins https://doi.org/10.1002/prot.26166 (2021).

  40. Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).

  41. Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2022).

  42. O’Brien, T. J. et al. The CA 125 gene: an extracellular superstructure dominated by repeat sequences. Tumour Biol. 22, 348–366 (2001).

    Article  PubMed  Google Scholar 

  43. Duffy, M. J. et al. CA125 in ovarian cancer: European Group on Tumor Markers guidelines for clinical use. Int. J. Gynecol. Cancer 15, 679–691 (2005).

    Article  CAS  PubMed  Google Scholar 

  44. Lloyd, K. O. & Yin, B. W. Synthesis and secretion of the ovarian cancer antigen CA 125 by the human cancer cell line NIH:OVCAR-3. Tumour Biol. 22, 77–82 (2001).

    Article  CAS  PubMed  Google Scholar 

  45. Kashmiri, S. V. S., De Pascalis, R., Gonzales, N. R. & Schlom, J. SDR grafting—a new approach to antibody humanization. Methods 36, 25–34 (2005).

    Article  CAS  PubMed  Google Scholar 

  46. De Pascalis, R. et al. Grafting of ‘abbreviated’ complementarity-determining regions containing specificity-determining residues essential for ligand contact to engineer a less immunogenic humanized monoclonal antibody. J. Immunol. 169, 3076–3084 (2002).

    Article  PubMed  Google Scholar 

  47. 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 

  48. Chao, G. et al. Isolating and engineering human antibodies using yeast surface display. Nat. Protoc. 1, 755–768 (2006).

    Article  CAS  PubMed  Google Scholar 

  49. Knappik, A. et al. Fully synthetic human combinatorial antibody libraries (HuCAL) based on modular consensus frameworks and CDRs randomized with trinucleotides. J. Mol. Biol. 296, 57–86 (2000).

    Article  CAS  PubMed  Google Scholar 

  50. Apgar, J. R. et al. Beyond CDR-grafting: structure-guided humanization of framework and CDR regions of an anti-myostatin antibody. MAbs 8, 1302–1318 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lowe, D. et al. Aggregation, stability, and formulation of human antibody therapeutics. Adv. Protein Chem. Struct. Biol. 84, 41–61 (2011).

    Article  CAS  PubMed  Google Scholar 

  52. Dudgeon, K. et al. General strategy for the generation of human antibody variable domains with increased aggregation resistance. Proc. Natl Acad. Sci. USA 109, 10879–10884 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Perchiacca, J. M., Bhattacharya, M. & Tessier, P. M. Mutational analysis of domain antibodies reveals aggregation hotspots within and near the complementarity determining regions. Proteins 79, 2637–2647 (2011).

    Article  CAS  PubMed  Google Scholar 

  54. Wu, T. T., Johnson, G. & Kabat, E. A. Length distribution of CDRH3 in antibodies. Proteins 16, 1–7 (1993).

    Article  CAS  PubMed  Google Scholar 

  55. Raybould, M. I. J. et al. Five computational developability guidelines for therapeutic antibody profiling. Proc. Natl Acad. Sci. USA 116, 4025–4030 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Baran, D. et al. Principles for computational design of binding antibodies. Proc. Natl Acad. Sci. USA 114, 10900–10905 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Borenstein-Katz, A. et al. Biomolecular recognition of the glycan neoantigen CA19-9 by distinct antibodies. J. Mol. Biol. 433, 167099 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Eddy, S. R. Profile hidden Markov models. Bioinformatics 14, 755–763 (1998).

    Article  CAS  PubMed  Google Scholar 

  59. Fleishman, S. J. et al. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS ONE 6, e20161 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ye, J., Ma, N., Madden, T. L. & Ostell, J. M. IgBLAST: an immunoglobulin variable domain sequence analysis tool. Nucleic Acids Res. 41, W34–W40 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Baker, N. A., Sept, D., Joseph, S., Holst, M. J. & McCammon, J. A. Electrostatics of nanosystems: application to microtubules and the ribosome. Proc. Natl Acad. Sci. USA 98, 10037–10041 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Madeira, F. et al. Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Res. 50, W276–W279 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Waterhouse, A. M., Procter, J. B., Martin, D. M. A., Clamp, M. & Barton, G. J. Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics 25, 1189–1191 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Alon, A. et al. The dynamic disulphide relay of quiescin sulphydryl oxidase. Nature 488, 414–418 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Javitt, G., Kinzel, A., Reznik, N. & Fass, D. Conformational switches and redox properties of the colon cancer‐associated human lectin ZG16. FEBS J. 288, 6465–6475 (2021).

  66. Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Noronha, A. et al. AXL and error-prone DNA replication confer drug resistance and offer strategies to treat EGFR-mutant lung cancer. Cancer Discov. 12, 2666–2683 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Chen, Y. et al. Armed antibodies targeting the mucin repeats of the ovarian cancer antigen, MUC16, are highly efficacious in animal tumor models. Cancer Res. 67, 4924–4932 (2007).

    Article  CAS  PubMed  Google Scholar 

  70. Liang, W.-C. et al. Cross-species vascular endothelial growth factor (VEGF)-blocking antibodies completely inhibit the growth of human tumor xenografts and measure the contribution of stromal VEGF. J. Biol. Chem. 281, 951–961 (2006).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank A. Mechaly (Israel Institute for Biological Research, Department of Infectious Diseases) for a critical reading of the paper, and R. Diskin (Weizmann Institute of Science, Department of Chemical and Structural Biology) and O. Khersonsky (Weizmann Institute of Science, Department of Biomolecular Sciences) for advice. Research in the Fleishman lab was supported by the European Research Council through a Consolidator Award (815379), the Dr Barry Sherman Institute for Medicinal Chemistry, and a donation in memory of Sam Switzer. Research in the Fass lab was supported by the European Research Council through a Proof-of-Concept grant (825076). Research in the Tessier lab was supported by the National Institutes of Health (RF1AG059723 and R35GM136300) and the National Science Foundation (1804313). We acknowledge the European Synchrotron Radiation Facility for the provision of beam time on ID30B, and A. McCarthy for assistance. The collaboration between the Yarden and Fleishman labs was supported by the Weizmann Institute’s BINA framework. Funding for this research was provided by Teva Pharmaceutical Industries Ltd as part of the Israeli National Forum for BioInnovators (NFBI). This work was supported in part by a grant from the Manya Igel Centre for Biomedical Engineering and Signal Processing and the Moross Integrated Cancer Center.

Author information

Author notes

  1. Ashish Noronha

    Present address: Department of Urology, University of California, San Francisco, San Francisco, CA, USA

Authors and Affiliations

  1. Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel

    Ariel Tennenhouse & Sarel J. Fleishman

  2. Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel

    Lev Khmelnitsky, Noa Yeshaya & Deborah Fass

  3. Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel

    Razi Khalaila, Ashish Noronha, Moshit Lindzen, Jakub Abramson & Yosef Yarden

  4. Biointerfaces Institute and Departments of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA

    Emily K. Makowski & Peter M. Tessier

  5. Antibody Engineering Unit, Weizmann Institute of Science, Rehovot, Israel

    Ira Zaretsky

  6. Protein Analysis Unit, Weizmann Institute of Science, Rehovot, Israel

    Yael Fridmann Sirkis & Yael Galon-Wolfenson

Authors
  1. Ariel Tennenhouse
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lev Khmelnitsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Razi Khalaila
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Noa Yeshaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ashish Noronha
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Moshit Lindzen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Emily K. Makowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ira Zaretsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yael Fridmann Sirkis
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yael Galon-Wolfenson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Peter M. Tessier
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jakub Abramson
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yosef Yarden
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Deborah Fass
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Sarel J. Fleishman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.T. and S.J.F. wrote the paper, with input from all authors. A.T. developed the humanization algorithm and designed all humanized sequences. L.K. performed experimental work related to anti-QSOX1 antibodies except for the developability experiments. L.K., N.Y. and D.F. performed crystallography-data collection, and L.K. and D.F. analysed the data. E.K.M. performed developability experiments for anti-QSOX1 antibodies, and E.K.M. and P.M.T. analysed the data. A.N. performed the IP assay for anti-AXL antibody. M.L. and I.Z. expressed and purified anti-AXL antibodies. R.K. performed experimental work related to anti-MUC16 antibodies, and R.K. and J.A. analysed the data. A.T. and L.K. expressed and purified anti-HEWL antibodies. A.T., Y.F.S. and Y.G.-W. performed nano-DSF and SPR characterization of anti-AXL antibodies and analysed the data together with S.J.F., M.L. and Y.Y. A.T., Y.F.S. and Y.G.-W. performed nano-DSF and SPR characterization of anti-HEWL antibodies and analysed the data together with S.J.F.

Corresponding author

Correspondence to Sarel J. Fleishman.

Ethics declarations

Competing interests

A.T. and S.J.F. are named inventors in a patent filing on the CUMAb method (WO2023012807A1). A.T., S.J.F., L.K. and D.F. are named inventors in a patent filing on humanized anti-QSOX1 designs. The other authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Charlotte Deane and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data

Extended Data Fig. 1 Additional biophysical and structural characterization of humanized anti-QSOX1 antibodies.

a, Dot-blot analysis of 15 CUMAb designs. 12 designs exhibit comparable expression levels to the AbLIFT18 design of the anti-QSOX1 antibody14. CUMAb designs are labelled according to heavy chain V-gene subgroup and then by light chain subgroup according to IMGT. If there are two chains from the same subgroup, they are labelled as ‘a’ or ‘b’ arbitrarily. ‘Mock’ refers to a control transfection where no DNA was added. ‘Prev’ refers to a construct from a prior version of CUMAb. b, Functional screen of 15 CUMAb designs, labelled as in A. Presence of an upper band, corresponding to MBP-ZG16 modified with PEG-mal 5000, indicates remaining unoxidized MBP-ZG16 as a result of inhibited QSOX1 activity. As evident from the ‘no QSOX1’ lane, the MBP-ZG16 used in the experiment was not fully reduced, but the reduced population was sufficient to distinguish between the mock-transfected sample, which lacks antibody, and the antibody-containing samples, which all show some PEG-mal-modified species indicative of QSOX1 inhibition. Designs marked with * were further analyzed. c, Titration of relative QSOX1 activity at different antibody concentrations for mαQSOX1 and four CUMAb designs (aggregate data shown in main Fig. 2B). Points represent the average of two technical repeats and errors bars represent the standard deviation. d, Thermal denaturation of mαQSOX1 and four CUMAb designs using nano differential scanning fluorimetry. Shown is the average of two technical repeats. e, Crystal structure of oxidoreductase fragment of human QSOX1 in complex with parental mouse antibody (QSOX1 in dark gray, parental mouse antibody Fv in light gray, PDB entry 4IJ3) aligned with crystal structure of oxidoreductase fragment of human QSOX1 in complex with hαQSOX1.4 (QSOX1 in purple, hαQSOX1.4 Fv in magenta, PDB entry 8AON. Structures are nearly identical (0.7 Å) despite 51 Fv mutations.

Source data

Extended Data Fig. 2 Additional controls for the FACS analyses of anti-MUC16 antibodies.

a, Non-transfected HEK293 cells were stained with a commercial anti-tag antibody, cαMUC16_Ab1, hαMUC16_Ab1, cαMUC16_Ab2, hαMUC16_Ab2.1, and hαMUC16_Ab2.2, demonstrating that none of the five antibodies showed significant binding to cells not expressing the MUC16 construct. b, HEK293 cells expressing the MUC16 construct were stained either without a primary antibody or with a commercial anti-tag antibody, demonstrating that the secondary antibody alone does not label the cells and that the cells do display the intended construct. c, Acetone-fixed OVCAR-3 cells were stained either without a primary antibody or with a control anti-MUC16 antibody69, demonstrating that the secondary antibody alone does not label the cells and that the cells do display MUC16.

Extended Data Fig. 3 Yeast surface-display expression and binding analyses of anti-HEWL antibodies.

a, Expression levels of negative control (the G6 anti-VEGF70 antibody), mouse anti-HEWL antibody (mαHEWL), and 6 CUMAb designs, all formatted as scFv. b, Binding levels to HEWL of the eight antibodies.

Extended Data Fig. 4 Sequence alignments of V genes to the human germline V genes.

Sequence alignments of V genes in successful (a) anti-QSOX1 and (b) anti-HEWL designs to the human germline V genes with the highest sequence identity.

Supplementary information

Source data

Source Data Figs. 2–5 and Extended Data Fig. 1

Source data and unprocessed gels.

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

Tennenhouse, A., Khmelnitsky, L., Khalaila, R. et al. Computational optimization of antibody humanness and stability by systematic energy-based ranking. Nat. Biomed. Eng 8, 30–44 (2024). https://doi.org/10.1038/s41551-023-01079-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-023-01079-1

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