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An adaptive autoregressive diffusion approach to design active humanized antibodies and nanobodies

  • Davies, D. R. & Chacko, S. Antibody structure. Acc. Chem. Res. 26, 421–427 (1993).

    Article 

    Google Scholar 

  • Stanfield, R. L. & Wilson, I. A. Antibody structure. in Antibodies for Infectious Diseases (eds Crowe, J. E., Boraschi, D. & Rappuoli, R.) 49–62 (ASM Press, 2015).

  • Goldsby, R. A. Immunology (Macmillan, 2003).

  • Waldmann, H. Human monoclonal antibodies: the benefits of humanization. in Human Monoclonal Antibodies Methods in Molecular Biology, vol. 1904 (ed Steinitz, M.) 1–10 (Humana Press, 2019).

  • Lu, R.-M. et al. Development of therapeutic antibodies for the treatment of diseases. J. Biomed. Sci. 27, 1 (2020).

    Article 

    Google Scholar 

  • Wu, Q., Yang, S., Liu, J., Jiang, D. & Wei, W. Antibody theranostics in precision medicine. Med 4, 69–74 (2023).

    Article 

    Google Scholar 

  • Vincke, C. et al. General strategy to humanize a camelid single-domain antibody and identification of a universal humanized nanobody scaffold. J. Biol. Chem. 284, 3273–3284 (2009).

    Article 

    Google Scholar 

  • Jovčevska, I. & Muyldermans, S. The therapeutic potential of nanobodies. BioDrugs 34, 11–26 (2020).

    Article 

    Google Scholar 

  • Bannas, P., Hambach, J. & Koch-Nolte, F. Nanobodies and nanobody-based human heavy chain antibodies as antitumor therapeutics. Front. Immunol. 8, 309808 (2017).

    Article 

    Google Scholar 

  • Wesolowski, J. et al. Single domain antibodies: promising experimental and therapeutic tools in infection and immunity. Med. Microbiol. Immunol. 198, 157–174 (2009).

    Article 

    Google Scholar 

  • Salvador, J.-P., Vilaplana, L. & Marco, M.-P. Nanobody: outstanding features for diagnostic and therapeutic applications. Anal. Bioanal. Chem. 411, 1703–1713 (2019).

    Article 

    Google Scholar 

  • Kaneko, Y. & Takeuchi, T. Targeted antibody therapy and relevant novel biomarkers for precision medicine for rheumatoid arthritis. Int. Immunol. 29, 511–517 (2017).

    Article 

    Google Scholar 

  • Garattini, L. & Padula, A. Precision medicine and monoclonal antibodies: breach of promise? Croat. Med. J. 60, 284 (2019).

    Article 

    Google Scholar 

  • Klee, G. G. Human anti-mouse antibodies. Arch. Pathol. Lab. Med. 124, 921–923 (2000).

    Article 

    Google Scholar 

  • Tjandra, J. J., Ramadi, L. & McKenzie, I. F. Development of human anti-murine antibody (HAMA) response in patients. Immunol. Cell Biol. 68, 367–376 (1990).

    Article 

    Google Scholar 

  • Almagro, J. C. & Fransson, J. Humanization of antibodies. Front. Biosci. 13, 1619–33 (2008).

    Google Scholar 

  • Presta, L. G. Antibody engineering. Curr. Opin. Struct. Biol. 2, 593–596 (1992).

    Article 

    Google Scholar 

  • Jolliffe, L. K. Humanized antibodies: enhancing therapeutic utility through antibody engineering. Int. Rev. Immunol. 10, 241–250 (1993).

    Article 

    Google Scholar 

  • Vaswani, S. K. & Hamilton, R. G. Humanized antibodies as potential therapeutic drugs. Ann. Allergy Asthma Immunol. 81, 105–119 (1998).

    Article 

    Google Scholar 

  • Co, M. S. et al. Chimeric and humanized antibodies with specificity for the CD33 antigen. J. Immunol. 148, 1149–1154 (1992).

    Article 

    Google Scholar 

  • Katoh, M., Tateno, C., Yoshizato, K. & Yokoi, T. Chimeric mice with humanized liver. Toxicology 246, 9–17 (2008).

    Article 

    Google Scholar 

  • Lo, B. K. C. Antibody humanization by CDR grafting. in Antibody Engineering Methods in Molecular Biology vol. 248 (ed Lo, B. K. C.) 135–159 (Humana Press, 2004).

  • Winter, G. & Harris, W. J. Humanized antibodies. Immunol. Today 14, 243–246 (1993).

    Article 

    Google Scholar 

  • Williams, D. G., Matthews, D. J. & Jones, T. Humanising antibodies by CDR grafting. in Antibody Engineering Springer Protocols Handbooks (eds Kontermann, R. & Dübel, S.) 319–339 (Springer, 2010).

  • Hu, W.-G., Yin, J., Chau, D., Hu, C. C. & Cherwonogrodzky, J. W. in Ricin Toxin (ed. Cherwonogrodzky, J. W.) 159 (Bentham Science, 2014).

  • 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 

    Google Scholar 

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

    Article 

    Google Scholar 

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

    Article 

    Google Scholar 

  • Kashmiri, S. V. S, De Pascalis, R. & Gonzales, N. R. Developing a minimally immunogenic humanized antibody by SDR grafting. in Antibody Engineering Methods in Molecular Biology vol. 248 (ed Lo, B. K. C.) 361–376 (Human Press, 2004).

  • Gonzales, N. R. et al. SDR grafting of a murine antibody using multiple human germline templates to minimize its immunogenicity. Mol. Immunol. 41, 863–872 (2004).

    Article 

    Google Scholar 

  • Kim, J. H. & Hong, H. J. Humanization by CDR grafting and specificity-determining residue grafting. in Antibody Engineering Methods in Molecular Biology vol. 907 (ed Chames, P.) 237–245 (Human Press, 2012).

  • 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 

    Google Scholar 

  • Clavero-Álvarez, A., Di Mambro, T., Perez-Gaviro, S., Magnani, M. & Bruscolini, P. Humanization of antibodies using a statistical inference approach. Sci. Rep. 8, 14820 (2018).

    Article 

    Google Scholar 

  • 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 

    Google Scholar 

  • Tennenhouse, A. et al. Computational optimization of antibody humanness and stability by systematic energy-based ranking. Nat. Biomed. Eng. 8, 30–44 (2024).

    Article 

    Google Scholar 

  • Sang, Z., Xiang, Y., Bahar, I. & Shi, Y. Llamanade: an open-source computational pipeline for robust nanobody humanization. Structure 30, 418–429 (2022).

    Article 

    Google Scholar 

  • Ramon, A. Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV. Nat. Mach. Intell. 6, 74–91 (2024).

    Article 

    Google Scholar 

  • Thullier, P., Huish, O., Pelat, T. & Martin, A. C. The humanness of macaque antibody sequences. J. Mol. Biol. 396, 1439–1450 (2010).

    Article 

    Google Scholar 

  • Gao, S. H., Huang, K., Tu, H. & Adler, A. S. Monoclonal antibody humanness score and its applications. BMC Biotechnol. 13, 55 (2013).

    Article 

    Google Scholar 

  • Abhinandan, K. & Martin, A. C. Analyzing the ‘degree of humanness’ of antibody sequences. J. Mol. Biol. 369, 852–862 (2007).

    Article 

    Google Scholar 

  • Wollacott, A. M. et al. Quantifying the nativeness of antibody sequences using long short-term memory networks. Protein Eng. Des. Sel. 32, 347–354 (2019).

    Article 

    Google Scholar 

  • Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. In 34th Conference on Neural Information Processing Systems (NeurIPS 2020) https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf (NeurIPS, 2020).

  • Yan, D., Qi, L., Hu, V. T., Yang, M.-H. & Tang, M. Training class-imbalanced diffusion model via overlap optimization. Preprint at https://arxiv.org/abs/2402.10821 (2024).

  • Bian, T. et al. Hierarchical graph latent diffusion model for molecule generation. Preprint at OpenReview https://openreview.net/forum?id=RSincg5RBe (2024).

  • Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article 

    Google Scholar 

  • Lisanza, S. L. et al. Multistate and functional protein design using RoseTTAFold sequence space diffusion. Nat. Biotechnol. 43, 1288–1298 (2025).

    Article 

    Google Scholar 

  • Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).

    Article 

    Google Scholar 

  • Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    Article 

    Google Scholar 

  • Alamdari, S. et al. Protein generation with evolutionary diffusion: sequence is all you need. Preprint at bioRxiv https://doi.org/10.1101/2023.09.11.556673 (2023).

  • Wang, X. et al. Diffusion language models are versatile protein learners. In Proc. 41st International Conference on Machine Learning (eds Salakhutdinov, R. et al.) 52309–5233 (JMLR, 2024).

  • Hoogeboom, E. et al. Autoregressive diffusion models. In International Conference on Learning Representations (ICLR, 2022) https://openreview.net/forum?id=Lm8T39vLDTE (ICLR, 2022).

  • Austin, J., Johnson, D. D., Ho, J., Tarlow, D. & Van Den Berg, R. Structured denoising diffusion models in discrete state-spaces In 35th Conference on Neural Information Processing Systems (NeurIPS 2021) https://proceedings.neurips.cc/paper_files/paper/2021/file/958c530554f78bcd8e97125b70e6973d-Paper.pdf (NeurIPS, 2021).

  • Li, M. et al. Broadly neutralizing and protective nanobodies against SARS-CoV-2 Omicron subvariants BA.1, BA.2, and BA.4/5 and diverse sarbecoviruses. Nat. Commun. 13, 7957 (2022).

    Article 

    Google Scholar 

  • Lan, J. et al. Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature 581, 215–220 (2020).

    Article 

    Google Scholar 

  • Pedersen, H. et al. A complement C3–specific nanobody for modulation of the alternative cascade identifies the C-terminal domain of C3b as functional in C5 convertase activity. J. Immunol. 205, 2287–2300 (2020).

    Article 

    Google Scholar 

  • Burbach, S. M. & Briney, B. Improving antibody language models with native pairing. Patterns 5, 100967 (2024).

    Article 

    Google Scholar 

  • Olsen, T. H., Boyles, F. & Deane, C. M. Observed Antibody Space: a diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences. Protein Sci. 31, 141–146 (2022).

    Article 

    Google Scholar 

  • Abanades, B. et al. The patent and literature antibody database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures. Nucleic Acids Res. 52, D545–D551 (2024).

    Article 

    Google Scholar 

  • Lefranc, M.-P. et al. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res. 37, D1006–D1012 (2009).

    Article 

    Google Scholar 

  • Pavlinkova, G. et al. Effects of humanization and gene shuffling on immunogenicity and antigen binding of anti-TAG-72 single-chain Fvs. Int. J. Cancer 94, 717–726 (2001).

    Article 

    Google Scholar 

  • Errico, J. M. et al. Structural mechanism of SARS-CoV-2 neutralization by two murine antibodies targeting the RBD. Cell Rep. 37, 109882 (2021).

    Article 

    Google Scholar 

  • Kovaltsuk, A. et al. Observed Antibody Space: a resource for data mining next-generation sequencing of antibody repertoires. J. Immunol. 201, 2502–2509 (2018).

    Article 

    Google Scholar 

  • Abanades, B. et al. The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures. Nucleic Acids Res. 52, D545–D551 (2023).

    Article 

    Google Scholar 

  • Hadsund, J. T. et al. nanoBERT: A deep learning model for gene agnostic navigation of the nanobody mutational space. Bioinform. Adv. https://doi.org/10.1093/bioadv/vbae033 (2024).

  • Webb, B. & Sali, A. Comparative protein structure modeling using modeller. Curr. Protoc. Bioinforma. 54, 5–6 (2016).

    Article 

    Google Scholar 

  • Dunbar, J. & Deane, C. M. Anarci: antigen receptor numbering and receptor classification. Bioinformatics 32, 298–300 (2016).

    Article 

    Google Scholar 

  • Levenshtein, V. Binary codes capable of correcting deletions, insertions, and reversals. Proc. Soviet Physics Doklady 10, 707–710 (1966).

    MathSciNet 

    Google Scholar 

  • Jang, E., Gu, S. & Poole, B. Categorical reparametrization with Gumbel–Softmax. In International Conference on Learning Representations (ICLR, 2017) https://openreview.net/forum?id=rkE3y85ee (ICLR, 2017).

  • Devlin J. et al. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019) https://doi.org/10.18653/v1/N19-1423 (2019).

  • Mullis, K. et al. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. Cold Spring Harb. Symp. Quant. Biol. 51, 263–273 (1986).

    Article 

    Google Scholar 

  • Ma, J. et al. HuDiff. Zenodo https://doi.org/10.5281/zenodo.16974296 (2025).

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    2025-10-01 00:00:00

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