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).
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).
Google Scholar
Wu, Q., Yang, S., Liu, J., Jiang, D. & Wei, W. Antibody theranostics in precision medicine. Med 4, 69–74 (2023).
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).
Google Scholar
Jovčevska, I. & Muyldermans, S. The therapeutic potential of nanobodies. BioDrugs 34, 11–26 (2020).
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).
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).
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).
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).
Google Scholar
Garattini, L. & Padula, A. Precision medicine and monoclonal antibodies: breach of promise? Croat. Med. J. 60, 284 (2019).
Google Scholar
Klee, G. G. Human anti-mouse antibodies. Arch. Pathol. Lab. Med. 124, 921–923 (2000).
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).
Google Scholar
Almagro, J. C. & Fransson, J. Humanization of antibodies. Front. Biosci. 13, 1619–33 (2008).
Presta, L. G. Antibody engineering. Curr. Opin. Struct. Biol. 2, 593–596 (1992).
Google Scholar
Jolliffe, L. K. Humanized antibodies: enhancing therapeutic utility through antibody engineering. Int. Rev. Immunol. 10, 241–250 (1993).
Google Scholar
Vaswani, S. K. & Hamilton, R. G. Humanized antibodies as potential therapeutic drugs. Ann. Allergy Asthma Immunol. 81, 105–119 (1998).
Google Scholar
Co, M. S. et al. Chimeric and humanized antibodies with specificity for the CD33 antigen. J. Immunol. 148, 1149–1154 (1992).
Google Scholar
Katoh, M., Tateno, C., Yoshizato, K. & Yokoi, T. Chimeric mice with humanized liver. Toxicology 246, 9–17 (2008).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
Google Scholar
Ramon, A. Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV. Nat. Mach. Intell. 6, 74–91 (2024).
Google Scholar
Thullier, P., Huish, O., Pelat, T. & Martin, A. C. The humanness of macaque antibody sequences. J. Mol. Biol. 396, 1439–1450 (2010).
Google Scholar
Gao, S. H., Huang, K., Tu, H. & Adler, A. S. Monoclonal antibody humanness score and its applications. BMC Biotechnol. 13, 55 (2013).
Google Scholar
Abhinandan, K. & Martin, A. C. Analyzing the ‘degree of humanness’ of antibody sequences. J. Mol. Biol. 369, 852–862 (2007).
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).
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).
Google Scholar
Lisanza, S. L. et al. Multistate and functional protein design using RoseTTAFold sequence space diffusion. Nat. Biotechnol. 43, 1288–1298 (2025).
Google Scholar
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
Google Scholar
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
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).
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).
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).
Google Scholar
Burbach, S. M. & Briney, B. Improving antibody language models with native pairing. Patterns 5, 100967 (2024).
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).
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).
Google Scholar
Lefranc, M.-P. et al. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res. 37, D1006–D1012 (2009).
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).
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).
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).
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).
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).
Google Scholar
Dunbar, J. & Deane, C. M. Anarci: antigen receptor numbering and receptor classification. Bioinformatics 32, 298–300 (2016).
Google Scholar
Levenshtein, V. Binary codes capable of correcting deletions, insertions, and reversals. Proc. Soviet Physics Doklady 10, 707–710 (1966).
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).
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