Atomically accurate de novo design of antibodies with RFdiffusion
Wilson, P. C. & Andrews, S. F. Tools to therapeutically harness the human antibody response. Nat. Rev. Immunol. 12, 709–719 (2012).
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
Paulk, A. M., Williams, R. L. & Liu, C. C. Rapidly inducible yeast surface display for antibody evolution with OrthoRep. ACS Synth. Biol. 13, 2629–2634 (2024).
Lyu, X. et al. The global landscape of approved antibody therapies. Antib. Ther. 5, 233–257 (2022).
Sormanni, P., Aprile, F. A. & Vendruscolo, M. Rational design of antibodies targeting specific epitopes within intrinsically disordered proteins. Proc. Natl Acad. Sci. USA 112, 9902–9907 (2015).
Liu, X. et al. Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping. Sci. Rep. 7, 41306 (2017).
Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011).
Xie, X., Valiente, P. A., Lee, J. S., Kim, J. & Kim, P. M. Antibody-SGM, a score-based generative model for antibody heavy-chain design. J. Chem. Inf. Model. 64, 6745–6757 (2024).
Eguchi, R. R. et al. Deep generative design of epitope-specific binding proteins by latent conformation optimization. Preprint at bioRxiv https://doi.org/10.1101/2022.12.22.521698 (2022).
Shanehsazzadeh, A. et al. Unlocking de novo antibody design with generative artificial intelligence. Preprint at bioRxiv https://doi.org/10.1101/2023.01.08.523187 (2023).
Porebski, B. T. et al. Rapid discovery of high-affinity antibodies via massively parallel sequencing, ribosome display and affinity screening. Nat. Biomed. Eng. 8, 214–232 (2024).
Agarwal, A. A. et al. AlphaBind, a domain-specific model to predict and optimize antibody–antigen binding affinity. mAbs 17, 2534626 (2025).
Vázquez Torres, S. et al. De novo design of high-affinity binders of bioactive helical peptides. Nature 626, 435–442 (2024).
Sappington, I. et al. Improved protein binder design using beta-pairing targeted RFdiffusion. Preprint at bioRxiv https://doi.org/10.1101/2024.10.11.617496 (2024).
Cao, L. et al. Design of protein-binding proteins from the target structure alone. Nature 605, 551–560 (2022).
Gainza, P. et al. De novo design of protein interactions with learned surface fingerprints. Nature 617, 176–184 (2023).
Pacesa, M. et al. One-shot design of functional protein binders with BindCraft. Nature 646, 483–492 (2025).
Cutting, D., Dreyer, F. A., Errington, D., Schneider, C. & Deane, C. M. De novo antibody design with SE(3) diffusion. Preprint at arXiv https://doi.org/10.48550/arXiv.2405.07622 (2024).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Yang, J. et al. Improved protein structure prediction using predicted interresidue orientations. Proc. Natl Acad. Sci. USA 117, 1496–1503 (2020).
Wang, J. et al. Scaffolding protein functional sites using deep learning. Science 377, 387–394 (2022).
Bennett, N. et al. Improving de novo protein binder design with deep learning. Nat. Commun. 14, 2625 (2023).
Yin, R. & Pierce, B. G. Evaluation of AlphaFold antibody–antigen modeling with implications for improving predictive accuracy. Protein Sci. 33, e4865 (2024).
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Jin, B., Odongo, S., Radwanska, M. & Magez, S. Nanobodies: a review of generation, diagnostics and therapeutics. Int. J. Mol. Sci. 24, 5994 (2023).
Mitchell, L. S. & Colwell, L. J. Analysis of nanobody paratopes reveals greater diversity than classical antibodies. Protein Eng. Des. Sel. 31, 267–275 (2018).
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).
Hunt, A. C. et al. Multivalent designed proteins neutralize SARS-CoV-2 variants of concern and confer protection against infection in mice. Sci. Transl. Med. 14, eabn1252 (2022).
Ragotte, R. J. et al. De novo design of potent inhibitors of clostridial family toxins. Proc. Natl Acad. Sci. USA 122, e2509329122 (2025).
Rix, G. et al. Continuous evolution of user-defined genes at 1 million times the genomic mutation rate. Science 386, eadm9073 (2024).
Ravikumar, A., Arzumanyan, G. A., Obadi, M. K. A., Javanpour, A. A. & Liu, C. C. Scalable, continuous evolution of genes at mutation rates above genomic error thresholds. Cell 175, 1946–1957.e13 (2018).
Walls, A. C. et al. Unexpected receptor functional mimicry elucidates activation of coronavirus fusion. Cell 176, 1026–1039.e15 (2019).
Yarmarkovich, M. et al. Targeting of intracellular oncoproteins with peptide-centric CARs. Nature 623, 820–827 (2023).
Sun, Y. et al. Structural principles of peptide-centric chimeric antigen receptor recognition guide therapeutic expansion. Sci. Immunol. 8, eadj5792 (2023).
Du, H. et al. Targeting peptide antigens using a multiallelic MHC I-binding system. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02505-8 (2024).
Sim, M. J. W. et al. High-affinity oligoclonal TCRs define effective adoptive T cell therapy targeting mutant KRAS-G12D. Proc. Natl Acad. Sci. USA 117, 12826–12835 (2020).
Sun, Y. et al. Universal open MHC-I molecules for rapid peptide loading and enhanced complex stability across HLA allotypes. Proc. Natl Acad. Sci. USA 120, e2304055120 (2023).
Hitawala, F. N. & Gray, J. J. What has AlphaFold3 learned about antibody and nanobody docking, and what remains unsolved? Preprint at bioRxiv https://doi.org/10.1101/2024.09.21.614257 (2024).
Wang, C. et al. Proteus: pioneering protein structure generation for enhanced designability and efficiency. Preprint at bioRxiv https://doi.org/10.1101/2024.02.10.579791 (2024).
Yim, J. et al. Fast protein backbone generation with SE(3) flow matching. Preprint at arXiv https://doi.org/10.48550/arXiv.2310.05297 (2023).
Bose, J. et al. SE(3)-stochastic flow matching for protein backbone generation. In Proc. 12th International Conference on Learning Representations (ICLR, 2024).
Geffner, T. et al. Proteina: scaling flow-based protein structure generative models. In Proc. 13th International Conference on Learning Representations (ICLR, 2025).
Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science https://doi.org/10.1126/science.adl2528 (2024).
Gao, S. H., Huang, K., Tu, H. & Adler, A. S. Monoclonal antibody humanness score and its applications. BMC Biotechnol. 13, 55 (2013).
Dreyer, F. A., Cutting, D., Schneider, C., Kenlay, H. & Deane, C. M. Inverse folding for antibody sequence design using deep learning. Preprint at https://doi.org/10.48550/arXiv.2310.19513 (2023).
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).
Bio, N. & Biswas, S. De novo design of epitope-specific antibodies against soluble and multipass membrane proteins with high specificity, developability, and function. Preprint at bioRxiv https://doi.org/10.1101/2025.01.21.633066 (2025).
Watson, J. L. Antibody training dataset for “Atomically accurate de novo design of antibodies with RFdiffusion” (data set). Zenodo https://doi.org/10.5281/zenodo.15741710 (2025).
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
Dunbar, J. et al. SAbDab: the structural antibody database. Nucleic Acids Res. 42, D1140–D1146 (2014).
Jäger, M., Gehrig, P. & Plückthun, A. The scFv fragment of the antibody hu4D5-8: evidence for early premature domain interaction in refolding. J. Mol. Biol. 305, 1111–1129 (2001).
Kawai, S., Hashimoto, W. & Murata, K. Transformation of Saccharomyces cerevisiae and other fungi. Bioeng. Bugs 1, 395–403 (2010).
■ مصدر الخبر الأصلي
نشر لأول مرة على: www.nature.com
تاريخ النشر: 2025-11-05 02:00:00
الكاتب: Nathaniel R. Bennett
تنويه من موقع “wakalanews”:
تم جلب هذا المحتوى بشكل آلي من المصدر:
www.nature.com
بتاريخ: 2025-11-05 02:00:00.
الآراء والمعلومات الواردة في هذا المقال لا تعبر بالضرورة عن رأي موقع “wakalanews”، والمسؤولية الكاملة تقع على عاتق المصدر الأصلي.
ملاحظة: قد يتم استخدام الترجمة الآلية في بعض الأحيان لتوفير هذا المحتوى.
موقع Wakala News يقدم أحدث الأخبار العالمية والعربية لحظة بلحظة، بتغطية دقيقة وموثوقة للأحداث السياسية والاقتصادية والتقنية حول العالم.
