Atomically Accurate De Novo Design Of Antibodies With RFdiffusion

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).

    Article
    CAS
    PubMed
    PubMed Central

    Google Scholar

  • Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion.Nature620, 1089–1100 (2023).

    PubMed و Wilson, و P. – تفاصيل مهمة

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

  • 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).

    Article
    CAS
    PubMed

    Google Scholar

  • Lyu, X. et al. The global landscape of approved antibody therapies.Antib. Ther.5, 233–257 (2022).

    C. و antibody و Paulk, – تفاصيل مهمة

    CAS
    PubMed
    PubMed Central

    Google Scholar

  • Sormanni, P., Aprile, F. A. & Vendruscolo, M. Rational design of antibodies targeting specific epitopes within intrinsically disordered proteins.Proc. Natl Acad. Sci. USA112, 9902–9907 (2015).

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

  • 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).

    design و of و Sci. – تفاصيل مهمة

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

  • 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

  • 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).

    J. و for و PubMed – تفاصيل مهمة

  • Eguchi, R. R. et al. Deep generative design of epitope-specific binding proteins by latent conformation optimization. Preprint atbioRxivhttps://doi.org/10.1101/2022.12.22.521698 (2022).

  • Shanehsazzadeh, A. et al. Unlocking de novo antibody design with generative artificial intelligence. Preprint atbioRxivhttps://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).

    Article
    CAS
    PubMed

    Google Scholar

    et و al. و Shanehsazzadeh, – تفاصيل مهمة

  • Agarwal, A. A. et al. AlphaBind, a domain-specific model to predict and optimize antibody–antigen binding affinity.mAbs17, 2534626 (2025).

  • Vázquez Torres, S. et al. De novo design of high-affinity binders of bioactive helical peptides.Nature626, 435–442 (2024).

    Article
    PubMed
    ADS

    Google Scholar

  • Sappington, I. et al. Improved protein binder design using beta-pairing targeted RFdiffusion. Preprint atbioRxivhttps://doi.org/10.1101/2024.10.11.617496 (2024).

    et و al. و design – تفاصيل مهمة

  • Cao, L. et al. Design of protein-binding proteins from the target structure alone.Nature605, 551–560 (2022).

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

  • Gainza, P. et al. De novo design of protein interactions with learned surface fingerprints.Nature617, 176–184 (2023).

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

    PubMed و Article و CAS – تفاصيل مهمة

  • Pacesa, M. et al. One-shot design of functional protein binders with BindCraft.Nature646, 483–492 (2025).

  • Cutting, D., Dreyer, F. A., Errington, D., Schneider, C. & Deane, C. M. De novo antibody design with SE(3) diffusion. Preprint atarXivhttps://doi.org/10.48550/arXiv.2405.07622 (2024).

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold.Nature596, 583–589 (2021).

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

    D., و C. و with – تفاصيل مهمة

  • Yang, J. et al. Improved protein structure prediction using predicted interresidue orientations.Proc. Natl Acad. Sci. USA117, 1496–1503 (2020).

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

  • Wang, J. et al. Scaffolding protein functional sites using deep learning.Science377, 387–394 (2022).

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

    PubMed و Article و CAS – تفاصيل مهمة

  • Bennett, N. et al. Improving de novo protein binder design with deep learning.Nat. Commun.14, 2625 (2023).

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

  • 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.Nature630, 493–500 (2024).

    PubMed و of و AlphaFold – تفاصيل مهمة

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

  • Jin, B., Odongo, S., Radwanska, M. & Magez, S. Nanobodies: a review of generation, diagnostics and therapeutics.Int. J. Mol. Sci.24, 5994 (2023).

    Article
    CAS
    PubMed
    PubMed Central

    Google Scholar

  • Mitchell, L. S. & Colwell, L. J. Analysis of nanobody paratopes reveals greater diversity than classical antibodies.Protein Eng. Des. Sel.31, 267–275 (2018).

    & و S. و of – تفاصيل مهمة

    Article
    CAS
    PubMed
    PubMed Central

    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
    CAS
    PubMed

    Google Scholar

  • 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).

    C. و et و al. – تفاصيل مهمة

    Article
    CAS
    PubMed
    PubMed Central

    Google Scholar

  • Ragotte, R. J. et al. De novo design of potent inhibitors of clostridial family toxins.Proc. Natl Acad. Sci. USA122, e2509329122 (2025).

  • Rix, G. et al. Continuous evolution of user-defined genes at 1 million times the genomic mutation rate.Science386, eadm9073 (2024).

    Article
    CAS
    PubMed

    Google Scholar

    of و et و al. – تفاصيل مهمة

  • 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.Cell175, 1946–1957.e13 (2018).

    Article
    CAS
    PubMed
    PubMed Central

    Google Scholar

  • Walls, A. C. et al. Unexpected receptor functional mimicry elucidates activation of coronavirus fusion.Cell176, 1026–1039.e15 (2019).

    Article
    CAS
    PubMed
    PubMed Central

    Google Scholar

    PubMed و Article و CAS – تفاصيل مهمة

  • Yarmarkovich, M. et al. Targeting of intracellular oncoproteins with peptide-centric CARs.Nature623, 820–827 (2023).

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

  • Sun, Y. et al. Structural principles of peptide-centric chimeric antigen receptor recognition guide therapeutic expansion.Sci. Immunol.8, eadj5792 (2023).

    Article
    CAS
    PubMed
    PubMed Central

    Google Scholar

    PubMed و Article و CAS – تفاصيل مهمة

  • 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. USA117, 12826–12835 (2020).

    Article
    CAS
    PubMed
    PubMed Central
    ADS

    Google Scholar

  • Sun, Y. et al. Universal open MHC-I molecules for rapid peptide loading and enhanced complex stability across HLA allotypes.Proc. Natl Acad. Sci. USA120, e2304055120 (2023).

    et و al. و Proc. – تفاصيل مهمة

    Article
    CAS
    PubMed
    PubMed Central

    Google Scholar

  • Hitawala, F. N. & Gray, J. J. What has AlphaFold3 learned about antibody and nanobody docking, and what remains unsolved? Preprint atbioRxivhttps://doi.org/10.1101/2024.09.21.614257 (2024).

  • Wang, C. et al. Proteus: pioneering protein structure generation for enhanced designability and efficiency. Preprint atbioRxivhttps://doi.org/10.1101/2024.02.10.579791 (2024).

  • Yim, J. et al. Fast protein backbone generation with SE(3) flow matching. Preprint atarXivhttps://doi.org/10.48550/arXiv.2310.05297 (2023).

    J. و and و Preprint – تفاصيل مهمة

  • Bose, J. et al. SE(3)-stochastic flow matching for protein backbone generation. InProc. 12th International Conference on Learning Representations(ICLR, 2024).

  • Geffner, T. et al. Proteina: scaling flow-based protein structure generative models. InProc. 13th International Conference on Learning Representations(ICLR, 2025).

  • Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom.Sciencehttps://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).

    et و al. و and – تفاصيل مهمة

    Article
    CAS
    PubMed
    PubMed Central

    Google Scholar

  • 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.mAbs14, 2020203 (2022).

    Article
    PubMed
    PubMed Central

    Google Scholar

    antibody و for و deep – تفاصيل مهمة

  • Bio, N. & Biswas, S. De novo design of epitope-specific antibodies against soluble and multipass membrane proteins with high specificity, developability, and function. Preprint atbioRxivhttps://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).Zenodohttps://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).

    Article
    CAS
    PubMed
    PubMed Central

    Google Scholar

    of و PubMed و Watson, – تفاصيل مهمة

  • Dunbar, J. et al. SAbDab: the structural antibody database.Nucleic Acids Res.42, D1140–D1146 (2014).

    Article
    CAS
    PubMed

    Google Scholar

  • 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).

    Article
    PubMed

    Google Scholar

    Article و PubMed و Google – تفاصيل مهمة

  • Kawai, S., Hashimoto, W. & Murata, K. Transformation ofSaccharomyces cerevisiaeand other fungi.Bioeng. Bugs1, 395–403 (2010).

    Article
    PubMed
    PubMed Central

    Google Scholar

  • Disclaimer: This news article has been republished exactly as it appeared on its original source, without any modification.
    We do not take any responsibility for its content, which remains solely the responsibility of the original publisher.

    Author:Nathaniel R. Bennett
    Published on:2025-11-05 04:00:00
    Source: www.nature.com


    Disclaimer: This news article has been republished exactly as it appeared on its original source, without any modification.
    We do not take any responsibility for its content, which remains solely the responsibility of the original publisher.


    Author: uaetodaynews
    Published on: 2025-11-05 18:19:00
    Source: uaetodaynews.com

    daliynewslb.com

    Stay updated with Daily News LB – your trusted source for the latest news and in-depth analysis on politics, economy, and technology in Lebanon and the world.

    Related Articles

    Leave a Reply

    Your email address will not be published. Required fields are marked *

    Back to top button