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@ARTICLE{Rettie:1051977,
      author       = {Rettie, Stephen A. and Juergens, David and Adebomi, Victor
                      and Bueso, Yensi Flores and Zhao, Qinqin and Leveille,
                      Alexandria N. and Liu, Andi and Bera, Asim K. and Wilms,
                      Joana A. and Üffing, Alina and Kang, Alex and
                      Brackenbrough, Evans and Lamb, Mila and Gerben, Stacey R.
                      and Murray, Analisa and Levine, Paul M. and Schneider, Maika
                      and Vasireddy, Vibha and Ovchinnikov, Sergey and
                      Weiergräber, Oliver H. and Willbold, Dieter and Kritzer,
                      Joshua A. and Mougous, Joseph D. and Baker, David and
                      DiMaio, Frank and Bhardwaj, Gaurav},
      title        = {{A}ccurate de novo design of high-affinity protein-binding
                      macrocycles using deep learning},
      journal      = {Nature chemical biology},
      volume       = {21},
      number       = {12},
      issn         = {1552-4450},
      address      = {Basingstoke},
      publisher    = {Nature Publishing Group},
      reportid     = {FZJ-2026-00658},
      pages        = {1948 - 1956},
      year         = {2025},
      abstract     = {Developing macrocyclic binders to therapeutic proteins
                      typically relies on large-scale screening methods that are
                      resource intensive and provide little control over binding
                      mode. Despite progress in protein design, there are
                      currently no robust approaches for de novo design of
                      protein-binding macrocycles. Here we introduce RFpeptides, a
                      denoising diffusion-based pipeline for designing macrocyclic
                      binders against protein targets of interest. We tested 20 or
                      fewer designed macrocycles against each of four diverse
                      proteins and obtained binders with medium to high affinity
                      against all targets. For one of the targets, Rhombotarget A
                      (RbtA), we designed a high-affinity binder
                      (Kd < 10 nM) despite starting from the predicted
                      target structure. X-ray structures for macrocycle-bound
                      myeloid cell leukemia 1, γ-aminobutyric acid type A
                      receptor-associated protein and RbtA complexes match closely
                      with the computational models, with a Cα root-mean-square
                      deviation < 1.5 Å to the design models. RFpeptides
                      provides a framework for rapid and custom design of
                      macrocyclic peptides for diagnostic and therapeutic
                      applications.},
      cin          = {IBI-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBI-7-20200312},
      pnm          = {5241 - Molecular Information Processing in Cellular Systems
                      (POF4-524)},
      pid          = {G:(DE-HGF)POF4-5241},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1038/s41589-025-01929-w},
      url          = {https://juser.fz-juelich.de/record/1051977},
}