Accurate de novo design of high-affinity protein-binding macrocycles using deep learning

Stephen A Rettie^#^{1

2

3}, David Juergens^#^{2

4}, Victor Adebomi^#^{1

2}, Yensi Flores Bueso^{1

2

5

6}, Qinqin Zhao⁷, Alexandria N Leveille⁸, Andi Liu⁷, Asim K Bera², Joana A Wilms^{9

10}, Alina Üffing^{9

10

11}, Alex Kang², Evans Brackenbrough², Mila Lamb², Stacey R Gerben², Analisa Murray², Paul M Levine², Maika Schneider^{1

2

12}, Vibha Vasireddy^{1

2}, Sergey Ovchinnikov¹³, Oliver H Weiergräber¹⁰, Dieter Willbold^{9

10}, Joshua A Kritzer⁸, Joseph D Mougous^{7

14}, David Baker^{15

16

17}, Frank DiMaio^{18

19}, Gaurav Bhardwaj^{20

21}

Affiliations

¹ Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
² Institute for Protein Design, University of Washington, Seattle, WA, USA.
³ Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA.
⁴ Graduate Program in Molecular Engineering, University of Washington, Seattle, WA, USA.
⁵ Department of Biochemistry, University of Washington, Seattle, WA, USA.
⁶ Cancer Research @UCC, University College Cork, Cork, Ireland.
⁷ Department of Microbiology, University of Washington, Seattle, WA, USA.
⁸ Department of Chemistry, Tufts University, 62 Talbot Avenue, Medford, MA, USA.
⁹ Heinrich-Heine-Universität Düsseldorf, Institut für Physikalische Biologie, Düsseldorf, Germany.
¹⁰ Forschungszentrum Jülich, Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Jülich, Germany.
¹¹ Molecular Cell Biology of Autophagy Laboratory, The Francis Crick Institute, London, UK.
¹² Department of Chemistry, University of Washington, Seattle, WA, USA.
¹³ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁴ Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
¹⁵ Institute for Protein Design, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
¹⁶ Department of Biochemistry, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
¹⁷ Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
¹⁸ Institute for Protein Design, University of Washington, Seattle, WA, USA. dimaio@uw.edu.
¹⁹ Department of Biochemistry, University of Washington, Seattle, WA, USA. dimaio@uw.edu.
²⁰ Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA. gauravb@uw.edu.
²¹ Institute for Protein Design, University of Washington, Seattle, WA, USA. gauravb@uw.edu.

^# Contributed equally.

PMID: 40542165
DOI: 10.1038/s41589-025-01929-w

Accurate de novo design of high-affinity protein-binding macrocycles using deep learning

Stephen A Rettie et al. Nat Chem Biol. 2025.

. 2025 Jun 20.

doi: 10.1038/s41589-025-01929-w. Online ahead of print.

Authors

Affiliations

¹ Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
² Institute for Protein Design, University of Washington, Seattle, WA, USA.
³ Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA.
⁴ Graduate Program in Molecular Engineering, University of Washington, Seattle, WA, USA.
⁵ Department of Biochemistry, University of Washington, Seattle, WA, USA.
⁶ Cancer Research @UCC, University College Cork, Cork, Ireland.
⁷ Department of Microbiology, University of Washington, Seattle, WA, USA.
⁸ Department of Chemistry, Tufts University, 62 Talbot Avenue, Medford, MA, USA.
⁹ Heinrich-Heine-Universität Düsseldorf, Institut für Physikalische Biologie, Düsseldorf, Germany.
¹⁰ Forschungszentrum Jülich, Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Jülich, Germany.
¹¹ Molecular Cell Biology of Autophagy Laboratory, The Francis Crick Institute, London, UK.
¹² Department of Chemistry, University of Washington, Seattle, WA, USA.
¹³ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁴ Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
¹⁵ Institute for Protein Design, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
¹⁶ Department of Biochemistry, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
¹⁷ Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
¹⁸ Institute for Protein Design, University of Washington, Seattle, WA, USA. dimaio@uw.edu.
¹⁹ Department of Biochemistry, University of Washington, Seattle, WA, USA. dimaio@uw.edu.
²⁰ Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA. gauravb@uw.edu.
²¹ Institute for Protein Design, University of Washington, Seattle, WA, USA. gauravb@uw.edu.

^# Contributed equally.

PMID: 40542165
DOI: 10.1038/s41589-025-01929-w

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 (K_d < 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.

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Conflict of interest statement

Competing interests: D.W. is a cofounder of Priavoid and Attyloid. D.B. and G.B. are cofounders, advisors and shareholders of Vilya. The other authors declare no competing interests.

Update of

Accurate de novo design of high-affinity protein binding macrocycles using deep learning.
Rettie SA, Juergens D, Adebomi V, Bueso YF, Zhao Q, Leveille AN, Liu A, Bera AK, Wilms JA, Üffing A, Kang A, Brackenbrough E, Lamb M, Gerben SR, Murray A, Levine PM, Schneider M, Vasireddy V, Ovchinnikov S, Weiergräber OH, Willbold D, Kritzer JA, Mougous JD, Baker D, DiMaio F, Bhardwaj G. Rettie SA, et al. bioRxiv [Preprint]. 2024 Nov 18:2024.11.18.622547. doi: 10.1101/2024.11.18.622547. bioRxiv. 2024. Update in: Nat Chem Biol. 2025 Jun 20. doi: 10.1038/s41589-025-01929-w. PMID: 39605685 Free PMC article. Updated. Preprint.

References

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Accurate de novo design of high-affinity protein-binding macrocycles using deep learning

Affiliations

Accurate de novo design of high-affinity protein-binding macrocycles using deep learning

Authors

Affiliations

Abstract

Conflict of interest statement

Update of

References

Grants and funding

LinkOut - more resources

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