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Cyclic peptide structure prediction and design using AlphaFold

View ORCID ProfileStephen A. Rettie, View ORCID ProfileKatelyn V. Campbell, Asim K. Bera, Alex Kang, View ORCID ProfileSimon Kozlov, View ORCID ProfileJoshmyn De La Cruz, View ORCID ProfileVictor Adebomi, View ORCID ProfileGuangfeng Zhou, View ORCID ProfileFrank DiMaio, View ORCID ProfileSergey Ovchinnikov, View ORCID ProfileGaurav Bhardwaj
doi: https://doi.org/10.1101/2023.02.25.529956
Stephen A. Rettie
1Molecular and Cell Biology program, University of Washington, Seattle, WA, USA
2Institute for Protein Design, University of Washington, Seattle, WA, USA
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  • ORCID record for Stephen A. Rettie
Katelyn V. Campbell
2Institute for Protein Design, University of Washington, Seattle, WA, USA
3Department of Biochemistry, University of Washington, Seattle, WA, USA
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Asim K. Bera
2Institute for Protein Design, University of Washington, Seattle, WA, USA
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Alex Kang
2Institute for Protein Design, University of Washington, Seattle, WA, USA
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Simon Kozlov
6FAS Division of Science, Harvard University, Cambridge, MA, USA
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Joshmyn De La Cruz
2Institute for Protein Design, University of Washington, Seattle, WA, USA
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Victor Adebomi
2Institute for Protein Design, University of Washington, Seattle, WA, USA
4Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA
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Guangfeng Zhou
2Institute for Protein Design, University of Washington, Seattle, WA, USA
3Department of Biochemistry, University of Washington, Seattle, WA, USA
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Frank DiMaio
2Institute for Protein Design, University of Washington, Seattle, WA, USA
3Department of Biochemistry, University of Washington, Seattle, WA, USA
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Sergey Ovchinnikov
5John Harvard Distinguished Science Fellowship, Harvard University, Cambridge, MA, USA
6FAS Division of Science, Harvard University, Cambridge, MA, USA
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  • For correspondence: so@fas.harvard.edu gauravb@uw.edu
Gaurav Bhardwaj
1Molecular and Cell Biology program, University of Washington, Seattle, WA, USA
2Institute for Protein Design, University of Washington, Seattle, WA, USA
4Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA
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  • For correspondence: so@fas.harvard.edu gauravb@uw.edu
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ABSTRACT

Deep learning networks offer considerable opportunities for accurate structure prediction and design of biomolecules. While cyclic peptides have gained significant traction as a therapeutic modality, developing deep learning methods for designing such peptides has been slow, mostly due to the small number of available structures for molecules in this size range. Here, we report approaches to modify the AlphaFold network for accurate structure prediction and design of cyclic peptides. Our results show this approach can accurately predict the structures of native cyclic peptides from a single sequence, with 36 out of 49 cases predicted with high confidence (pLDDT > 0.85) matching the native structure with root mean squared deviation (RMSD) less than 1.5 Å. Further extending our approach, we describe computational methods for designing sequences of peptide backbones generated by other backbone sampling methods and for de novo design of new macrocyclic peptides. We extensively sampled the structural diversity of cyclic peptides between 7–13 amino acids, and identified around 10,000 unique design candidates predicted to fold into the designed structures with high confidence. X-ray crystal structures for seven sequences with diverse sizes and structures designed by our approach match very closely with the design models (root mean squared deviation < 1.0 Å), highlighting the atomic level accuracy in our approach. The computational methods and scaffolds developed here provide the basis for custom-designing peptides for targeted therapeutic applications.

Competing Interest Statement

GB is a co-founder, shareholder, and advisor for Vilya, a biotech company in Seattle, WA, USA.

Footnotes

  • https://github.com/sokrypton/ColabDesign/blob/main/af/examples/af_cyc_design.ipynb

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 26, 2023.
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Cyclic peptide structure prediction and design using AlphaFold
Stephen A. Rettie, Katelyn V. Campbell, Asim K. Bera, Alex Kang, Simon Kozlov, Joshmyn De La Cruz, Victor Adebomi, Guangfeng Zhou, Frank DiMaio, Sergey Ovchinnikov, Gaurav Bhardwaj
bioRxiv 2023.02.25.529956; doi: https://doi.org/10.1101/2023.02.25.529956
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Cyclic peptide structure prediction and design using AlphaFold
Stephen A. Rettie, Katelyn V. Campbell, Asim K. Bera, Alex Kang, Simon Kozlov, Joshmyn De La Cruz, Victor Adebomi, Guangfeng Zhou, Frank DiMaio, Sergey Ovchinnikov, Gaurav Bhardwaj
bioRxiv 2023.02.25.529956; doi: https://doi.org/10.1101/2023.02.25.529956

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