Cyclic peptide structure prediction and design using AlphaFold.

Journal

bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187

Informations de publication

Date de publication:
26 Feb 2023
Historique:
pubmed: 4 3 2023
medline: 4 3 2023
entrez: 3 3 2023
Statut: epublish

Résumé

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

Identifiants

DOI: 10.1101/2023.02.25.529956 PMID: 36865323 PMC: PMC9980166
pubmed: 36865323
doi: 10.1101/2023.02.25.529956
pmc: PMC9980166
pii:
doi:

Types de publication

Preprint

Langues

eng

Déclaration de conflit d'intérêts

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

Auteurs

Stephen A Rettie (SA)

Molecular and Cell Biology program, University of Washington, Seattle, WA, USA.
Institute for Protein Design, University of Washington, Seattle, WA, USA.

Katelyn V Campbell (KV)

Institute for Protein Design, University of Washington, Seattle, WA, USA.
Department of Biochemistry, University of Washington, Seattle, WA, USA.

Asim K Bera (AK)

Institute for Protein Design, University of Washington, Seattle, WA, USA.

Alex Kang (A)

Institute for Protein Design, University of Washington, Seattle, WA, USA.

Simon Kozlov (S)

FAS Division of Science, Harvard University, Cambridge, MA, USA.

Joshmyn De La Cruz (J)

Institute for Protein Design, University of Washington, Seattle, WA, USA.

Victor Adebomi (V)

Institute for Protein Design, University of Washington, Seattle, WA, USA.
Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.

Guangfeng Zhou (G)

Institute for Protein Design, University of Washington, Seattle, WA, USA.
Department of Biochemistry, University of Washington, Seattle, WA, USA.

Frank DiMaio (F)

Institute for Protein Design, University of Washington, Seattle, WA, USA.
Department of Biochemistry, University of Washington, Seattle, WA, USA.

Sergey Ovchinnikov (S)

John Harvard Distinguished Science Fellowship, Harvard University, Cambridge, MA, USA.
FAS Division of Science, Harvard University, Cambridge, MA, USA.

Gaurav Bhardwaj (G)

Molecular and Cell Biology program, University of Washington, Seattle, WA, USA.
Institute for Protein Design, University of Washington, Seattle, WA, USA.
Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.

Classifications MeSH