Evidence map›Paper›PMID 36865323›Full record

ArticlebioRxiv : the preprint server for biology2023

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 and 1 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 79 citations in OpenAlex.

  1. Heuristic energy-based cyclic peptide design.PLoS computational biology · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Stephen A RettieMolecular and Cell Biology program, University of Washington, Seattle, WA, USA.
Katelyn V CampbellInstitute for Protein Design, University of Washington, Seattle, WA, USA.
Asim K BeraInstitute for Protein Design, University of Washington, Seattle, WA, USA.
Alex KangInstitute for Protein Design, University of Washington, Seattle, WA, USA.
Simon KozlovFAS Division of Science, Harvard University, Cambridge, MA, USA.
Joshmyn De La CruzInstitute for Protein Design, University of Washington, Seattle, WA, USA.
Victor AdebomiInstitute for Protein Design, University of Washington, Seattle, WA, USA.
Guangfeng ZhouInstitute for Protein Design, University of Washington, Seattle, WA, USA.
Frank DiMaioInstitute for Protein Design, University of Washington, Seattle, WA, USA.
Sergey OvchinnikovJohn Harvard Distinguished Science Fellowship, Harvard University, Cambridge, MA, USA.
Gaurav BhardwajMolecular and Cell Biology program, University of Washington, Seattle, WA, USA.
University of Washington · USHarvard University · US

Funding

User Training and OutreachP30GM124165 · NIGMS · CORNELL UNIVERSITY · PI Nozomi Ando, J Christopher Fromme · 2018 to 2026
$34.2M
Exploring the unknown protein universe using evolutionary informationDP5OD026389 · OD · HARVARD UNIVERSITY · PI OVCHINNIKOV, SERGEY L. · 2018 to 2022
$2.0M
NIGMS NIH HHS P30 GM124165NIH HHS DP5 OD026389
6 · The paper itself

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

Identifiers

PMID36865323
PMCPMC9980166
OpenAlexW4322207749

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.