Evidence map›Paper›PMID 22061265›Full record

ArticleJournal of molecular biology2012

Computational design of high-affinity epitope scaffolds by backbone grafting of a linear epitope.

Mihai L Azoitei, Yih-En Andrew Ban, Jean-Philippe Julien, Steve Bryson, Alexandria Schroeter, Oleksandr Kalyuzhniy, Justin R Porter, Yumiko Adachi, David Baker, Emil F Pai and 1 more

Open access · bronzeAbstract read
In one paragraph

Article in Journal of molecular biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed
7.6field-weighted citation impact, top 2% of its field
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

61 citing papers in PubMed, 102 citations in OpenAlex.

  1. Deep learning-enabled scaffolding of spatial arrays of PfCSP epitopes.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026
    Review
  4. AntiBMPNN: Structure-Guided Graph Neural Networks for Precision Antibody Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Adaptation-proof SARS-CoV-2 vaccine design.Advanced functional materials · 2022
    Article
  11. Review
  12. Toward rational vaccine engineering.Advanced drug delivery reviews · 2022
    Review
  13. Protein Assembly by Design.Chemical reviews · 2021
    Review
  14. Article
  15. Review
  16. Review
  17. Review
  18. Article
  19. Review
  20. Article

1 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 5 institutions in 2 countries.

Mihai L AzoiteiDepartment of Biochemistry, University of Washington, Seattle, WA 98195, USA.
Yih-En Andrew Ban
Jean-Philippe Julien
Steve Bryson
Alexandria Schroeter
Oleksandr Kalyuzhniy
Justin R Porter
Yumiko Adachi
David Baker
Emil F Pai
William R Schief
University of Washington · USOntario Institute for Cancer Research · CAJohns Hopkins University · USScripps Research Institute · USUniversity of Toronto · CA

Funding

Canadian Institutes of Health Research
6 · The paper itself

Abstract

Computational grafting of functional motifs onto scaffold proteins is a promising way to engineer novel proteins with pre-specified functionalities. Typically, protein grafting involves the transplantation of protein side chains from a functional motif onto structurally homologous regions of scaffold proteins. Using this approach, we previously transplanted the human immunodeficiency virus 2F5 and 4E10 epitopes onto heterologous proteins to design novel "epitope-scaffold" antigens. However, side-chain grafting is limited by the availability of scaffolds with compatible backbone for a given epitope structure and offers no route to modify backbone structure to improve mimicry or binding affinity. To address this, we report here a new and more aggressive computational method-backbone grafting of linear motifs-that transplants the backbone and side chains of linear functional motifs onto scaffold proteins. To test this method, we first used side-chain grafting to design new 2F5 epitope scaffolds with improved biophysical characteristics. We then independently transplanted the 2F5 epitope onto three of the same parent scaffolds using the newly developed backbone grafting procedure. Crystal structures of side-chain and backbone grafting designs showed close agreement with both the computational models and the desired epitope structure. In two cases, backbone grafting scaffolds bound antibody 2F5 with 30- and 9-fold higher affinity than corresponding side-chain grafting designs. These results demonstrate that flexible backbone methods for epitope grafting can significantly improve binding affinities over those achieved by fixed backbone methods alone. Backbone grafting of linear motifs is a general method to transplant functional motifs when backbone remodeling of the target scaffold is necessary.

Indexed as

Amino Acid MotifsAmino Acid SequenceAntibodies, MonoclonalAntibody AffinityAntigens, HeterophileBinding Sites, AntibodyBroadly Neutralizing AntibodiesComputer SimulationCrystallography, X-RayEpitopesHIV AntibodiesHumansModels, MolecularMolecular Sequence DataProtein BindingProtein Engineering2F5 monoclonal antibodyAntibodies, MonoclonalAntigens, HeterophileBroadly Neutralizing AntibodiesEpitopesHIV Antibodies

Identifiers

PMID22061265
PMCPMC7105911
OpenAlexW2109339916

What OpenQuestion holds

Textmetadata
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.