Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 499

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

A robust pipeline for rapid production of versatile nanobody repertoires

Open access
Fridy, Peter C, Li, Yinyin, Keegan, Sarah, Thompson, Mary K, Nudelman, Ilona, Scheid, Johannes F, Oeffinger, Marlene, Nussenzweig, Michel C, Fenyö, David, Chait, Brian T, Rout, Michael P·2014·Nature Methods

Abstract

Nanobodies are single-domain antibodies derived from the variable regions of Camelidae atypical immunoglobulins. They show promise as high-affinity reagents for research, diagnostics and therapeutics owing to their high specificity, small size (∼15 kDa) and straightforward bacterial expression. However, identification of repertoires with sufficiently high affinity has proven time consuming and difficult, hampering nanobody implementation. Our approach generates large repertoires of readily expressible recombinant nanobodies with high affinities and specificities against a given antigen. We demonstrate the efficacy of this approach through the production of large repertoires of nanobodies against two antigens, GFP and mCherry, with Kd values into the subnanomolar range. After mapping diverse epitopes on GFP, we were also able to design ultrahigh-affinity dimeric nanobodies with Kd values as low as ∼30 pM. The approach presented here is well suited for the routine production of high-affinity capture reagents for various biomedical applications.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/nmeth.3170

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 499

Metadata

Authors
Fridy, P. C., Li, Y., Keegan, S., Thompson, M. K., Nudelman, I., Scheid, J. F., Oeffinger, M., Nussenzweig, M. C., Fenyö, D., Chait, B. T., & Rout, M. P.
Venue
Nature Methods
Published
2014-11-02
Volume
11
Issue
12
Pages
1253-1260
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
OpenAlex
W2087904752
Cited by
499
References
5
FWCI
18.76
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.