Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 37

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

Computational stabilization of T cell receptors allows pairing with antibodies to form bispecifics

Froning, Karen, Maguire, Jack, Sereno, Arlene, Huang, Flora, Chang, Shawn, Weichert, Kenneth, Frommelt, Anton J., Dong, Jessica, Wu, Xiufeng, Austin, Heather, Conner, Elaine M., Fitchett, Jonathan R., Heng, Aik Roy, Balasubramaniam, Deepa, Hilgers, Mark T., Kuhlman, Brian, Demarest, Stephen J.·2020·Nature Communications
DOI 10.1038/s41467-020-16231-7
Open this paper in CoChat — free account

Abstract

Recombinant T cell receptors (TCRs) can be used to redirect naïve T cells to eliminate virally infected or cancerous cells; however, they are plagued by low stability and uneven expression. Here, we use molecular modeling to identify mutations in the TCR constant domains (Cα/Cβ) that increase the unfolding temperature of Cα/Cβ by 20 °C, improve the expression of four separate α/β TCRs by 3- to 10-fold, and improve the assembly and stability of TCRs with poor intrinsic stability. The stabilizing mutations rescue the expression of TCRs destabilized through variable domain mutation. The improved stability and folding of the TCRs reduces glycosylation, perhaps through conformational stabilization that restricts access to N-linked glycosylation enzymes. The Cα/Cβ mutations enables antibody-like expression and assembly of well-behaved bispecific molecules that combine an anti-CD3 antibody with the stabilized TCR. These TCR/CD3 bispecifics can redirect T cells to kill tumor cells with target HLA/peptide on their surfaces in vitro.

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/s41467-020-16231-7

Cites

59 of 59 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 37

Metadata

Authors
Froning, K., Maguire, J., Sereno, A., Huang, F., Chang, S., Weichert, K., Frommelt, A. J., Dong, J., Wu, X., Austin, H., Conner, E. M., Fitchett, J. R., Heng, A. R., Balasubramaniam, D., Hilgers, M. T., Kuhlman, B., & Demarest, S. J.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-05-11
Volume
11
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-020-16231-7
Cited by
37
References
59
Language
en
Licence

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.