Evidence map›Paper›PMID 41415277›Full record

ArticleFrontiers in immunology2025

T-cell receptors that are

Hyunjin Park, Jonathan Krog, Brandon Carter, Pallavi A Balivada, Emily M Pogue, Samyuktha Anand, Michael E Birnbaum, David K Gifford

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Hyunjin ParkComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States.
Jonathan KrogDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
Brandon CarterComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States.
Pallavi A BalivadaDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
Emily M PogueDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
Samyuktha AnandKoch Institute for Integrative Cancer Research, Cambridge, MA, United States.
Michael E BirnbaumDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
David K GiffordComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States.

Funding

Deep learning based antibody design using high-throughput affinity testing of synthetic sequencesR01CA218094 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GIFFORD, DAVID K · 2018 to 2024
$2.9M
NCI NIH HHS R01 CA218094
6 · The paper itself

Abstract

Previous studies have revealed that individual T cell receptors (TCRs) can recognize a diverse set of peptide targets displayed by Major Histocompatibility Complexes (MHCs) to enable effective adaptive immune surveillance. However, how TCR sequences encode their cross-reactivity remains poorly understood. Here, we used an

Indexed as

Receptors, Antigen, T-CellAmino Acid SequenceComplementarity Determining RegionsCross ReactionsHLA-A2 AntigenHumansMachine LearningPeptidesProtein BindingComplementarity Determining RegionsHLA-A*02:01 antigenHLA-A2 AntigenPeptidesReceptors, Antigen, T-CellHLA-A*02:01HLA-A allotypeimmunological specificityk-bindingMHC class IT-cell receptorTCRTCR cross-reactivity

Identifiers

PMID41415277
PMCPMC12709673

What OpenQuestion holds

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Registered trials

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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.