Evidence map›Paper›PMID 25260513›Full record

ArticleBMC bioinformatics2014

Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes.

Brian G Pierce, Thom Vreven, Zhiping Weng

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 30% 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

4 citing papers in PubMed, 11 citations in OpenAlex.

  1. Three-Dimensional Modeling ofAntibodies (Basel, Switzerland) · 2025
    Article
  2. How an alloreactive T-cell receptor achieves peptide and MHC specificity.Proceedings of the National Academy of Sciences of the United States of America · 2017
    Article
  3. Article
  4. Article
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

3 authors at 1 institution in 1 country.

Brian G PierceProgram in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USA. pierce@umd.edu.
Thom Vreven
Zhiping Weng
University of Massachusetts Chan Medical School · US

Funding

Development of a Computational Framework for TCR EngineeringR01GM103773 · NIGMS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI BAKER, BRIAN M, WENG, ZHIPING · 2013 to 2016
$1.2M
NIGMS NIH HHS GM103773NIGMS NIH HHS R01 GM103773
6 · The paper itself

Abstract

backgroundT cell receptors (TCRs) can recognize diverse lipid and metabolite antigens presented by MHC-like molecules CD1 and MR1, and the molecular basis of many of these interactions has not been determined. Here we applied our protein docking algorithm TCRFlexDock, previously developed to perform docking of TCRs to peptide-MHC (pMHC) molecules, to predict the binding of αβ and γδ TCRs to CD1 and MR1, starting with the structures of the unbound molecules.

resultsEvaluating against TCR-CD1d complexes with crystal structures, we achieved near-native structures in the top 20 models for two out of four cases, and an acceptable-rated prediction for a third case. We also predicted the structure of an interaction between a MAIT TCR and MR1-antigen that has not been structurally characterized, yielding a top-ranked model that agreed remarkably with a characterized TCR-MR1-antigen structure that has a nearly identical TCR α chain but a different β chain, highlighting the likely dominance of the conserved α chain in MR1-antigen recognition. Docking performance was improved by re-training our scoring function with a set of TCR-pMHC complexes, and for a case with an outlier binding mode, we found that alternative docking start positions improved predictive accuracy. We then performed unbound docking with two mycolyl-lipid specific TCRs that recognize lipid-bound CD1b, which represent a class of interactions that is not structurally characterized. Highly-ranked models of these complexes showed remarkable agreement between their binding topologies, as expected based on their shared germline sequences, while differences in residue-level interactions with their respective antigens point to possible mechanisms underlying their distinct specificities.

conclusionsTogether these results indicate that flexible docking simulations can provide accurate models and atomic-level insights into TCR recognition of MHC-like molecules presenting lipid and other small molecule antigens.

Indexed as

AlgorithmsAnimalsAntigens, CD1Antigens, CD1dGalactosylceramidesHumansMolecular Docking SimulationReceptors, Antigen, T-Cell, alpha-betaReceptors, Antigen, T-Cell, gamma-deltaVitaminsAntigens, CD1Antigens, CD1dCD1b antigenGalactosylceramidesReceptors, Antigen, T-Cell, alpha-betaReceptors, Antigen, T-Cell, gamma-deltaVitamins

Identifiers

PMID25260513
PMCPMC4261541
OpenAlexW2148194798

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.