Evidence map›Paper›PMID 32124321›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2020

Modeling and Viewing T Cell Receptors Using TCRmodel and TCR3d.

Ragul Gowthaman, Brian G Pierce

Abstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Ragul GowthamanInstitute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, USA.
Brian G PierceInstitute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, USA. pierce@umd.edu.

Funding

High Resolution Modeling and Design of T-Cell ReceptorsR01GM126299 · NIGMS · UNIV OF MARYLAND, COLLEGE PARK · PI PIERCE, BRIAN G. · 2018 to 2021
$1.3M
NIGMS NIH HHS R01 GM126299
6 · The paper itself

Abstract

The past decade has seen a rapid increase in T cell receptor (TCR) sequences from single cell cloning and repertoire-scale high throughput sequencing studies. Many of these TCRs are of interest as potential therapeutics or for their implications in autoimmune disease or effective targeting of pathogens. As it is impractical to characterize the structure or targeting of the vast majority of these TCRs experimentally, advanced computational methods have been developed to predict their 3D structures and gain mechanistic insights into their antigen binding and specificity. Here, we describe the use of a TCR modeling web server, TCRmodel, which generates models of TCRs from sequence, and TCR3d, which is a weekly-updated database of all known TCR structures. Additionally, we describe the use of RosettaTCR, which is a protocol implemented in the Rosetta framework that serves as the command-line backend to TCRmodel. We provide an example where these tools are used to analyze and model a therapeutically relevant TCR based on its amino acid sequence.

Indexed as

Amino Acid SequenceAnimalsDatabases, ProteinHumansModels, MolecularProtein ConformationReceptors, Antigen, T-CellSoftwareReceptors, Antigen, T-CellAntigenBioinformaticsImmunologyMHCRosettaTCR

Identifiers

PMID32124321
PMCPMC7224193

What OpenQuestion holds

Textmetadata
LicenceTDM
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.