Evidence map›Paper›PMID 38535584›Full record

ReviewPathogens (Basel, Switzerland)2024

"It's Only a Model": When Protein Structure Predictions Need Experimental Validation, the Case of the HTLV-1 Tax Protein.

Christophe Guillon, Xavier Robert, Patrice Gouet

Open access · goldAbstract readReview
In one paragraph

Review in Pathogens (Basel, Switzerland), 2024. 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
0.7field-weighted citation impact, top 32% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  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

3 authors at 1 institution in 1 country.

Christophe GuillonRetroviruses and Structural Biochemistry Team, Molecular Microbiology and Structural Biochemistry, UMR 5086 CNRS-Lyon 1, CNRS, Université de Lyon, 69007 Lyon, France.ORCID 0000-0001-6378-3303
Xavier RobertRetroviruses and Structural Biochemistry Team, Molecular Microbiology and Structural Biochemistry, UMR 5086 CNRS-Lyon 1, CNRS, Université de Lyon, 69007 Lyon, France.ORCID 0000-0002-6174-8595
Patrice GouetRetroviruses and Structural Biochemistry Team, Molecular Microbiology and Structural Biochemistry, UMR 5086 CNRS-Lyon 1, CNRS, Université de Lyon, 69007 Lyon, France.
Université Claude Bernard Lyon 1 · FR

Funding

La Ligue Contre le Cancer R23GOUET
6 · The paper itself

Abstract

Human T-cell Leukemia Virus type 1 (HTLV-1) is a human retrovirus responsible for leukaemia in 5 to 10% of infected individuals. Among the viral proteins, Tax has been described as directly involved in virus-induced leukemogenesis. Tax is therefore an interesting therapeutic target. However, its 3D structure is still unknown and this hampers the development of drug-design-based therapeutic strategies. Several algorithms are available that can be used to predict the structure of proteins, particularly with the recent appearance of artificial intelligence (AI)-driven pipelines. Here, we review how the structure of Tax is predicted by several algorithms using distinct modelling strategies. We discuss the consequences for the understanding of Tax structure/function relationship, and more generally for the use of structure models for modular and/or flexible proteins, which are frequent in retroviruses.

Indexed as

AlgorithmsGene Products, taxHuman T-lymphotropic virus 1Artificial IntelligenceHTLV-I InfectionsHumansModels, MolecularProtein ConformationGene Products, taxtax protein, Human T-lymphotrophic virus 1HTLV-1modelpredictionstructureTax

Identifiers

PMID38535584
PMCPMC10976231
OpenAlexW4392600260

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.