Evidence map›Paper›PMID 22566858›Full record

ArticleFrontiers in immunology2011

Conservation analysis of dengue virus T-cell epitope-based vaccine candidates using Peptide block entropy.

Lars Rønn Olsen, Guang Lan Zhang, Derin B Keskin, Ellis L Reinherz, Vladimir Brusic

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

17 citing papers in PubMed, 24 citations in OpenAlex.

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  14. Bioinformatics for cancer immunotherapy target discovery.Cancer immunology, immunotherapy : CII · 2014
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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

5 authors at 1 institution in 2 countries.

Lars Rønn OlsenCancer Vaccine Center, Dana-Farber Cancer Institute Boston, MA, USA.
Guang Lan Zhang
Derin B Keskin
Ellis L Reinherz
Vladimir Brusic
Technical University of Denmark · DK

Funding

Crossprotective CTL Against InfluenzaU01AI090043 · NIAID · DANA-FARBER CANCER INST · PI REINHERZ, ELLIS L · 2010 to 2015
$6.4M
NIAID NIH HHS U01 AI090043
6 · The paper itself

Abstract

Broad coverage of the pathogen population is particularly important when designing CD8+ T-cell epitope vaccines against viral pathogens. Traditional approaches are based on combinations of highly conserved T-cell epitopes. Peptide block entropy analysis is a novel approach for assembling sets of broadly covering antigens. Since T-cell epitopes are recognized as peptides rather than individual residues, this method is based on calculating the information content of blocks of peptides from a multiple sequence alignment of homologous proteins rather than using the information content of individual residues. The block entropy analysis provides broad coverage of variant antigens. We applied the block entropy analysis method to the proteomes of the four serotypes of dengue virus (DENV) and found 1,551 blocks of 9-mer peptides, which cover 99% of available sequences with five or fewer unique peptides. In contrast, the benchmark study by Khan et al. (2008) resulted in 165 conserved 9-mer peptides. Many of the conserved blocks are located consecutively in the proteins. Connecting these blocks resulted in 78 conserved regions. Of the 1551 blocks of 9-mer peptides 110 comprised predicted HLA binder sets. In total, 457 subunit peptides that encompass the diversity of all sequenced DENV strains of which 333 are T-cell epitope candidates.

Indexed as

antigenic diversityepitope-based vaccinesimmunoinformaticspolyvalent vaccinesreverse vaccinologyvaccine informatics

Identifiers

PMID22566858
PMCPMC3341948
OpenAlexW2104505071

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.