Evidence map›Paper›PMID 21984903›Full record

ArticlePloS one2011

Identification of Plasmodium vivax proteins with potential role in invasion using sequence redundancy reduction and profile hidden Markov models.

Daniel Restrepo-Montoya, David Becerra, Juan G Carvajal-Patiño, Alvaro Mongui, Luis F Niño, Manuel E Patarroyo, Manuel A Patarroyo

Abstract read
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

5 citing papers in PubMed.

  1. Frontiers in microbiology · 2021
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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

7 authors.

Daniel Restrepo-MontoyaBioinformatics and Intelligent Systems Research Laboratory - BIOLISI, Universidad Nacional de Colombia, Bogotá D.C., Colombia.
David Becerra
Juan G Carvajal-Patiño
Alvaro Mongui
Luis F Niño
Manuel E Patarroyo
Manuel A Patarroyo

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study describes a bioinformatics approach designed to identify Plasmodium vivax proteins potentially involved in reticulocyte invasion. Specifically, different protein training sets were built and tuned based on different biological parameters, such as experimental evidence of secretion and/or involvement in invasion-related processes. A profile-based sequence method supported by hidden Markov models (HMMs) was then used to build classifiers to search for biologically-related proteins. The transcriptional profile of the P. vivax intra-erythrocyte developmental cycle was then screened using these classifiers.

resultsA bioinformatics methodology for identifying potentially secreted P. vivax proteins was designed using sequence redundancy reduction and probabilistic profiles. This methodology led to identifying a set of 45 proteins that are potentially secreted during the P. vivax intra-erythrocyte development cycle and could be involved in cell invasion. Thirteen of the 45 proteins have already been described as vaccine candidates; there is experimental evidence of protein expression for 7 of the 32 remaining ones, while no previous studies of expression, function or immunology have been carried out for the additional 25.

conclusionsThe results support the idea that probabilistic techniques like profile HMMs improve similarity searches. Also, different adjustments such as sequence redundancy reduction using Pisces or Cd-Hit allowed data clustering based on rational reproducible measurements. This kind of approach for selecting proteins with specific functions is highly important for supporting large-scale analyses that could aid in the identification of genes encoding potential new target antigens for vaccine development and drug design. The present study has led to targeting 32 proteins for further testing regarding their ability to induce protective immune responses against P. vivax malaria.

Indexed as

Markov ChainsModels, BiologicalComputational BiologyDatabases, ProteinPlasmodium vivaxProtozoan ProteinsSequence Analysis, ProteinProtozoan Proteins

Identifiers

PMID21984903
PMCPMC3184965

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LicenceCC BY
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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.