Evidence map›Paper›PMID 33218302›Full record

ArticleBMC molecular and cell biology2020

Identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches.

Olalekan Olanrewaju Bakare, Marshall Keyster, Ashley Pretorius

Open access · goldAbstract read
In one paragraph

Article in BMC molecular and cell biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

7 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
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  4. Frontiers in bioinformatics · 2022
    Article
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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

3 authors at 1 institution in 1 country.

Olalekan Olanrewaju BakareBioinformatics Research Group, Biotechnology Department, University of the Western Cape, Cape Town, 7535, South Africa. lekanbakare77@gmail.com.ORCID http://orcid.org/0000-0002-5856-8286
Marshall KeysterEnvironmental Biotechnology Laboratory, Biotechnology Department, University of the Western Cape, Cape Town, 7535, South Africa.
Ashley PretoriusBioinformatics Research Group, Biotechnology Department, University of the Western Cape, Cape Town, 7535, South Africa.
University of the Western Cape · ZA

Funding

South African Agency for Science and Technology Advancement 112332
6 · The paper itself

Abstract

backgroundPneumonia ranks as one of the main infectious sources of mortality among kids under 5 years of age, killing 2500 a day; late research has additionally demonstrated that mortality is higher in the elderly. A few biomarkers, which up to this point have been distinguished for its determination lack specificity, as these biomarkers fail to build up a differentiation between pneumonia and other related diseases, for example, pulmonary tuberculosis and Human Immunodeficiency Infection (HIV). There is an inclusive global consensus of an improved comprehension of the utilization of new biomarkers, which are delivered in light of pneumonia infection for precision identification to defeat these previously mentioned constraints. Antimicrobial peptides (AMPs) have been demonstrated to be promising remedial specialists against numerous illnesses. This research work sought to identify AMPs as biomarkers for three bacterial pneumonia pathogens such as Streptococcus pneumoniae, Klebsiella pneumoniae, Acinetobacter baumannii using in silico technology. Hidden Markov Models (HMMER) was used to identify putative anti-bacterial pneumonia AMPs against the identified receptor proteins of Streptococcus pneumoniae, Klebsiella pneumoniae, and Acinetobacter baumannii. The physicochemical parameters of these putative AMPs were computed and their 3-D structures were predicted using I-TASSER. These AMPs were subsequently subjected to docking interaction analysis against the identified bacterial pneumonia pathogen proteins using PATCHDOCK.

resultsThe in silico results showed 18 antibacterial AMPs which were ranked based on their E values with significant physicochemical parameters in conformity with known experimentally validated AMPs. The AMPs also bound the pneumonia receptors of their respective pathogens sensitively at the extracellular regions.

conclusionsThe propensity of these AMPs to bind pneumonia pathogens proteins justifies that they would be potential applicant biomarkers for the recognizable detection of these bacterial pathogens in a point-of-care POC pneumonia diagnostics. The high sensitivity, accuracy, and specificity of the AMPs likewise justify the utilization of HMMER in the design and discovery of AMPs for disease diagnostics and therapeutics.

Indexed as

Acinetobacter baumanniiAnti-Bacterial AgentsBiomarkersComputer SimulationDatabases, ChemicalHumansKlebsiella pneumoniaeLigandsMarkov ChainsMolecular Docking SimulationPneumonia, BacterialPore Forming Cytotoxic ProteinsProtein Conformation, alpha-HelicalProtein Conformation, beta-StrandProteomeSoftwareAnti-Bacterial AgentsBiomarkersLigandsPore Forming Cytotoxic ProteinsProteomeAlgorithmsAntimicrobial peptidesBacteriaDatabasesDiagnosticsPathogensProtein and ligandsReceptors

Identifiers

PMID33218302
PMCPMC7678116
OpenAlexW3103494243

What OpenQuestion holds

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LicenceCC BY
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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.