Evidence map›Paper›PMID 34663864›Full record

ArticleScientific reports2021

Building HMM and molecular docking analysis for the sensitive detection of anti-viral pneumonia antimicrobial peptides (AMPs).

Olalekan Olanrewaju Bakare, Marshall Keyster, Ashley Pretorius

Abstract read
In one paragraph

Article in Scientific reports, 2021. 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

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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

2 citing papers in PubMed.

  1. Article
  2. Frontiers in bioinformatics · 2022
    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.

Olalekan Olanrewaju BakareBioinformatics Research Group, University of the Western Cape, Cape Town, 7535, South Africa. lekanbakare77@gmail.com.
Marshall KeysterEnvironmental Biotechnology Laboratory, Biotechnology Department, University of the Western Cape, Cape Town, 7535, South Africa.
Ashley PretoriusBioinformatics Research Group, University of the Western Cape, Cape Town, 7535, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pneumonia is the main reason for mortality among children under five years, causing 1.6 million deaths every year; late research has exhibited that mortality is increasing in the elderly. A few biomarkers used for its diagnosis need specificity and precision, as they are related to different infections, for example, pulmonary tuberculosis and Human Immunodeficiency Virus. There is a quest for new biomarkers worldwide to diagnose the disease to defeat these previously mentioned constraints. Antimicrobial peptides (AMPs) are promising indicative specialists against infection. This research work used AMPs as biomarkers to detect viral pneumonia pathogens, for example, Respiratory syncytial virus, Influenza A and B viruses utilizing in silico technologies, such as Hidden Markov Model (HMMER). HMMER was used to distinguish putative anti-viral pneumonia AMPs against the recognized receptor proteins of Respiratory syncytial virus, Influenza A, and B viruses. The physicochemical parameters of these putative AMPs were analyzed, and their 3-D structures were determined utilizing I-TASSER. Molecular docking interaction of these AMPs against the recognized viral pneumonia proteins was carried out using the PATCHDOCK and HDock servers. The results demonstrated 27 anti-viral AMPs ranked based on their E values with significant physicochemical parameters in similarity with known experimentally approved AMPs. The AMPs additionally had a high anticipated binding potential to the pneumonia receptors of these microorganisms sensitively. The tendency of the putative anti-viral AMPs to bind pneumonia proteins showed that they would be promising applicant biomarkers to identify these viral microorganisms in the point-of-care (POC) pneumonia diagnostics. The high precision observed for the AMPs legitimizes HMM's utilization in the disease diagnostics' discovery process.

Indexed as

Antimicrobial PeptidesAntiviral AgentsBiomarkersComputational BiologyComputer SimulationDatabases, FactualHumansInfluenza A virusInfluenza B virusMarkov ChainsMolecular Docking SimulationPneumonia, ViralProtein BindingRespiratory Syncytial VirusesAntimicrobial PeptidesAntiviral AgentsBiomarkers

Identifiers

PMID34663864
PMCPMC8523717

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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.