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

BACKGROUND: Pneumonia 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 determinat...

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Autores principales: Bakare, Olalekan Olanrewaju, Keyster, Marshall, Pretorius, Ashley
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678116/
https://www.ncbi.nlm.nih.gov/pubmed/33218302
http://dx.doi.org/10.1186/s12860-020-00328-4
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author Bakare, Olalekan Olanrewaju
Keyster, Marshall
Pretorius, Ashley
author_facet Bakare, Olalekan Olanrewaju
Keyster, Marshall
Pretorius, Ashley
author_sort Bakare, Olalekan Olanrewaju
collection PubMed
description BACKGROUND: Pneumonia 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. RESULTS: The 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. CONCLUSIONS: The 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.
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spelling pubmed-76781162020-11-20 Identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches Bakare, Olalekan Olanrewaju Keyster, Marshall Pretorius, Ashley BMC Mol Cell Biol Research Article BACKGROUND: Pneumonia 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. RESULTS: The 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. CONCLUSIONS: The 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. BioMed Central 2020-11-20 /pmc/articles/PMC7678116/ /pubmed/33218302 http://dx.doi.org/10.1186/s12860-020-00328-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Bakare, Olalekan Olanrewaju
Keyster, Marshall
Pretorius, Ashley
Identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches
title Identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches
title_full Identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches
title_fullStr Identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches
title_full_unstemmed Identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches
title_short Identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches
title_sort identification of biomarkers for the accurate and sensitive diagnosis of three bacterial pneumonia pathogens using in silico approaches
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678116/
https://www.ncbi.nlm.nih.gov/pubmed/33218302
http://dx.doi.org/10.1186/s12860-020-00328-4
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