Citation: Bakare, O.O.; Gokul, A.;
Keyster, M. Analytical Studies of
Antimicrobial Peptides as Diagnostic
Biomarkers for the Detection of
Bacterial and Viral Pneumonia.
Bioengineering 2022, 9, 305. https://
doi.org/10.3390/bioengineering9070305
Academic Editors: Giuseppe
Cesarelli, Carlo Ricciardi,
Leandro Donisi and Alfonso
Maria Ponsiglione
Received: 24 May 2022
Accepted: 29 June 2022
Published: 11 July 2022
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bioengineering
Article
Analytical Studies of Antimicrobial Peptides as Diagnostic
Biomarkers for the Detection of Bacterial and Viral Pneumonia
Olalekan Olanrewaju Bakare
1,2,
* , Arun Gokul
1,3
and Marshall Keyster
1
1
Environmental Biotechnology Laboratory (EBL), Department of Biotechnology, University of the Western
Cape, Cape Town 7535, South Africa; agokul5@gmail.com (A.G.); mkeyster@uwc.ac.za (M.K.)
2
Department of Biochemistry, Faculty of Basic Medical Sciences, Olabisi Onabanjo University,
Sagamu 120107, Ogun State, Nigeria
3
Department of Plant Sciences, Qwaqwa Campus, University of the Free State,
Phuthadithjaba 9866, South Africa
* Correspondence: lekanbakare77@gmail.com; Tel.: +27-603112776
Abstract: Pneumonia remains one of the leading causes of infectious mortality and significant
economic losses among our growing population. The lack of specific biomarkers for correct and timely
diagnosis to detect patients’ status is a bane towards initiating a proper treatment plan for the disease;
thus, current biomarkers cannot distinguish between pneumonia and other associated conditions
such as atherosclerotic plaques and human immunodeficiency virus (HIV). Antimicrobial peptides
(AMPs) are potential candidates for detecting numerous illnesses due to their compensatory roles as
theranostic molecules. This research sought to generate specific data for parental AMPs to identify
viral and bacterial pneumonia pathogens using in silico technology. The parental antimicrobial
peptides (AMPs) used in this work were AMPs discovered in our previous in silico analyses using
the HMMER algorithm, which were used to generate derivative (mutated) AMPs that would bind
with greater affinity, in order to detect the bacterial and viral receptors using an in silico site-directed
mutagenesis approach. These AMPs’ 3D structures were subsequently predicted and docked against
receptor proteins. The result shows putative AMPs with the potential capacity to detect pneumonia
caused by these pathogens through their binding precision with high sensitivity, accuracy, and
specificity for possible use in point-of-care diagnosis. These peptides’ tendency to detect receptor
proteins of viral and bacterial pneumonia with precision justifies their use for differential diagnostics,
in an attempt to reduce the problems of indiscriminate overuse, toxicity due to the wrong prescription,
bacterial resistance, and the scarcity and high cost of existing pneumonia antibiotics.
Keywords: antimicrobial peptides; bacteria; viruses; databases; machine learning tool; diagnostics;
receptors
1. Introduction
Pneumonia is challenging because of its significant impact on global health [1]. It is
characterized by specific infections with a distinct clinical source, presentation, epidemi-
ology, and pathogenesis [2]. The disease arises from an infection in the lower respiratory
tract, starting with inflammation and impaired tissue functioning [3]. Diagnosis of the
disease may be difficult for an established respiratory condition, cardiac failure, and
non-pneumonic respiratory infection because there is no evidence-based consensus on its
clinical signs and predictive symptoms [4]. The diagnostic biomarkers used at present
are produced in response to other conditions such as HIV and atherosclerosis [5]. The
use of antibodies has remained questionable despite its high sensitivity due to challenges
such as cross-reactivity [6]. The discovery of more specific and sensitive biomarkers for
detecting pneumonia in patients’ samples is an ultimate goal of researchers to reduce the
high mortality arising from the disease.
Bioengineering 2022, 9, 305. https://doi.org/10.3390/bioengineering9070305 https://www.mdpi.com/journal/bioengineering