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

Abstract

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.

Analytical Studies of Antimicrobial Peptides as Diagnostic Biomarkers for the Detection of Bacterial and Viral Pneumonia

“…Antimicrobial peptides have shown tremendous potential in circumventing the drawbacks of these systems and appear to be the favorite choice in diagnostic development due to their numerous properties that overcome these shortcomings, such as their small size, ease of modification, high stability, and minor/non-toxicity. The HMMER machine learning tool was employed to construct predictive models to identify sensitive and specific AMPs against Streptococcus pneumonia, Klebsiella pneumonia, Acinetobacter baumannii, respiratory syncytial virus, and the influenza A and B viruses in our previous studies [ 15 , 16 ]. This research employed the predicted AMPs as parental peptides to develop derivative AMPs using site-directed mutagenesis.…”

“…The docking interaction analysis of the antibacterial and antiviral pneumonia AMPs was carried out against their respective protein receptors, as identified in our previous study [ 15 ]. The online protein–protein interaction server PATCHDOCK was employed for this analysis [ 23 ].…”

“…In our previous studies, 19 antibacterial and 27 antiviral novel AMPs were identified using an in silico mathematical algorithm, HMMER, a machine learning tool whose codes are written in Figure 1 , to identify three bacterial [ 15 ] and viral pathogens [ 16 ], respectively, in which the binding of these AMPs to the pneumonia pathogen receptors identified was carried out. Determining the amino acid residues within the binding interface that contribute the most to the binding affinity, known as hotspot residues, is imperative.…”

PR-1-Like Protein as a Potential Target for the Identification of Fusarium oxysporum: An In Silico Approach

“…The isoelectric point (pI) of peptides is a component of individual amino acids in both original structures. A negative Boman index is said to be related to a more hydrophobic peptide, demonstrating a high protein binding potential, while a more hydrophilic peptide will, in general, have a more positive index [ 56 ]. In any case, the propensity of certain peptides to be positive in their Boman index values has been associated with the capacity to identify HIV in a lateral flow device [ 17 ].…”

Biomedical Relevance of Novel Anticancer Peptides in the Sensitive Treatment of Cancer

“…Molecular dynamic (MD) approaches have been adopted to study the relationship between the biological function and mechanism of HDPs to optimize these antibiotic candidates. In silico technologies, such as HMMER, with molecular validation techniques have also been used to explore the use of novel HDPs as diagnostic candidates against three bacterial pneumonias and HIV with great promise for industrial collaborations in a lateral flow device [83,84].…”