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

Abstract

: 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.