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. 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. 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.

中文翻译：

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使用计算机方法鉴定用于准确和灵敏地诊断三种细菌性肺炎病原体的生物标志物

肺炎是5岁以下儿童中主要的死亡传染源之一，每天造成2500人死亡。后期研究还表明，老年人的死亡率较高。到目前为止，由于其确定性而被区分的一些生物标记物缺乏特异性，因为这些生物标记物未能在肺炎和其他相关疾病（例如肺结核和人类免疫缺陷病毒感染（HIV））之间建立区分。对于新的生物标记物的利用，人们对肺炎感染的认识得到了广泛的共识，这些新的生物标记物是根据肺炎的感染情况进行精确鉴定以克服这些先前提到的限制因素。抗菌肽（AMPs）已被证明是针对多种疾病的有希望的补救专家。这项研究工作试图使用计算机技术将AMPs鉴定为三种细菌性肺炎病原体（如肺炎链球菌，肺炎克雷伯菌，鲍曼不动杆菌）的生物标记。使用隐马尔可夫模型（HMMER）来针对假定的肺炎链球菌，肺炎克雷伯菌和鲍曼不动杆菌的受体蛋白鉴定抗菌性肺炎AMP。计算这些推定AMP的理化参数，并使用I-TASSER预测其3-D结构。随后使用PATCHDOCK对这些AMPs与已鉴定的细菌性肺炎病原体蛋白进行对接相互作用分析。电脑分析结果显示，有18种抗菌AMP基于其E值进行排序，并具有明显的理化参数，与已知的经过实验验证的AMP一致。AMP还在细胞外区域敏感地结合其各自病原体的肺炎受体。这些AMPs结合肺炎病原体蛋白的倾向证明，它们是在即时医疗点POC肺炎诊断中可以识别这些细菌病原体的潜在申请人生物标志物。AMP的高灵敏度，准确性和特异性同样证明了在设计和发现用于疾病诊断和治疗的AMP中使用HMMER是合理的。这些AMPs结合肺炎病原体蛋白的倾向证明，它们是在即时医疗点POC肺炎诊断中可识别这些细菌病原体的潜在申请人生物标志物。AMP的高灵敏度，准确性和特异性同样证明了在设计和发现用于疾病诊断和治疗的AMP中使用HMMER是合理的。这些AMPs结合肺炎病原体蛋白的倾向证明，它们是在即时医疗点POC肺炎诊断中可识别这些细菌病原体的潜在申请人生物标志物。AMP的高灵敏度，准确性和特异性同样证明了在设计和发现用于疾病诊断和治疗的AMP中使用HMMER是合理的。