重组分枝杆菌PE_PGRS蛋白生物标志物的计算机分析、功能注释和表达

Avanthi Moodley-Reddy, Thamsanqa E. Chiliza, O. Pooe
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摘要

多年来,在治疗和诊断结核分枝杆菌(Mtb)方面取得了许多进展。近年来,耐药性的上升导致死亡率上升,特别是在较贫穷的国家。迫切需要新的治疗方案来对抗结核分枝杆菌。在结核分枝杆菌PE_PGRS蛋白中发现的一个基因家族在其生命周期中显示出作为药物靶点的潜力;防止潜伏结核分枝杆菌活化为活性结核分枝杆菌这些蛋白质的硅功能注释具有成本效益,避免了在没有潜力的蛋白质上浪费精力。以往的研究表明,PE_PGRS具有进一步研究的潜力。PE_PGRS17、PE_PGRS31、PE_PGRS50和PEPGRS54是最有希望的蛋白生物标志物。在Mycobrowser软件上检索这些蛋白的序列。通过将这些序列输入各种计算算法来设计结果。PE_PGRS17具有潜在候选疫苗的特性。考虑到这一结果,我们对重组PE_PGRS17 Mtb蛋白生物标志物进行了表达谱分析和纯化。使用各种在线软件算法计算结果。许多表征和其他方面的预测,以了解这些蛋白质的稳定性,定位和功能。据估计,所有的蛋白质都能产生免疫反应或参与免疫过程。优化了重组PE_PGRS17的表达条件,确定了以大肠杆菌为宿主细胞的纯化策略。这些发现使得这些蛋白,特别是PE_PGRS17,受到进一步科学研究的欢迎。蛋白质的预测结构、蛋白-蛋白相互作用和抗原特性可评估蛋白质是否可作为药物/疫苗靶点用于进一步研究。最终,根据其结构,PE_PGRS17被认为是最稳定的,它更有希望成为结核病研究的关键因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational Analysis, in silico Functional Annotation and Expression of Recombinant mycobacterium, PE_PGRS Protein Biomarkers
Over the years, there have been many advances made within the treatment and diagnosis of Mycobacterium Tuberculosis (Mtb). In recent times, the rise of drug resistance has led to higher mortality rates specifically in poorer countries. There is an urgent need for novel treatment regimens to work against Mtb. A gene family, found in Mtb, PE_PGRS proteins shows potential as a drug target in its life cycle; to prevent the activation of latent Mtb to active Mtb. In silico functional annotations of these proteins are cost-effective and avoid wasting effort on a protein with no potential. Previous studies indicated that the PE_PGRS showed potential for further research. The protein biomarkers which showed the most promise was identified as PE_PGRS17, PE_PGRS31, PE_PGRS50 and PEPGRS54. The sequences of these proteins were searched on the Mycobrowser software. Results were designed by entering these sequences into various computational algorithms. PE_PGRS17 showed characteristics of a potential vaccine candidate. Considering this result, expression profiling and purification was conducted on the recombinant PE_PGRS17 Mtb protein biomarker. The results were calculated using various online software algorithms. Many characterizations and other aspects were predicted to understand the stability, localization, and function of these proteins. All the proteins are estimated to produce an immune response or be involved in the process of immunity. The expression condition of the recombinant PE_PGRS17 was optimised, and purification strategy achieved using E.coli as a host cell. These findings make these proteins, specifically PE_PGRS17, popular for further scientific study. The predicted structures, protein-protein interaction and antigenic properties of the proteins estimate whether the protein can be used for further studies specifically as drug/vaccine targets. Ultimately PE_PGRS17 is seen as the most stable according to its structure and it holds more promise as a key factor to tuberculosis studies.
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