结核分枝杆菌中发现的重组PE_PGRS蛋白生物标志物的计算分析、计算机功能注释和表达

Q3 Computer Science Open Bioinformatics Journal Pub Date : 2023-04-05 DOI:10.2174/18750362-v16-e230306-2022-6
Avanthi Moodley-Reddy, Thamsanqa E. Chiliza, O. Pooe
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引用次数: 0

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。先前的研究已经确定了结核分枝杆菌中的一个基因家族,称为PE_PGRS蛋白,它已经显示出作为药物靶点的潜力。功能注释可以帮助鉴定这些蛋白质在结核分枝杆菌中可能发挥的作用。以往的研究表明,PE_PGRS具有进一步研究的潜力。鉴定出最有希望的蛋白生物标志物为PE_PGRS17、PE_PGRS31、PE_PGRS50和PEPGRS54。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。预测了许多特征,以了解这些蛋白质的稳定性,定位和功能。据估计,所有这些蛋白质都能产生免疫反应或参与免疫过程。选择重组pe_pgrs17蛋白,以大肠杆菌为宿主细胞进行最佳表达和纯化。这些关于PE_PGRS17的发现可以在未来的科学研究中扩展。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用于进一步研究,特别是作为药物/疫苗靶点。最终,根据其预测的结构,PE_PGRS17被认为是最稳定的,这有望成为未来结核病研究的关键因素。
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Computational Analysis, In silico Functional Annotation, and Expression of Recombinant PE_PGRS Protein Biomarkers Found in Mycobacterium tuberculosis
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. Previous studies have identified a gene family within Mtb, known as PE_PGRS proteins, which has shown potential as a drug target. Functional annotations can assist with identifying the role these proteins may play within Mtb. Previous studies indicated PE_PGRS to have potential for further research. The protein biomarkers that showed the most promise were 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 were conducted on the recombinant PE_PGRS17 Mtb protein biomarker. The results were calculated using various online software algorithms. Many characteristics were predicted to understand the stability, localization, and function of these proteins. All the proteins have been estimated to produce an immune response or be involved in the process of immunity. The recombinantPE_PGRS17 protein was chosen to be optimally expressed and purified using E.coli as a host cell. These findings specifically on PE_PGRS17, can be expanded in future scientific studies. The predicted structures, protein-protein interaction, and antigenic properties of the proteins estimate whether a protein can be used for further studies, specifically as drug/vaccine targets. Ultimately, PE_PGRS17 is seen as the most stable according to its predicted structure, which holds promise as a key factor in future tuberculosis studies.
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来源期刊
Open Bioinformatics Journal
Open Bioinformatics Journal Computer Science-Computer Science (miscellaneous)
CiteScore
2.40
自引率
0.00%
发文量
4
期刊介绍: The Open Bioinformatics Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters, clinical trial studies and guest edited single topic issues in all areas of bioinformatics and computational biology. The coverage includes biomedicine, focusing on large data acquisition, analysis and curation, computational and statistical methods for the modeling and analysis of biological data, and descriptions of new algorithms and databases. The Open Bioinformatics Journal, a peer reviewed journal, is an important and reliable source of current information on the developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.
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