Computational Analysis, In silico Functional Annotation, and Expression of Recombinant PE_PGRS Protein Biomarkers Found in Mycobacterium tuberculosis

The Open Bioinformatics Journal 05 Apr 2023 RESEARCH ARTICLE DOI: 10.2174/18750362-v16-e230306-2022-6



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.

Results and Discussion:

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.

Keywords: Functional annotation, Mycobacterium tuberculosis, PE_PGRS, Protein biomarkers, Tuberculosis studies, Computational analysis.
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