A computational method for the prediction and functional analysis of potential Mycobacterium tuberculosis adhesin-related proteins

ABSTRACT

Objectives

Mycobacterial adherence plays a major role in the establishment of infection within the host. Adhesin-related proteins attach to host receptors and cell-surface components. The current study aimed to utilize in-silico strategies to determine the adhesin potential of conserved hypothetical (CH) proteins.

Methods

Computational analysis was performed on the whole Mycobacterium tuberculosis H37Rv proteome using a software program for the prediction of adhesin and adhesin-like proteins using neural networks (SPAAN) to determine the adhesin potential of CH proteins. A robust pipeline of computational analysis tools: Phyre2 and pFam for homology prediction; Mycosub, PsortB, and Loctree3 for subcellular localization; SignalP-5.0 and SecretomeP-2.0 for secretory prediction, were utilized to identify adhesin candidates.

Results

SPAAN revealed 776 potential adhesins within the whole MTB H37Rv proteome. Comprehensive analysis of the literature was cross-tabulated with SPAAN to verify the adhesin prediction potential of known adhesin (n = 34). However, approximately a third of known adhesins were below the probability of adhesin (P_ad) threshold (P_ad ≥0.51). Subsequently, 167 CH proteins of interest were categorized using essential in-silico tools.

Conclusion

The use of SPAAN with supporting in-silico tools should be fundamental when identifying novel adhesins. This study provides a pipeline to identify CH proteins as functional adhesin molecules.

KEYWORDS:

• Bioinformatic analysis
• mycobacterial
• tuberculosis
• protein analysis
• SPAAN tool

Article highlights

• Bacterial cell adhesion molecules known as adhesins are cell surface receptor proteins that facilitate host–pathogen interactions which enhance pathogenesis in the host.

• Identification of novel Mycobacterium tuberculosis (MTB) adhesins may provide pipeline for vaccine and serological biomarkers.

• This study focuses on characterizing conserved hypothetical (CH) proteins in the MTB proteome to ascertain adhesin role using a bioinformatic analysis approach.

• SPAAN analysis of the whole MTB H37Rv genome identified 167 CH genes as potential adhesin/adhesin-like proteins.

• Further stringent in-silico analysis determined homology protein classification, subcellular localization, and translocation of individual CH proteins.

• Herein this study, a dataset was generated with comparison with known MTB adhesins from the literature to enhance biomarker selection among CH proteins.

Acknowledgments

This work was presented at the 7^th SA TB conference 2022 and the SAMRC 16 early career scientific convention 2022.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

R Maharajh, S Senzani and M Pillay were involved in the conception and design of the study. R Maharajh, and S Senzani provided analysis and interpretation of the data. R Maharajh drafted of the paper, S Senzani and M Pillay revised the manuscript for intellectual content. All authors provided the final approval of the version to be published and agreed to be accountable for all aspects of the work.

Ethical statements

The study was approved by the Biomedical Research Ethics Council, University of KwaZulu-Natal (approval ref: BREC/00001970/2020).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14789450.2023.2275678.

Additional information

Funding

This study was supported by the University of KwaZulu-Natal (UKZN) College of Health Sciences scholarship, UKZN APACHE Flagship funds, and the SAMRC Division of Research Capacity Development under the Bongani Mayosi Health Scholars Programme. The content hereof is the sole responsibility of the authors and does not necessarily represent the official views of the funding bodies.