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Updated: Sep 26, 2025
A Soluble Tetrazolium-Based Reduction Assay to Evaluate the Effect of Antibodies on Candida tropicalis Biofilms
Published on: September 16, 2022
Subtractive proteomics assisted therapeutic targets mining and designing ensemble vaccine against Candida auris for
Taimoor Khan1, Muhammad Suleman2, Syed Shujait Ali2
1Department of Bioinformatics and Biological Statistics School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Abstract:
The emergence of variants and the reports of co-infection caused by Candida auris in COVID-19 patients adds a further complication to the global pandemic situation. To date, no effective therapy is available for C. auris infections. Thus, characterization of therapeutic targets and designing effective vaccine candidates using subtractive proteomics and immune-informatics approaches is useful tool in controlling the emerging infections associated with SARS-CoV-2. In the current study, subtractive proteomics-assisted annotation of the vaccine targets was performed, which revealed seven vaccine targets. An immunoinformatic-driven approach was then employed to map protein-specific and proteome-wide immunogenic peptides (CTL, B cell, and HTL) for the design of multi-epitope vaccine candidates (MEVCs). The results demonstrated that the vaccine candidates possess strong antigenic features (>0.4 threshold score) and are classified as non-allergenic. Validation of the designed MEVCs through molecular docking, in-silico cloning, and immune simulation further demonstrated the efficacy of the vaccines by producing immune factor titers (ranging from 2500 to 16000 au/mL) i.e., IgM, IgG, IL-6, and Interferon-α. In conclusion, the current study provides a strong impetus in designing anti-fungal strategies against Candida auris.
Insights
This study identifies potential vaccine targets for Candida auris, a dangerous fungus that co-infects COVID-19 patients. Computational methods designed multi-epitope vaccine candidates showing strong efficacy in silico.
Area of Science:
- Mycology and Infectious Diseases
- Computational Biology and Bioinformatics
- Vaccine Development
Background:
- Emergence of Candida auris and its co-infection with SARS-CoV-2 poses a significant global health threat.
- Lack of effective therapies for Candida auris infections necessitates novel treatment strategies.
- Subtractive proteomics and immunoinformatics offer promising avenues for developing anti-fungal interventions.
Purpose of the Study:
- To identify and characterize potential vaccine targets against Candida auris using subtractive proteomics.
- To design multi-epitope vaccine candidates (MEVCs) by mapping immunogenic peptides using immunoinformatics.
- To validate the efficacy and safety of the designed MEVCs through in silico methods.
Main Methods:
- Subtractive proteomics was employed to identify seven potential vaccine targets from the Candida auris proteome.
- Immunoinformatics approaches were used to predict T-cell (CTL, HTL) and B-cell epitopes.
- In silico validation included molecular docking, cloning, and immune simulations to assess vaccine efficacy and immune responses.
Main Results:
- Seven effective vaccine targets were identified through subtractive proteomics.
- Designed MEVCs demonstrated strong antigenic properties and were predicted to be non-allergenic.
- In silico immune simulations showed significant production of immune factors like IgM, IgG, IL-6, and Interferon-α.
Conclusions:
- The study successfully designed promising multi-epitope vaccine candidates against Candida auris.
- Computational approaches provide a robust framework for developing anti-fungal vaccines.
- These findings offer a strong foundation for developing novel anti-fungal strategies against Candida auris.
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