Identification of core therapeutic targets for Monkeypox virus and repurposing potential of drugs against them: An in silico approach.

Monkeypox virus (mpox virus) outbreak has rapidly spread to 82 non-endemic countries. Although it primarily causes skin lesions, secondary complications and high mortality (1-10%) in vulnerable populations have made it an emerging threat. Since there is no specific vaccine/antiviral, it is desirable to repurpose existing drugs against mpox virus. With little knowledge about the lifecycle of mpox virus, identifying potential inhibitors is a challenge. Nevertheless, the available genomes of mpox virus in public databases represent a goldmine of untapped possibilities to identify druggable targets for the structure-based identification of inhibitors. Leveraging this resource, we combined genomics and subtractive proteomics to identify highly druggable core proteins of mpox virus. This was followed by virtual screening to identify inhibitors with affinities for multiple targets. 125 publicly available genomes of mpox virus were mined to identify 69 highly conserved proteins. These proteins were then curated manually. These curated proteins were funnelled through a subtractive proteomics pipeline to identify 4 highly druggable, non-host homologous targets namely; A20R, I7L, Top1B and VETFS. High-throughput virtual screening of 5893 highly curated approved/investigational drugs led to the identification of common as well as unique potential inhibitors with high binding affinities. The common inhibitors, i.e., batefenterol, burixafor and eluxadoline were further validated by molecular dynamics simulation to identify their best potential binding modes. The affinity of these inhibitors suggests their repurposing potential. This work can encourage further experimental validation for possible therapeutic management of mpox.

Subtractive proteomics assisted therapeutic targets mining and designing ensemble vaccine against Candida auris for immune response induction.

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.