In-silico immunoinformatic analysis of SARS-CoV-2 virus for the development of putative vaccine construct

Dominant B cell-T cell epitopes instigated robust immune response in-silico against Scrub Typhus

Scrub typhus, a potentially life-threatening infectious disease, is attributed to bacteria Orientia tsutsugamushi (O. tsutsugamushi). The transmission of this illness to humans occurs through the bite of infected chiggers, which are the larval forms of mites belonging to the genus Leptotrombidium. In this research, we developed a subunit vaccine specifically designed to target outer membrane proteins. Immunodominant cytotoxic T-lymphocytes (CTLs), B- lymphocytes (BCLs), and major histocompatibility complex (MHC)- II epitopes were identified using machine learning and bioinformatics approaches. These epitopes were arranged in different combinations with the help of suitable linkers like AAY, KK, GPGPG and adjuvant (cholera toxin B) that resulted in a vaccine construct. Physiochemical properties were assessed, where the predicted solubility (0.571) was higher than threshold value. Tertiary structure was predicted using I-TASSER web server and evaluated using Ramachandran plot (94 % residues in most favourable region) and z-score (-6.04), which had shown the structure to have good stability and residue arrangement. Molecular docking with immune receptors, Toll-like receptor (TLR)-2 and -4 showed good residue interaction with 13 and 5 hydrogen bonds respectively. Molecular dynamics simulations of receptor-ligand complex provided the idea about the strong interaction having 1.524751 × 10-5 eigenvalue. Amino acid sequence of vaccine was converted to nucleotide sequence and underwent codon optimization. The optimized codon sequence was used for in-silico cloning, which provided idea about the possibility of synthesis of vaccine using E. coli as host. Overall, this study provided a promising blueprint for a scrub typhus vaccine, although experimental validation is needed for confirmation. Furthermore, it is crucial to acknowledge that while bioinformatics provides valuable insights, in-vitro and in-vivo studies are imperative for a comprehensive evaluation of vaccine candidate. Thus, the integration of computational predictions with empirical research is essential to validate the efficacy, safety, and real-world applicability of the designed vaccine against Scrub Typhus. Nevertheless, the findings are good to carry forward for in-vitro and in-vivo investigations.

Utility of in silico-identified-peptides in spike-S1 domain and nucleocapsid of SARS-CoV-2 for antibody detection in COVID-19 patients and antibody production

SARS-CoV-2 contains four structural proteins, two of which, the spike and nucleocapsid, are commonly used for the standardization of novel methods for antibody detection; however, some limitations in their use have been observed due to the homology of this virus with other phylogenetically-related viruses. We performed in silico analysis to search for novel immunogenic and antigenic peptides. A total of twenty-five peptides were preliminarily selected, located in the 3D structure of both proteins. Finally, eight peptides were selected: one located in the N protein and seven in the S1 domain of the spike protein. Additionally, the localization of selected peptides in 2D structures and possible changes in the sequences of these peptides in SARS-CoV-2 variants of concern were analyzed. All peptides were synthetized in MAP8 format, and recombinant S (trimer and RBD) and N proteins were used as antigens to search for antibodies in serum samples derived from COVID-19 patients, and for antibody response in New Zealand rabbits. Results showed high recognition of the serum derived from COVID-19 patients to all selected peptides; however, only the RBD3 peptide induced antibody production. In conclusion, this work provides evidence for a new strategy in peptide selection and its use for antibody detection or antibody production in animals.

Anurans against SARS-CoV-2: A review of the potential antiviral action of anurans cutaneous peptides

At the end of 2019, in China, clinical signs and symptoms of unknown etiology have been reported in several patients whose sample sequencing revealed pneumonia caused by the SARS-CoV-2 virus. COVID-19 is a disease triggered by this virus, and in 2020, the World Health Organization declared it a pandemic. Since then, efforts have been made to find effective therapeutic agents against this disease. Identifying novel natural antiviral drugs can be an alternative to treatment. For this reason, antimicrobial peptides secreted by anurans' skin have gained attention for showing a promissory antiviral effect. Hence, this review aimed to elucidate how and which peptides secreted by anurans' skin can be considered therapeutic agents to treat or prevent human viral infectious diseases. Through a literature review, we attempted to identify potential antiviral frogs' peptides to combat COVID-19. As a result, the Magainin-1 and -2 peptides, from the Magainin family, the Dermaseptin-S9, from the Dermaseptin family, and Caerin 1.6 and 1.10, from the Caerin family, are molecules that already showed antiviral effects against SARS-CoV-2 in silico. In addition to these peptides, this review suggests that future studies should use other families that already have antiviral action against other viruses, such as Brevinins, Maculatins, Esculentins, Temporins, and Urumins. To apply these peptides as therapeutic agents, experimental studies with peptides already tested in silico and new studies with other families not tested yet should be considered.

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.