Identification of vaccine targets in pathogens and design of a vaccine using computational approaches

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19

The emergence of COVID-19 progressed into a global pandemic that has functionally put the world at a standstill and catapulted major healthcare systems into an overburdened state. The dire need for therapeutic strategies to mitigate and successfully treat COVID-19 is now a public health crisis with national security implications for many countries. The current study employed Bayesian networks to a longitudinal proteomic dataset generated from Caco-2 cells transfected with SARS-CoV-2 (isolated from patients returning from Wuhan to Frankfurt). Two different approaches were employed to assess the Bayesian models, a titer-center topology analysis and a drug signature enrichment analysis. Topology analysis identified a set of proteins directly linked to the SAR-CoV2 titer, including ACE2, a SARS-CoV-2 binding receptor, MAOB and CHECK1. Aligning with the topology analysis, MAOB and CHECK1 were also identified within the enriched drug-signatures. Taken together, the data output from this network has identified nodal host proteins that may be connected to 18 chemical compounds, some already marketed, which provides an immediate opportunity to rapidly triage these assets for safety and efficacy against COVID-19.

Major antigen and paramyosin proteins as candidate biomarkers for serodiagnosis of canine infection by zoonotic Onchocerca lupi

Onchocerca lupi (Spirurida: Onchocercidae) is a filarial worm parasitizing domestic carnivores and humans. Adult nematodes usually localize beneath in the sclera or in the ocular retrobulbar of infected animals, whilst microfilariae are found in the skin. Therefore, diagnosis of O. lupi is achieved by microscopic and/or molecular detection of microfilariae from skin biopsy and/or surgical removal of adults from ocular tissues of infected hosts. An urgent non-invasive diagnostic tool for the diagnosis of O. lupi in dog is mandatory. In this study, an immunoproteomic analyses was performed using a combination of immunoblotting and mass spectrometry techniques. Onchocerca lupi major antigen (Ol-MJA) and paramyosin (Ol-PARA) proteins were identified as potential biomarkers for serodiagnosis. Linear epitopes were herein scanned for both proteins using high-density peptide microarray. Sera collected from dog infected with O. lupi and healthy animal controls led to the identification of 11 immunodominant antigenic peptides (n = 7 for Ol-MJA; n = 4 for Ol-PARA). These peptides were validated using sera of dogs uniquely infected with the most important filarioids infesting dogs either zoonotic (Dirofilaria repens, Dirofilaria immitis) or not (Acanthocheilonema reconditum and Cercopithifilaria bainae). Overall, six antigenic peptides, three for Ol-MJA and for Ol-PARA, respectively, were selected as potential antigens for the serological detection of canine O. lupi infection. The molecular and proteomic dataset herein reported should provide a useful resource for studies on O. lupi toward supporting the development of new interventions (drugs, vaccines and diagnostics) against canine onchocercosis.