Computational vaccine development against protozoa

Abstract

Protozoan parasites remain a major global health and economic burden, particularly in low- and middle-income countries. Conventional strategies such as chemotherapy and vector control face growing limitations due to resistance, toxicity, and implementation challenges. Vaccination represents a sustainable solution, but the complexity of protozoan life cycles and antigenic diversity has hindered vaccine development. Computational vaccinology offers innovative tools to overcome these barriers, combining immuno-informatics, reverse vaccinology, and artificial intelligence to accelerate the identification of immunogenic epitopes and streamline vaccine design. This review explores the current landscape of computational vaccine development against protozoa, highlighting advances in epitope prediction, population-specific vaccine design, and digital twin technologies. Applications include multivalent vaccines targeting conserved antigens across species, personalized formulations based on host immunogenetics, and the emerging use of protozoan vectors in cancer immunotherapy. Despite these promising avenues, significant challenges remain, particularly the need for robust experimental validation, improved delivery systems for short peptides, and greater acceptance of in silico methods by the broader scientific community. We argue that integrating computational tools with experimental immunology, high-throughput genomics, and translational research will be the key to developing safe, effective, and broadly accessible vaccines against protozoan infections. This convergence of disciplines has the potential to not only address neglected tropical diseases but also to establish new paradigms in precision vaccinology and immunotherapy.

Keywords: Computational vaccinology1; Immuno-informatics3; Multi-epitope peptide vaccines5; Protozoa2; Vaccination4.

Graphical abstract

Fig. 1

Computational pipeline for the in silico design of epitope-based vaccines against protozoan pathogens. The process begins with genome and proteome mining to identify candidate antigenic proteins. These sequences are then screened using immuno-informatics tools for B-cell and T-cell epitope prediction, taking into account MHC binding affinity, antigenicity, allergenicity, and population coverage. Conserved and immunodominant epitopes are prioritized using multiple sequence alignment and conservation analysis across strains or species. Selected epitopes are assembled into multi-epitope constructs, optimized for solubility, stability, and immunogenicity. The final constructs are subject to experimental validation to assess immunogenicity and protective efficacy in vitro and in vivo.