Computational Molecular Biology 2025, Vol.15, No.6, 265-272 http://bioscipublisher.com/index.php/cmb 268 Figure 1 Summary of MLR regulatory network and corresponding equations defining the state machine (Adopted from Matalon et al., 2025) 4.3 Prediction of immunogenicity and population coverage Designing a vaccine that is effective for the majority of people cannot be achieved merely through experiments. To know in advance which epitopes are more likely to induce immune responses, it is necessary to rely on computational prediction. The model will refer to the specificity of T-cell receptors, analyze the binding affinity of epitopes to MHC molecules, and thereby determine which candidates are more reliable. Interestingly, some machine learning models can handle nonlinear features and integrate a large number of variables - for instance, this advantage is particularly evident in complex tasks such as predicting the pertussis vaccine response (Shinde et al., 2025). As for whether the vaccine is suitable for a wide range of people, it still needs to be considered in combination with the distribution of MHC alleles in the population. Combining these predictive capabilities with immune simulation models can more effectively assess the efficacy of vaccines in advance and also help optimize immunization strategies for different populations. 5 Case Studies in Computational Vaccine Development 5.1 COVID-19: AI-assisted epitope mapping and vaccine candidate design At the beginning of the outbreak of the epidemic, no one expected that AI would be so quickly involved in vaccine research and development. But in fact, immunoinformatics platforms like iVAX had already begun screening for conserved and immunopotential T-cell epitopes shortly after the SARS-CoV-2 genome was published. These platforms do not rely on "guessing". They predict immune responses by analyzing sequences and can even optimize the construction plan of antigens. Although traditional methods remain important, computational tools have clearly shortened the entire time window from sequencing to candidate vaccine design (De Groot et al., 2020). 5.2 Multi-epitope vaccine design for tuberculosis and malaria For stubborn and structurally complex pathogens such as tuberculosis and malaria, the intervention of computational methods is not an added bonus; rather, it is more often the key to solving the problem. Researchers first identified antigenic proteins by combining reverse vaccinology with immunoinformatics techniques, and then selected from them the highly immunogenic epitopes that could trigger MHC Class I, II or B-cell responses. After the epitopes are assembled into a structure, linkers and adjuvants are added, somewhat like building with blocks.
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