Computational Molecular Biology 2025, Vol.15, No.6, 265-272 http://bioscipublisher.com/index.php/cmb 266 directions of integrating multi-omics data and artificial intelligence to enhance vaccine efficacy and safety. Through a comprehensive analysis of existing research and tools, this paper emphasizes the transformative impact of computational methods on the fields of immunology and public health, highlighting their significant role in the rapid response to infectious disease outbreaks and personalized vaccine design, with the aim of accelerating vaccine development. 2 Computational Approaches for Antigen Discovery 2.1 Genome-based vaccine target identification (reverse vaccinology) Traditionally, finding suitable vaccine targets often requires step-by-step screening through experiments, but this method is time-consuming and limited. Reverse vaccinology bypasses this. It does not cultivate bacteria but directly starts from the genome, screening for genes that encode surface or secreted proteins, which are usually virus-related and more easily recognized by the immune system. Of course, this strategy is not applicable to every pathogen, but it has been proven in multiple cases to identify potential antigens (Rawal et al., 2021). When screening, it is not only necessary to look at antigenicity, but also to consider whether these proteins have homology with the host. If they are too similar to human proteins, they may instead cause immune side effects. These computational processes are like multi-layer filters, sifting out candidate antigens layer by layer and laying the foundation for subsequent immunoinformatics analysis. 2.2 Epitope prediction algorithms (B-cell and T-cell epitopes) Predicting epitopes may sound highly technical, but the logic is actually quite simple: it's about identifying which fragments can be recognized by the immune system. B-cell epitopes are usually regions that antibodies can directly recognize, while T-cells pay more attention to whether peptides can bind to MHC molecules. The problem is that there are too many combinations of TCR and MHC, and it is difficult to exhaust all possibilities relying on experience. For this reason, more and more algorithms have introduced machine learning models, especially performing well in identifying T-cell epitopes. Many tools have been able to balance affinity and specificity prediction (Zhang et al., 2021; Gao et al., 2023). Although the accuracy rate still cannot be compared with that of experiments, in the early screening stage, it can greatly improve efficiency and also help to find some conserved regions that are not easily detectable but have strong immunogenicity. 2.3 Applications of structural bioinformatics in antigen design Vaccine design without structural information is like picking a key to unlock with eyes closed. Structural bioinformatics is precisely the toolbox for solving this problem. It can tell us how antigens and antibodies "adhere", which epitopes are stereoscopically exposed and which may be hidden. By means of computational simulation and docking technology, it is possible to predict in advance whether the antigen design is reasonable. In recent years, some models have incorporated machine learning algorithms, which can predict antibody affinity and binding sites more accurately (Mason et al., 2021; Wilman et al., 2022). Of course, all of this is based on a reliable structural template. If the pathogen is a "structural blind box", modeling will be limited. Even so, integrating structural information with sequence prediction results can still provide a more realistic conformational basis for vaccine design and enhance its immune effect in vivo. 3 Immunoinformatics and Data Integration 3.1 Vaccine development databases and resources (e.g., IEDB, VaxiJen) Often, the first step in vaccine design is not in the laboratory but in the database. Platforms like IEDB contain tens of thousands of verified B-cell and T-cell epitope information. It is more like a constantly updated "immune map", and researchers can hardly do without it. On the other side, tools like VaxiJen simply do not even consider the structure and directly predict antigenicity based on the sequence, enabling the screening of potential vaccine candidate proteins without the need for comparative analysis. Although these databases and tools cannot replace experimental verification, they do indeed speed up the antigen screening process significantly. Especially when the research is confronted with a large number of candidate proteins, having a system that can automatically prioritize them is much more efficient than relying solely on intuition (Oli et al., 2020). Of course, the prerequisite is that these databases should be updated in a timely manner and have friendly entry points; otherwise, no matter how good the resources are, they won't be able to play their role.
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