Computational Molecular Biology 2025, Vol.15, No.6, 265-272 http://bioscipublisher.com/index.php/cmb 267 3.2 Sequence alignment, motif search, and homology modeling techniques Identifying key regions on proteins does not always rely on experience. Sometimes, a fragment that is conserved across species often conceals crucial immune information. At this point, sequence alignment and motif search tools come in handy. They help identify stable fragments that may trigger immune responses, and are particularly suitable for designing vaccines with high coverage rates of multiple strains. However, if these tools alone are not accurate enough, homologous modeling can fill the gap at the structural level. Even without complete structural data, as long as there is a similar template, a rough three-dimensional conformation can be pieced together. This is crucial for determining which epitopes can be exposed to the "field of view" of the immune system (Zaher et al., 2025). These tools can be used separately, but when combined, the effect is even better. They are an indispensable "intermediate stop" in many vaccine research and development processes. 3.3 Integration of multi-omics data for comprehensive antigen profiling It is difficult to describe the full picture of an antigen through a single data source. The genome tells us "what components there are", the transcriptome says "who is being expressed", and the proteome and immunopeptide group involve "who is really at work". Relying solely on a set of data, it is very easy to miss the key links in the immune response. Although it is not easy to integrate these multi-omics data, as the data formats, analysis dimensions, and sampling time points may all be inconsistent, once they are connected, a complete picture of the interaction between pathogens and hosts can be presented. Nowadays, many new algorithms and machine learning models are being used to solve the problem of data heterogeneity. Although they are not fully automated yet, the trend is already very clear: Whoever can integrate better is likely to find new vaccine targets in advance (Anderson et al., 2025; Kamali et al., 2025). Especially in the design of personalized vaccines, multi-omics analysis can more accurately identify the most effective antigenic loci for a specific population or individual, which is a cutting-edge step in the field of computational vaccines. 4 Computational Modeling of Immune Responses 4.1 Agent-based and mechanistic models of the immune system There is more than one path for modeling immune responses. Some models revolve around individual cells or molecules, treating them as objects that can "act", while others choose to use a set of differential equations to capture the operational rules of the immune system. Ultimately, the core objective of these methods is the same - to figure out how the immune system reacts step by step in the face of viruses, bacteria, and even vaccines. For instance, some studies have used hybrid modeling methods to investigate how IL-2 and IL-4 regulate lymphocyte activation. The simulated immune cell proliferation pathways have revealed the details of immune behavior during the infection period (Atitey and Anchang, 2022). When facing viral infections like SARS-CoV-2, mechanism models attempt to capture the dynamic interactions among viruses, immune cells, and cytokines, helping us predict the progression of the disease and even the possibility of immune clearance (Leon et al., 2023; Miroshnichenko et al., 2025). Although these models each have their own focuses, the theoretical support they provide remains indispensable for the validation of vaccine strategies or immunotherapies. 4.2 Applications of immune simulation tools (e.g., C-ImmSim, SimuLymph) Not everyone can build an immune model from scratch. Fortunately, platforms like C-ImmSim and SimuLymph have already set up the "framework". The design concept of C-ImmSim is to string the antigenic epitope information with the characteristics of lymphocyte receptors and predict the formation of immune response and even immune memory through amino acid sequences (Rapin et al., 2010). In fact, it has been able to reproduce some classic experiments quite well, such as the influence of different MHC combinations on immune responses. SimuLymph takes a different approach. It is based on proxy modeling and pays more attention to individualized immune responses - particularly suitable for predicting the responses of certain individuals to specific immunotherapies (Figure 1) (Matalon et al., 2025). Although these tools each have their own mechanisms, they are essentially like a "digital immunity sandbox" that can test vaccine construction or treatment pathways in advance in a virtual environment.
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