Immunoinformatics, also known as computational immunology, is the combination of computer science and experimental immunology. Due to technological advancements in immunology research, large quantities of data are now available from genome sequencing, scientific literature and clinical records.1
Together with bioinformatics approaches, the potential to discover and develop immunotherapies have opened much more. For instance, there are now tools to predict and characterize human epitopes and human leukocyte antigen (HLA) allotypes with the use of whole-genome sequencing, exome sequencing and RNA sequencing data.2 This epitope prediction can further be used for research into vaccine design candidates and the study of host-pathogen interactions.3
Epitope Mapping and Vaccine Design
Epitope mapping allows us to understand immune system interactions with pathogens and designing vaccines. Epitope mapping identifies specific parts of antigens—foreign substances like viruses—that can elicit an immune response. In silico tools, such as reverse vaccinology and structural vaccinology, are now being employed to accelerate vaccine design by predicting these epitopes without the need for time-consuming lab experiments.
For example, epitope prediction models were critical in the development of the mRNA vaccines for SARS-CoV-2, using computational resources to predict viral protein structures that the immune system could recognize. Similarly, multi-epitope vaccines, such as those for the Zika virus, rely on tools like the Artemis Comparison Tool (ACT) for comparative sequencing, identifying viral mutations that affect immune recognition. As the complexity of vaccine design increases, these predictive tools enable faster, more precise vaccine development.4
Related: Epitope Characterization and its Importance in Antibody Therapeutics
Host-Pathogen Interactions and Genetic Predisposition to Disease
Immunoinformatics also improves our understanding of disease pathogenesis and genetic predisposition by analyzing host-pathogen interactions. For instance, studies have shown that certain pathogens like Herpes simplex have gene products that mimic human proteins involved in Alzheimer’s disease, hinting at molecular mimicry as a mechanism of disease. Tools such as the BepiPred server, part of the Immune Epitope Database (IEDB), are widely used to investigate these interactions.5
Moreover, disease prediction models are becoming more accurate with the use of machine learning algorithms like support vector machines (SVM) and random forest. These models analyze large datasets of single nucleotide polymorphisms (SNPs) to identify genetic predispositions for complex diseases such as celiac disease or rheumatoid arthritis. By pinpointing genetic risk factors, immunoinformatics helps guide preventive measures and early interventions.
Biomarker Discovery for Early Diagnosis and Prognosis
The discovery of biomarkers—molecular signatures that indicate the presence or progression of a disease—has been significantly accelerated by immunoinformatics. Biomarkers can aid in the early diagnosis, prognosis, and monitoring of conditions like cancer and autoimmune diseases. For example, in systemic lupus erythematosus (SLE), autoantibodies serve as key biomarkers. Tools like the k-nearest neighbor (kNN) algorithm have been applied to classify patients based on these biomarkers, allowing for earlier and more accurate diagnoses.
In oncology, immunoinformatics tools such as the Immunoscore quantify immune cell infiltration in tumor tissues, which serves as a prognostic marker in colon cancer.6 Coupling this with artificial intelligence (AI) and machine learning (ML) models enables faster, more robust diagnostic outcomes, improving clinical decision-making. The use of AI in digitized pathology is another area where immunoinformatics is making strides, as seen in HER2 expression scoring for breast cancer, allowing for targeted therapy selection.
Related: AI Deep Learning Models in Antibody Research
Toward Global Data-Driven Research
As immunology moves further into the data-driven era, the integration of informatics will become more seamless and widespread. The rapid rise of high-throughput technologies, such as next-generation sequencing and mass spectrometry, has resulted in an overwhelming amount of biological data. Immunoinformatics tools are essential for processing and extracting meaningful insights from this data, ensuring that discoveries can be translated into clinical practice more quickly and efficiently.
Tomar, N., & De, R. K. (2014). Immunoinformatics: a brief review. Methods in molecular biology (Clifton, N.J.), 1184, 23–55. https://doi.org/10.1007/978-1-4939-1115-8_3
Backert, L., Kohlbacher, O. Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Med 7, 119 (2015). https://doi.org/10.1186/s13073-015-0245-0
Oli AN, Obialor WO, Ifeanyichukwu MO, Odimegwu DC, Okoyeh JN, Emechebe GO, Adejumo SA, Ibeanu GC. Immunoinformatics and Vaccine Development: An Overview. Immunotargets Ther. 2020;9:13-30. https://doi.org/10.2147/ITT.S241064
Chatanaka, M. K., Ulndreaj, A., Sohaei, D., & Prassas, I. (2022). Immunoinformatics: Pushing the boundaries of immunology research and medicine. ImmunoInformatics, 5, 100007. https://doi.org/10.1016/j.immuno.2021.100007
Zhang, Q., Wang, P., Kim, Y., P. Haste-Andersen, Beaver, J., Bourne, P. E., Bui, H.-H., Buus, S., S. Frankild, Greenbaum, J., Lund, O., C. Lundegaard, Nielsen, M., Ponomarenko, J., Sette, A., Zhu, Z., & Peters, B. (2008). Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Research, 36, W513–W518. https://doi.org/10.1093/nar/gkn254
Galon, J., Bernhard Mlecnik, Bindea, G., Angell, H. K., Berger, A., Lagorce, C., Lugli, A., Inti Zlobec, Hartmann, A., Bifulco, C., Nagtegaal, I. D., Palmqvist, R., Masucci, G. V., Botti, G., Fabiana Tatangelo, Delrio, P., Maio, M., Laghi, L., Fabio Grizzi, & Asslaber, M. (2013). Towards the introduction of the “Immunoscore” in the classification of malignant tumours. The Journal of Pathology, 232(2), 199–209. https://doi.org/10.1002/path.4287
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