This Research Topic is the second volume of the “Data-Driven Vaccine Design for Microbial-Associated Diseases” Community Series. Please see Volume I here.

Vaccination is one of the greatest achievements in immunology and medicine, nearly eradicating many infectious diseases that once harmed humanity. It introduces a form of a pathogen into the body, enabling the immune system to develop a memory response for protection against future infections. Traditionally, vaccine development has followed empirical methods, and time consumption but some pathogens have evolved to evade these approaches. To address these challenges, we need interdisciplinary strategies integrating engineering, immunology, and physical sciences in vaccine development.

The Research Topic Volume II of "Data-Driven Vaccine Design for Microbial-Associated Diseases", explores the role of data-driven approaches in designing vaccines for infectious diseases and cancers caused by pathogenic microbes. Further, this issue emphasizes the integration of genomics, proteomics, transcriptomics, immunomics, structural, and synthetic biology data, alongside data from computer simulations, bioinformatics analyses, and systems biology. Additionally, it highlights the use of machine learning models to predict immune responses and antigen structures, ultimately improving vaccine design and development efficiency.

Emerging and harmful microbes include severe acute respiratory syndrome-related coronavirus, Monkeypox, Avian influenza, Human Immunodeficiency Virus, Hepatitis B and C viruses, Mycobacterium tuberculosis, Human Papillomavirus, Epstein-Barr virus, Helicobacter pylori, Schistosoma haematobium, and many more that cause a wide range of infectious diseases and deadly cancers. These pathogens pose significant challenges due to their high mutation rates, making it difficult to develop universal vaccines. The available vaccines for some specific pathogens offer limited protection, require multiple doses, or show reduced effectiveness in immunocompromised individuals. Additionally, no approved vaccines are available for most of the highly mutated microbes with complex structural proteins. The pathogens’ complex life cycles, latency periods, and immune evasion mechanisms further complicate vaccine development. Although strain-specific vaccines for HPV-related cancers are available, they are primarily prophylactic.

To overcome these limitations, data-driven and computational approaches are being applied to the design of epitope-based vaccines, including peptide and RNA vaccines. Epitopes, the parts of pathogens recognized by the immune system, are essential in both peptide and RNA vaccines, as they determine the immune response's specificity, strength, and duration. Whether delivered as peptides or encoded by mRNA, epitopes are crucial in guiding the immune system to recognize and eliminate pathogens effectively. Data-driven methods help identify the most promising epitopes, accelerating the development of novel vaccine candidates. This Research Topic highlights how multi-omics data and AI-driven tools can aid in identifying key microbial antigens, leading to the creation of more effective, epitope-based vaccines for a range of challenging diseases. The wet lab experiments are essential to validate computational findings to revolutionize the development of vaccines.

This Research Topic accepts Original Research, Systematic Review, Methods, Review and Mini-Review, Technology, and Code, Brief Research Report, and Opinion. We welcome manuscripts focusing on, but not limited to, the following sub-topics:

• Antigens

• Artificial intelligence

• Cancer vaccines

• Data-driven design

• Epitopes

• Immunoinformatics

• Infectious diseases

• Machine learning models

• mRNA

• Multi-omics (Genomics, transcriptomics, immunomics and proteomics)

• Mutation and pathogen evolution

• Structural modeling and Molecular dynamics simulation

• Systems biology

• Toll-like receptors and vaccine efficacy

Are you interested in contributing a manuscript to this Research Topic?

Community Series in Data-Driven Vaccine Design for Microbial-Associated Diseases: Vol II