antigenet

Antigenet is a term used to describe a network-based representation of antigenic determinants and their interactions with the immune system. In this conceptual framework, antigens, epitopes, and related immunogenic features are modeled as interconnected nodes, with edges capturing relationships such as sequence similarity, cross-reactivity, or shared binding motifs to immune receptors.

Construction and data can vary, but typical implementations treat nodes as antigens or epitopes, while edges encode measures like sequence similarity, structural similarity, or predicted or observed cross-reactivity. Node and edge attributes may include source organism, epitope type (linear or conformational), MHC binding potential, immunogenicity scores, and experimental validation. Data sources can include sequence databases, serological assays, structural models, and computational predictions.

Applications of antigenet approaches include mapping antigenic neighborhoods, analyzing cross-reactivity patterns, and guiding vaccine design. By examining the topology of antigen networks, researchers can identify clusters of related epitopes, potential immune escape routes, and candidates with broad coverage across pathogen variants. Such networks are also used in immune surveillance, epitope prioritization, and comparative immunology studies.

Limitations and context should be noted. The term antigenet is not universally standardized, and practical networks depend on data quality and the choice of relationship definitions. Complexities of conformational epitopes, dynamic immune responses, and host factors may limit the accuracy of static network representations. Related concepts include antigenic cartography, sequence similarity networks, and immunoinformatics-based epitope mapping.