Epitope-Imprinted Polymers for Selective Recognition of Bacterial Proteins: From Design to Analytical Applications

Abstract

Epitope-imprinted polymers (EIPs) represent an advanced evolution of molecular imprinting technology for selective bacterial recognition. By using short, surface-exposed peptide fragments derived from bacterial proteins rather than whole cells or full-length proteins, EIPs enable the formation of structurally defined and chemically robust recognition cavities with improved accessibility, stability, and reproducibility. To the best of our knowledge, this review provides the first dedicated and focused overview of EIPs in bacterial detection. The fundamental principles of epitope imprinting are discussed, including rational epitope selection, computational modeling, monomer optimization, imprinting strategies (bulk, surface, nanoMIP, and electropolymerization), and template removal approaches. Representative applications targeting clinically significant pathogens such as Mycobacterium leprae, Salmonella Typhi, and Neisseria meningitidis are critically examined, highlighting analytical performance parameters including detection limits, imprinting factors, selectivity in complex biological matrices, and integration with electrochemical and piezoelectric transducers. In addition to summarizing current achievements, this review evaluates practical limitations related to epitope accessibility, matrix interference, fabrication reproducibility, scalability, and hospital-based implementation. Emerging strategies, including AI-assisted epitope design, multiepitope imprinting, nanomaterial-enhanced architectures, and portable point-of-care systems, are discussed as potential solutions to improve robustness and translational applicability. By consolidating current progress and identifying key scientific and technological gaps, this work clarifies the position of EIPs as promising synthetic recognition elements for next-generation bacterial diagnostics in clinical, food safety, and environmental monitoring.