Quantum vaccinomics platforms to advance in vaccinology

Abstract

The opinion flows from Introduction to the immunological quantum that requires a historical perspective, to Quantum vaccine algorithms supported by a bibliometric analysis, to Quantum vaccinomics describing from our perspective the different vaccinomics and quantum vaccinomics algorithms. Finally, in the Discussion and conclusions we propose novel platforms and algorithms developed to further advance on quantum vaccinomics. In the paper we refer to protective epitopes or immunological quantum for the design of candidate vaccine antigens, which may elicit a protective response through both cellular and antibody mediated mechanisms of the host immune system. Vaccines are key interventions for the prevention and control of infectious diseases affecting humans and animals worldwide. Biophysics led to quantum biology and quantum immunology reflecting quantum dynamics within living systems and their evolution. In analogy to quantum of light, immune protective epitopes were proposed as the immunological quantum. Multiple quantum vaccine algorithms were developed based on omics and other technologies. Quantum vaccinomics is the methodological approach with different platforms used for the identification and combination of immunological quantum for vaccine development. Current quantum vaccinomics platforms include in vitro, in music and in silico algorithms and top trends in biotechnology for the identification, characterization and combination of candidate protective epitopes. These platforms have been applied to different infectious diseases and in the future should target prevalent and emerging infectious diseases with novel algorithms.

Keywords: immunology; quantum; vaccine; vaccinology; vaccinomics.

Figure 1

Quantum vaccinomics platforms for the identification of immunological quantum and design of vaccine chimeric protective antigens. Candidate protective antigens are identified using systems biology integration of omics dataset combined Big Data analytics and machine learning. Then, immunological quantum can be identified using in silico algorithms or epitope mapping for predicting protective epitopes or using in vitro, in silico and in music approaches for the identification of domains involved in protein-protein interactions.