Viral proteins are dynamic molecular machines that couple molecular-scale events to microbiological phenotypes, including host-cell tropism, entry efficiency, environmental persistence, strain-specific immune escape, and antiviral resistance. In this review, we critically evaluated how experimental, computational, and biochemical approaches define these links and support clinical translation. We organized the evidence around four key observables: Structure, dynamics, mechanics, and thermodynamics. We then assessed which conclusions remained robust across methods and viral families. Nuclear magnetic resonance spectroscopy, cryogenic electron microscopy, atomic force microscopy, calorimetry, all-atom molecular dynamics (MD), coarse-grained MD, structural bioinformatics, artificial intelligence, and biochemical reconstitution were compared in terms of resolution, sampling, assumptions, and validation requirements. Human immunodeficiency virus type 1 and severe acute respiratory syndrome coronavirus 2 were used as data-rich exemplars, but the resulting conclusions were tested against influenza virus, hepatitis B virus (HBV), human papillomavirus (HPV), Ebola virus, adenovirus, bacteriophages, and other systems. Particular attention was given to force-field selection, enhanced sampling, free-energy calculations, convergence, experimentally restrained modeling, and the multiscale relationship between all-atom and coarse-grained simulations. We also examined integrative workflows, the licensed HBV and HPV virus-like particle (VLP) vaccines, emerging dengue and coronavirus VLP platforms, and clinically validated structure-guided antivirals and prefusion-stabilized antigens. The analysis identified where evidence converges, where method-specific biases or limited taxonomic sampling constrain generalization, and which measurements are required to connect a molecular mechanism to infection, transmission-relevant traits, surveillance, or therapeutic performance. This critical, experimentally anchored framework defines the distinctive contribution of the review.
Citation: Alessandro Paolo Addamo, Floriana D'Angeli, Carlo Genovese. Molecular biophysics of viral proteins: From mechanistic insights to microbiological and clinical applications[J]. AIMS Microbiology, 2026, 12(3): 682-717. doi: 10.3934/microbiol.2026030
Viral proteins are dynamic molecular machines that couple molecular-scale events to microbiological phenotypes, including host-cell tropism, entry efficiency, environmental persistence, strain-specific immune escape, and antiviral resistance. In this review, we critically evaluated how experimental, computational, and biochemical approaches define these links and support clinical translation. We organized the evidence around four key observables: Structure, dynamics, mechanics, and thermodynamics. We then assessed which conclusions remained robust across methods and viral families. Nuclear magnetic resonance spectroscopy, cryogenic electron microscopy, atomic force microscopy, calorimetry, all-atom molecular dynamics (MD), coarse-grained MD, structural bioinformatics, artificial intelligence, and biochemical reconstitution were compared in terms of resolution, sampling, assumptions, and validation requirements. Human immunodeficiency virus type 1 and severe acute respiratory syndrome coronavirus 2 were used as data-rich exemplars, but the resulting conclusions were tested against influenza virus, hepatitis B virus (HBV), human papillomavirus (HPV), Ebola virus, adenovirus, bacteriophages, and other systems. Particular attention was given to force-field selection, enhanced sampling, free-energy calculations, convergence, experimentally restrained modeling, and the multiscale relationship between all-atom and coarse-grained simulations. We also examined integrative workflows, the licensed HBV and HPV virus-like particle (VLP) vaccines, emerging dengue and coronavirus VLP platforms, and clinically validated structure-guided antivirals and prefusion-stabilized antigens. The analysis identified where evidence converges, where method-specific biases or limited taxonomic sampling constrain generalization, and which measurements are required to connect a molecular mechanism to infection, transmission-relevant traits, surveillance, or therapeutic performance. This critical, experimentally anchored framework defines the distinctive contribution of the review.
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Figures(3) / Tables(8)
Alessandro Paolo Addamo, Floriana D'Angeli, Carlo Genovese. Molecular biophysics of viral proteins: From mechanistic insights to microbiological and clinical applications[J]. AIMS Microbiology, 2026, 12(3): 682-717. doi: 10.3934/microbiol.2026030
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