An Integrative Biophysical Framework for Quantifying Conformational Dynamics in Complex Biomolecular Systems: Perspectives on Viral Glycoprotein Evolutionary Divergence

Conformational dynamics underpin the functional adaptability of viral glycoproteins, facilitating host receptor recognition, membrane fusion, and evasion of immune surveillance during rapid evolutionary processes. Herein, we propose a stringent, falsifiable integrative modeling framework that seamlessly amalgamates heterogeneous experimental datasets—including cryo-electron microscopy (cryo-EM), nuclear magnetic resonance (NMR), small-angle X-ray scattering (SAXS), and spectroscopic observations—with atomistic computational ensembles. This integration is achieved through precise forward modeling, error-propagated Bayesian maximum entropy reweighting, advanced enhanced sampling techniques such as well-tempered metadynamics and umbrella sampling, and rigorous multi-fold cross-validation protocols to prevent overfitting. To ensure reproducibility, transparency, and accessibility, the framework incorporates version-controlled computational workflows using Git, containerization via Docker, and archival of raw datasets, scripts, and ensembles on platforms such as Zenodo or Figshare. Validation on a synthetic double-well potential system, where complete ground-truth simulation data are available, demonstrates the framework's ability to faithfully recover underlying free-energy landscapes with high fidelity, quantified by low root-mean-square error (RMSE) in potential of mean force (PMF) reconstruction. When applied to the SARS-CoV-2 spike (S) glycoprotein, previous studies using similar approaches have delineated variant-specific evolutionary strategies: early variants such as Alpha and Beta augment binding affinity via stabilization of receptor-binding domain (RBD) conformations, Delta imposes rigidity for optimized ACE2 engagement, whereas Omicron and its descendants (e.g., BA.1, XBB, JN.1) leverage heightened RBD-up flexibility to enhance transmissibility and immune evasion, with quantitative shifts in free-energy biases (\(\Delta G\)) and population distributions. Extension to influenza A hemagglutinin (HA) uncovers pH-modulated conformational transitions essential for fusion, with H3N2 variants exhibiting adaptive dynamics in receptor-binding domains, including pH-dependent barrier reductions of \(\sim 3-4\) kT. Further application to respiratory syncytial virus (RSV) fusion (F) protein elucidates trigger-mediated pre- to post-fusion transitions, providing insights for vaccine stabilization through mutation-induced barrier enhancements. Application to influenza C virus hemagglutinin-esterase-fusion (HEF) protein reveals lattice organization, conformational flexibility, virion motility, and receptor cleavage aiding membrane fusion. By incorporating AlphaFold3-derived structures, generative AI-driven sampling via diffusion models, and uncertainty quantification via bootstrapping, this methodology could enable the creation of dynamical digital twins, facilitating proactive engineering of pan-variant therapeutics and vaccines to fortify defenses against emergent pathogens.