DOI: 10.5281/zenodo.18091242
title: An Integrative Biophysical Framework for Quantifying Conformational Dynamics in Complex Biomolecular Systems: Perspectives on Viral Glycoprotein Evolutionary Divergence

description: 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.