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, governing host-receptor recognition, membrane fusion, and evasion of immune surveillance under rapid evolutionary pressure. We present a falsifiable, integrative modeling framework that combines heterogeneous experimental data—cryo-electron microscopy (cryo-EM), nuclear magnetic resonance (NMR), small-angle X-ray scattering (SAXS), and spectroscopic observables—with atomistic simulation ensembles through forward modeling, error-propagated Bayesian maximum-entropy reweighting, enhanced sampling (well-tempered metadynamics and umbrella sampling), and multi-fold cross-validation to guard against overfitting. Reproducibility is enforced through version-controlled workflows (Git), containerization (Docker), and archival of raw data, scripts, and ensembles (Zenodo/Figshare). We benchmark the framework against an analytically tractable one-dimensional double-well potential, for which complete ground-truth data are available. Execution of the archived, fully disclosed simulation code reproduces the reported potential-of-mean-force (PMF) table exactly (verified against a SHA-256 fingerprint of the output) and yields a root-mean-square error (RMSE) of 0.18 kT relative to the analytical PMF in the well-sampled region (|𝑥| < 1.6), confirming that the framework recovers the underlying free-energy landscape with high fidelity when the input data and code are followed precisely. We further quantify, via seed-replicated (𝑛 = 20) uncertainty analysis, one-at-a-time and multi-parameter factorial sensitivity analysis on the integration parameters, and robustness scans over histogram construction and trajectory length, that this reconstruction accuracy is statistically insensitive to ±10% perturbations of the integration timestep and friction coefficient and is robust to reasonable choices of bin count and histogram range at adequate sampling depth—while also showing, by direct comparison, that unreplicated single-trajectory sensitivity checks can be dominated by sampling noise. We then survey how the framework's methodology—not new simulation output generated for this manuscript—can organize and interpret published structural and biophysical findings on conformational divergence across a broad panel of viral fusion glycoproteins, including SARS-CoV-2 spike, influenza A hemagglutinin, influenza C hemagglutinin-esterase-fusion protein, respiratory syncytial virus fusion protein, HIV-1 envelope glycoprotein, Epstein–Barr virus glycoproteins, herpes simplex virus glycoproteins, varicella-zoster virus glycoproteins, human cytomegalovirus glycoproteins, and Nipah virus glycoproteins. All quantitative values attributed to specific variants or systems in this survey are drawn from the cited primary literature; no new molecular dynamics trajectories were generated for this manuscript. We close with an explicit falsifiability statement, a scientific and technical risk assessment, and a staged roadmap for experimental validation, positioning the framework as a reproducible methodological contribution rather than a report of novel simulation results.