Systematic methodological bias drives 64–94% of structure–dynamics correlations in PDB-scale analyses: A three-layered empirical chain from ~240,000 proteins

Abstract Background: The Protein Data Bank (PDB) is the cornerstone of structural biology, housing over 200,000 structures determined by X-ray crystallography, NMR spectroscopy, and cryo-EM. The vast majority of PDB-scale statistical studies treat these mixed-source structures as interchangeable, without systematically testing whether the experimental method itself constitutes a source of bias. The "systematic methodological bias" reported here refers specifically to directional measurement bias arising from inherent differences in physical principles and data-processing conventions across experimental methods—bias that cannot be eliminated through repeated measurement and, in the statistical sense, satisfies the definition of a confounding variable by correlating with both independent (structural features) and dependent (kinetic indices) variables. Methods: Using an independently constructed whole-library kinetic database of 239,624 high-quality protein structures (Hengyue Protein Conformation Quantification System, an open-access repository), grounded in the Fundamental Evolution (FE) theoretical framework (Shi, 2026, The Universal Gravity of Evolution: Iterative Invariant), we performed X-ray/NMR/EM stratified analyses to quantify, for the firsttime, the impact of systematic methodological bias on structure–dynamics correlations. Seven mainstream structural features and three kinetic indices were examined. All correlations are reported with both Pearson r and Spearman ρ. For NMR ensembles, only the first model was used to maintain data-density parity with X-ray; results using the lowest-energy model were verified as a robustness check. Core scoring parameters (bond length standards, Ramachandran favoured regions) are derived from universal physicochemical constants, not fitted from X-ray statistics, mitigating concerns of circular reasoning. Results: Three independent lines of evidence converge: (1) NMR structures are enriched 95.97-fold in the A1 tail region of the free-energy landscape (46.35% vs. 0.48% in the A3 core); (2) a weak positive τ–barrier correlation in the full library (Pearson r = +0.026, Spearman ρ = −0.002) essentially vanishes in the X-ray-only subset (r = +0.002); (3) for four features with moderate full-library correlations (|r| > 0.15)—hydrophobic fraction, aromatic fraction, backbone hydrogen-bond density, packing density μ—the correlations attenuate by 64–94% in the X-ray-only subset. Spearman ρ attenuation mirrors Pearson r, ruling out outlier-driven artifacts. Only geometric order Δ retains 76% of its signal (full-library r = −0.149; X-ray r = −0.113), owing to its measurement of backbone geometric quality, which is least coupled to experimental method. The EM subset (n = 29,057) yields results intermediate between X-ray and NMR. Conclusions: The majority of composition-ratio-based structure–dynamics correlations observed in mixed PDB analyses are statistical echoes of systematic differences between NMR and X-ray across experimental technique, physical environment, and data-processing convention—not reflections of genuine physical laws. We propose a "control size first, then stratify by method" standard for PDB statistical analyses. Any whole-library association not validated in the X-ray-only subset may be substantially overestimated. The deeper implication is that the field implicitly treats crystal-environment-derived standards as "universal," and applying them without calibration to solution-state proteins produces systematic scoring offsets—a meta- scientific issue extending beyond data cleaning to the fundamental interpretation of mixed-source biological big data.Keywords: PDB, systematic methodological bias, confounding variable, NMR, X-ray crystallography, structure–dynamics correlation, structural bioinformatics, reproducibility, stratified analysis.