Supported measurements in ubiquitin. SGC induces a deterministic depth-dependent structural transition with a specific early “block-2” anomaly and a steep transition regime, quantified by RMSD and heavy-atom Q-factor.
Topological correspondence to MD. The pooled SGC conformational landscape shows an L-shaped RMSD×Q funnel and reports kNN “territorial overlap” with 390 K MD folding contours (72.6% in heavy-atom Q space). This is a topology/density-support match, not a thermodynamic ensemble match.
Flexibility tracking beyond geometry. SGC “perturbation sensitivity” correlates with MD equilibrium RMSF (pattern r≈0.83–0.89 at depths 5–20) and retains nontrivial partial correlation after controlling for weighted contact number (WCN).
Key discriminator: matched-power incoherent noise. White/spectral noise controls are seed-sensitive and qualitatively different (atomized debris or bimodal outcomes) rather than graded, seed-robust transitions, supporting that SGC’s structured output depends on deterministic weight-aligned perturbation rather than perturbation magnitude alone.
Interpretation risk (what is inferred, not proven). The paper interprets correspondences as “encoded conformational landscapes” in weights, but this leap is not uniquely determined: similar topologies could in principle arise from architectural dynamics under coherent attenuation, training-set exposure, or metric-specific projection geometry. The design addresses this partly (multi-model checks in ubiquitin; fold-switched absence in KaiB; model-dependent structured disagreement in α-synuclein) but does not provide a mechanistic “if-and-only-if” proof.
One falsification lever. If future probes (e.g., single-block/per-head targeted perturbations, broader protein panels, and alternative perturbation schemes) fail to reproduce ordered/MD-consistent topology-support patterns, the central “neural spectroscopy reads out encoded landscapes” claim would weaken substantially.
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