On the Structural Precision Required for Ligand-Specific Elastic Network Dynamics of AI Co-Folded GPCR Structures

Background/Objectives: AI-based protein–ligand co-folding methods such as Boltz-2 are able to reproduce experimentally determined ligand binding poses at G protein-coupled receptors (GPCRs) with increasing accuracy and precision. As these models advance, they raise several natural questions: assuming the correct placement of a ligand, do current methods generate structural ensembles that are sufficient as a starting point for predictive analysis of receptor dynamics? Does the noise introduced between predictions render these unreliable, or can they be trusted to a degree comparable with experimental structures (which themselves carry inherent noise)? We investigate this at the serotonin 5-HT2A receptor (5-HT2AR), leveraging a rich panel of experimentally determined structures and prior benchmarking of Boltz-2 at this receptor. Methods: First, a reference dynamic landscape is established by performing anisotropic network model (ANM) analysis across 11 published cryo-EM and crystal structures trimmed to a uniform 236-residue analysis mask spanning the structured receptor (TM1-H8). We then ask whether Boltz-2 co-folding ensembles recapitulate these experimentally derived dynamics. Results: All nine Gq-coupled agonist structures converge to near-identical dynamic subspaces (mean RMSIP = 0.970 ± 0.019), discriminating activation state but not agonist identity, chemotype, or hallucinogenic status. Principal component analysis of the Boltz-2 ensemble yields moderate, highly uniform overlap with the experimental ANM modes (mean RMSIP = 0.599 ± 0.007), with the first two principal components capturing 56.9% of structural variance. Conclusions: Replicate predictions of the same ligand are highly self-consistent yet show no usable ligand-specific discrimination (within- versus between-ligand effect size is negligible). These findings suggest a quantitative benchmark for the precision required to derive elastic network dynamics from predicted structures and support the distinction between learned pose recapitulation and genuine biophysical prediction.