A Sonification Framework for GPCR Molecular Dynamics: Auditory Signatures of β2-Adrenergic Receptor

Sonification, the systematic mapping of data to non-speech sound, has been applied with quantitative success in astronomy, seismology, and most recently materials chemistry, but has seen limited use in the analysis of biomolecular dynamics. Earlier protein-music studies have focused largely on the static amino-acid sequence, and G-protein-coupled receptor (GPCR) molecular dynamics (MD) trajectories have not previously been the subject of an auditory display framework. Here we present an end-to-end open-source sonification framework for GPCR molecular dynamics together with a quantitative cross-modal validation procedure, and we apply the framework as a proof of concept to three reference {beta}2-adrenergic receptor ({beta}2AR) trajectories from the GPCRMD repository spanning the activation continuum (inactive, active apo, active + orthosteric agonist). The framework extracts activation-related geometric features per MD frame, maps them under a single rule onto pitch, note duration, velocity, harmonic intensity, and percussive accents, and renders the result with three timbres (piano, violin, flute). The mapping was tested on two designed pairwise contrasts (activation pair; ligand pair) using Mann-Whitney U tests, Random Forest cross-modal classification with leave-one-instrument-out generalisation, and canonical correlation analysis between the MD and audio feature spaces. All four informative MD features differed between paired states at q < 1 x 10-20. A Random Forest classifier trained on audio features alone recovered the MD state with balanced accuracy 0.995 {+/-} 0.003 (activation pair) and 1.000 {+/-} 0.000 (ligand pair), corresponding to information-retention ratios of 1.006 and 1.000 relative to the MD-feature baseline. First canonical correlations between MD and audio spaces reached r1 = 0.926 (activation) and r1 = 0.995 (ligand). The sonification framework therefore provides a quantitatively faithful auditory representation of GPCR activation dynamics, with potential applications in exploratory MD analysis, accessibility, and education. The framework is system-agnostic and transfers to other GPCRs and allosteric MD systems without code changes beyond residue selection.