AI Insight
This study introduces frequency-space mechanics, a framework that represents proteins as mechanical harmonics graphs (MHGs) built from vibrational modes derived from molecular dynamics simulations, entirely independent of amino acid sequence or atomic coordinates. A graph neural network trained on 5,238 SwissProt proteins using only these vibrational representations successfully predicts Gene Ontology molecular function terms, demonstrating that collective vibrational dynamics alone carry sufficient information to classify broad protein function. Additionally, applying Kuramoto entrainment to the harmonic coupling graph improved predictions for proteins with conformational dynamics, and on the fold-switching protein CLIC1, this approach recovered both functional states with substantially amplified signal.
Why it matters
This framework offers a fundamentally new approach to protein function prediction that bypasses sequence and structural homology, which could benefit the annotation of highly divergent or structurally novel proteins where conventional methods fail. The compatibility with quantum annealing hardware also suggests potential computational advantages as quantum technologies mature.
arXiv:2605.13899v1 Announce Type: new
Abstract: Protein function prediction is dominated by representations grounded in sequence and static structure, neither of which captures the collective vibrational dynamics through which proteins act. Here we introduce frequency-space mechanics, a representational framework in which a protein is encoded as a mechanical harmonics graph (MHG): nodes are vibrational modes derived from molecular dynamics, and edges are harmonic couplings weighted by octave alignment between mode frequencies. The representation is coordinate-free, sequence-independent, scale-invariant, and inhabits a latent mechanical space in which the original atomic coordinates have been projected out. The same construction applies to any system with a tractable eigendecomposition. Trained on 5,238 SwissProt proteins under a strict 30% sequence-identity split and using no sequence information, a graph neural network over static MHGs predicts GO molecular function terms across the ontology, demonstrating that vibrational physics alone encodes broad functional class. Kuramoto entrainment of the harmonic coupling graph, formally a Hamiltonian operation over mode frequencies and directly compatible with quantum annealing hardware, improves prediction for proteins whose function depends on collective conformational dynamics. On CLIC1, a fold- and function-switching chloride channel excluded from training, entrainment amplifies channel-activity signal 7.5-fold and antioxidant signal 2.4-fold, recovering both functional states from dynamics alone.