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Stability and Vibrations of Proteins in Vacuum and Water: Bridging Quantum Accuracy and Force-Field Efficiency

Predicting biomolecular thermodynamics and spectroscopy requires accurate relative energies of metastable states and local curvatures on the potential-energy surface. We show that the general-purpose SO3LR machine-learned force field (MLFF) reproduces PBE0+MBD density-functional theory with unprecedented fidelity across bio-relevant molecules spanning sizes and complexities far beyond its training dataset. For 23 small molecules, SO3LR captures harmonic and anharmonic vibrational features, including frequencies, displacement patterns, and IR spectra. We perform detailed dynamical studies of the amino acid oF-Phe+, folding of the alanine-15 peptide, and assembly of monomeric p53 transactivation domains into tetramers, in vacuum and water. SO3LR consistently reproduces DFT-level potential-energy surfaces, vibrational densities of states, and mode eigenvectors, capturing anharmonicity, polarization, and medium-range environment-driven interactions crucial for proteins. Our results establish that MLFF-driven dynamics provide a quantum-accurate picture of metastable minima and vibrational properties, achieving DFT-level accuracy at force-field computational cost and opening new possibilities for the computational study of biomolecules.