CAN MOLECULAR-DYNAMICS SUPERVISION HELP PREDICT PROTEIN FUNCTIONS?

Protein functions depend on conformational ensembles, but incorporating molecular-dynamics (MD) information into sequence-based prediction requires supervision that can be learned without trajectories at inference. We introduce ESMLace, an ESM-2 35M encoder trained to predict compressed MD targets: moments, wavelets, cosine coefficients, or discrete VQ-VAE tokens from 96-frame windows. We transfer the frozen encoders to mutation-effect prediction and immune-recognition benchmarks, comparing matched mean-pool and token-level readouts. Across ten ProteinGym assays, VQ supervision changes from −0.008 Spearman relative to Base ESM under mean pooling to +0.166 under a two-block token readout, giving a compression-by-readout interaction of +0.174 (95% assay bootstrap [0.083, 0.271], family-wise p = .031). VQ supervision also improves BATCAVE global peptide-activity ranking from 0.495 to 0.557 Spearman, while antibody and TCR–pMHC results vary by benchmark. Replica and matched-probe analyses identify reproducible MD signals and increased target decodability, while waveform reconstructions distinguish temporal-shape recovery from amplitude recovery. MD supervision can therefore improve sequence-based function prediction, with its benefit determined jointly by temporal representation, readout capacity, and task-label alignment.