Learning Protein Representations with Conformational Dynamics.

MOTIVATION: Proteins change shape as they work, and these changing states control whether binding sites are exposed, signals are relayed, and catalysis proceeds. Most protein language models pair a sequence with a single structural snapshot, which can miss state-dependent features central to interaction, localization, and enzyme activity. Studies also indicate that many proteins assume multiple, functionally relevant shapes, motivating approaches that learn from this variability. RESULTS: We present DynamicsPLM, a protein language model conditioned on ensembles of computationally generated conformations to derive state-aware representations. DynamicsPLM improves predictive performance across protein-protein interaction, subcellular localization, enzyme classification, and metal-ion binding. On a widely used protein-protein interaction benchmark, it achieves a four-point accuracy gain over the strongest baseline. On a curated test set enriched for proteins with multiple conformational states, the margin increases to eleven points. These findings argue for a shift from static to dynamics-aware modeling, in which conformational variability is treated as informative. By elevating conformational state to a central element of machine learning in protein biology, this work advances modeling toward mechanisms that better reflect how proteins operate in cells and provides a route to actionable hypotheses about when and how binding, signaling, and catalysis occur. AVAILABILITY AND IMPLEMENTATION: Code, model weights, and inference scripts are available at https://github.com/kalifadan/DynamicsPLM (DOI: https://doi.org/10.5281/zenodo.17668302). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.