Sequence-context-aware decoding enables robust reconstruction of protein dynamics from crystallographic B-factors

While X-ray crystallography remains the primary source of protein structures, the B-factors associated with these coordinates are frequently obscured by crystal packing and refinement artifacts that limit their utility for quantifying solutionstate dynamics. We address the disparity between static structural abundance and dynamic information scarcity by introducing the B-Factor Corrector. This fine-tuned protein language model redefines B-factor analysis as a sequenceto-dynamics translation task rather than a purely physical calculation. By leveraging deep contextual embeddings to decouple intrinsic flexibility from lattice constraints, our model recovers ground-truth conformational fluctuations derived from structural ensembles and achieves a Pearson correlation of 0.80. We further demonstrate the utility of these sequence-inferred dynamics in structural biology applications where the model identifies binding-competent conformations for flexible molecular docking, unmasks cryptic mechanical hinges essential for chaperone function, and guides the energetic refinement of antibody interfaces to resolve steric conflicts. These results suggest that static crystallographic data can be effectively repurposed to decode accurate dynamic patterns latent within the Protein Data Bank.