Protein Sequence Annotation And Function Prediction

via “masked-position-prediction-with-context”

fill-mask model by undefined. 17,26,250 downloads.

Unique: Leverages 33 transformer layers trained on 250M diverse protein sequences to capture multi-scale evolutionary and functional patterns — the model learns implicit structural constraints and functional requirements without explicit 3D structure input, enabling predictions that correlate with experimentally-validated amino acid substitutions better than simple conservation-based methods

vs others: More accurate than position-specific scoring matrices (PSSMs) or conservation-based methods for predicting functional amino acids, and faster than structure-based design tools like Rosetta while maintaining competitive performance on protein engineering benchmarks