A Discrete Language of Protein Words for Functional Discovery and Design

Proteins function through hierarchical modules, yet conventional models treat sequences as linear strings of residues, overlooking the recurrent multi-residue patterns--or "Protein Words"--that govern biological architecture. We introduce a physics-aware framework that discretizes protein space into a learnable vocabulary derived from the evolutionary record. By encoding proteins as sequences of discrete "words," our model captures higher-order structural and functional signals inaccessible to residue-level models, achieving highly competitive performance against widely established baselines in remote homology and mutation effect prediction. Analysis across 54 species reveals that these words track evolutionary complexity, specifically identifying the expansion of eukaryotic disordered regions. We demonstrate the discovery potential of this semantic axis by identifying ADMAP1 as a previously uncharacterized regulator of sperm motility, validated via CRISPR-Cas9 knockout mice. Finally, this vocabulary enables programmable design, generating functional cofilin variants despite high sequence divergence. This work establishes a linguistically inspired framework for deciphering the dark proteome and engineering biological function.