S2S2Fun: Decoding Protein Function From Latent Structural Representations

Predicting mutational effects on protein function from sequences alone remains an unsolved challenge, despite its importance for protein engineering. Protein functions such as enzymatic activity are highly sensitive to mutations in a structure-dependent manner. Recent advances in structure prediction including AlphaFold3 and its open-source counterparts have enabled atomic-level modeling of biomolecular complexes. We hypothesize that AlphaFold3’s latent structural features of protein--ligand complexes can be harnessed for decoding functional differences of sequence variants. Focusing on the optical properties of light-sensitive proteins, we demonstrate that AlphaFold3 $pair$ and $single$ representations can effectively predict absorption peaks, fluorescence brightness, and protein stability of natural and de novo designed proteins. Our ''sequence-to-structure-to-function (S2S2Fun)'' approach offers an effective method for ranking protein function and provides an in silico metric for metagenomic protein discovery and protein engineering applications.