Miniaturizing and modifying natural proteins with Raygun

Proteins have evolved over billions of years through coordinated substitutions, insertions and deletions, yet computational protein design cannot fully replicate nature’s ability to engineer new proteins from existing templates. Protein language models1–3 generate informative per-residue representations, but harnessing them for large-scale, function-preserving sequence modifications has remained beyond reach. Here we introduce Raygun, a generative artificial intelligence framework that enables miniaturization, modification and augmentation of proteins, using a probabilistic encoding of protein sequences constructed from language model embeddings. Our key conceptual advance is to encode each protein not as a sequence of variable length in high-dimensional space, but as a probability distribution in fixed dimensions, making proteins of any length directly commensurable. Controlled by just two parameters governing substitutions and length changes, Raygun can shrink proteins by 10–25% (sometimes more than 50%), expand them beyond their natural size, and introduce extensive sequence diversity, all while preserving predicted structural integrity and functional sites. In cell-based validation, Raygun miniaturized fluorescent proteins (2 shorter than 96% of fluorescent proteins in FPbase) and TurboID, a synthetic biotin ligase that has been widely adopted for proteomics. It also expanded epidermal growth factor (EGF), generating variants with higher EGFR-binding affinity than the wild type. These results show that protein function can be faithfully captured in a length-agnostic representation, enabling the kind of coordinated, large-scale sequence modifications that characterize natural protein evolution. Probabilistic sequence encoding from language model embeddings within a generative AI framework enables protein miniaturization, modification and augmentation while maintaining native architecture and functional integrity.