evolutionary sieve natural evolution random mutations selection proteins sequences structures functions
Gene sequencing surveys Equal contribution EvolutionaryScale, PBC Arc Institute University of California, Berkeley Work done during internship at EvolutionaryScale, PBC arives@evolutionaryscale.ai">Correspondence to arives@evolutionaryscale.ai User:
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Earth's natural diversity proteins sequences structures large language models
language models protein sequences biological structure biological function scaling laws artificial intelligence compute parameters data
We present ESM3, a frontier multimodal generative model, that reasons over the sequences, structures, and functions of proteins. ESM3 is trained as a generative masked language model over discrete tokens for each modality. Structural reasoning is achieved by encoding three-dimensional atomic structure as discrete tokens rather than with the complex architecture and diffusion in three-dimensional space employed in recent predictive (22) and generative models $(14,23-25)$ of proteins. All-to-all modeling of discrete tokens is scalable, and allows ESM3 to be prompted with any combination of its modalities, enabling controllable generation of new proteins that respect combinations of prompts.###
ESM3 FLOPs tokens parameters alignment
We report the generation of a new green fluorescent protein (GFP) with ESM3. Fluorescent proteins are responsible for the glowing colors of jellyfish and corals (26) and are important tools in modern biotechnology (27). They share an elegant structure: an eleven stranded beta barrel with a helix that threads its center, which scaffolds the formation of a light-emitting chromophore out of the protein's own atoms. This mechanism is unique in nature-no other protein spontaneously forms a fluorescent chromophore out of its own structure-suggesting that producing fluorescence is hard even for nature.###
Aequorea victoria GFP GFP protein engineering new GFPs in nature
GFPs, natural diversification, evolution