Fast-Forwarding Evolution: AI Mimics 500 Million Years of Biology

Researchers used AI model ESM3 to simulate 500 million years of evolution and create a novel fluorescent protein, revolutionizing protein engineering.

Using a multimodal generative language model called ESM3, Thomas Hayes and colleagues designed and synthesized a novel bright fluorescent protein with a genetic sequence vastly different from any known fluorescent proteins. The researchers note that this achievement is comparable to ESM3 simulating 500 million years of biological evolution.

This approach offers a groundbreaking method for “searching” the vast landscape of potential proteins, enhancing our understanding of naturally evolved proteins and enabling the creation of new proteins for applications in medicine, environmental remediation, and numerous other fields.

How ESM3 Works: A New Approach to Protein Modeling

ESM3 can reason over protein sequence, structure, and function, by representing each of these through alphabets of discrete tokens that can be combined in a generative language model. This strategy differs from previous uses of language models that were only scaled for protein sequences.

The training data for ESM3 consists of 771 billion unique tokens created from 3.15 billion protein sequences, 236 million protein structures, and 539 million proteins with function annotations. ESM3 can train up to 98 billion parameters.

ESM3 is now available in public beta via an API, enabling scientists to engineer proteins programmatically or through interactive browser-based apps. Researchers can use the EvolutionaryScale Forge API through the free academic access tier or use the code and weights of the open model.

Reference: “Simulating 500 million years of evolution with a language model” by Thomas Hayes, Roshan Rao, Halil Akin, Nicholas J. Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q. Tran, Jonathan Deaton, Marius Wiggert, Rohil Badkundri, Irhum Shafkat, Jun Gong, Alexander Derry, Raul S. Molina, Neil Thomas, Yousuf A. Khan, Chetan Mishra, Carolyn Kim, Liam J. Bartie, Matthew Nemeth, Patrick D. Hsu, Tom Sercu, Salvatore Candido and Alexander Rives, 16 January 2025, Science.
DOI: 10.1126/science.ads0018

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