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Jan. 28, 2025, 4:30 p.m.
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AI Simulates 500 Million Years of Molecular Evolution to Create New Protein

Brief news summary

A recent study highlighted the advanced capabilities of the AI model ESM3, which simulated 500 million years of molecular evolution to create a new protein, esmGFP. This protein exhibits green fluorescence akin to natural proteins from jellyfish and coral, paving the way for innovative drug discovery. As proteins are essential for numerous biological functions and have diverse structures and sequences, ESM3 addresses gaps in incomplete protein sequences, allowing for the engineering of proteins beyond typical evolutionary constraints. Utilizing a vast dataset of 2.78 billion proteins, ESM3 has derived substantial insights into biological principles, enabling the design of proteins with optimized long-term evolutionary potential. Notably, esmGFP shows only 58% sequence similarity to its nearest fluorescent counterpart, showcasing its unique genetic characteristics. Published in Science, this research underscores the transformative role of AI in protein engineering while cautioning experts about the complexities involved in protein evolution.

A recent study has revealed that an artificial intelligence (AI) model simulated 500 million years of molecular evolution to design the code for a new and previously unknown protein. This glowing protein, named esmGFP, resembles those found in jellyfish and corals and could aid in developing new medications. Proteins, essential for various bodily functions including muscle building and disease fighting, are made up of amino acids whose sequences are dictated by genes. The esmGFP protein exists only in computer code and represents a unique type of green fluorescent protein, with only 58% sequence similarity to the closest known fluorescent protein—a modified version of one from bubble-tip sea anemones. Creating esmGFP naturally would require 96 genetic mutations over millions of years. The AI model, called ESM3, was introduced by EvolutionaryScale and has been validated by independent scientists.

Unlike traditional evolutionary processes, ESM3 fills in incomplete protein sequences, leveraging insights from a vast dataset of 2. 78 billion natural proteins. This gap-filling approach helps generate potential protein structures that evolution might not have produced. Researchers trained ESM3 using properties of proteins, allowing it to predict and complete protein codes like a person completing sentences. The implications of this research extend to various applications in protein engineering, including drug design. However, while experts like Tiffany Taylor from the University of Bath acknowledge the potential of AI in protein engineering, she cautions that such models might oversimplify the complex processes shaped by millions of years of natural selection.


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