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Simulating 500 million years of evolution with a language model.

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Summary
This summary is machine-generated.

Large language models can now generate novel, functional proteins by learning from evolutionary data. This breakthrough enables the creation of proteins unlike any found in nature, accelerating biological discovery.

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Protein Engineering

Background:

  • Biological systems and protein functions are encoded through billions of years of evolution.
  • Natural proteins represent a vast, yet incompletely understood, biological sequence space.

Purpose of the Study:

  • To demonstrate that large-scale language models trained on evolutionary data can generate novel, functional proteins.
  • To introduce ESM3, a multimodal generative language model for protein sequence, structure, and function.
  • To explore the capabilities of ESM3 in generating proteins with desired characteristics, such as fluorescence.

Main Methods:

  • Training a multimodal generative language model (ESM3) on large-scale evolutionary protein data.
  • Utilizing complex prompts that combine sequence, structure, and function modalities for protein generation.
  • Synthesizing and characterizing generated proteins, including fluorescent proteins.
  • Assessing sequence identity to known proteins to quantify evolutionary distance.

Main Results:

  • ESM3 successfully generated functional proteins distant from known protein families.
  • A novel bright fluorescent protein was generated with 58% sequence identity to known fluorescent proteins.
  • The evolutionary distance of the generated protein is estimated to be equivalent to 500 million years.

Conclusions:

  • Large language models are powerful tools for exploring and expanding the protein sequence space.
  • ESM3 can generate functional proteins that represent significant evolutionary novelty.
  • This approach has the potential to accelerate protein engineering and the discovery of new biological functions.