{
  "id": 17996802,
  "title": "Tell this AI what you want a protein to do—and it designs one",
  "url": "https://urgent.news/2026/11/03/tell-this-ai-what-you-want-a-protein-to-do-and-it-designs-one",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-03T17:40:17.000Z",
  "source": {
    "name": "Phys.org",
    "slug": "phys-org",
    "url": "https://phys.org/news/2026-11-ai-protein.html"
  },
  "original_language": "en",
  "account": "New artificial intelligence (AI) technology called Pinal has been developed by researchers at Westlake University in China. This AI model can generate candidate proteins from natural language prompts, eliminating the need for users to have extensive knowledge of molecular structures and geometric constraints.\n\nPinal uses two main modules to operate: T2struct and SaProt-T. The first module, T2struct, takes the natural language description of the protein's desired function and builds a 1D structural blueprint. The second module, SaProt-T, reads this blueprint and generates a matching amino acid sequence based on the original text prompt.\n\nPinal's standout feature is its ability to create multiple structural blueprints, design the best sequence for each, and score each pair to determine the optimal final design. Training Pinal required an extensive dataset of 135 million protein sequences and 1.7 billion protein-text pairs. The researchers tested Pinal by asking it to design four different proteins - a fluorescent protein, a plastic-breaking enzyme, an alcohol dehydrogenase, and a metabolic H-protein.\n\nFor the fluorescent protein, Pinal generated 50,000 sequences, which were filtered down to 29 for testing. Only one of these sequences fluoresced under illumination. While Pinal has shown promise in producing functional metabolic H-proteins, precise control of protein properties remains a challenge. For example, creating a fluorescent protein with a specific emission wavelength is still difficult. However, Pinal excelled in producing a metabolic H-protein that boosted the output of a key carbon-capturing process by 1.7 times that achieved using the natural E. coli protein.",
  "summary": "In a short space of time, artificial intelligence has become a whiz at designing proteins, molecular machines that perform various functions such as speeding up reactions, producing fluorescent signals and breaking down plastic. But getting AI to design the specific protein you want usually requires the user to have a strong understanding of molecular structures and geometric constraints to guide…",
  "key_points": [
    "AI technology Pinal generates proteins from natural language prompts",
    "Two modules: T2struct creates 1D structural blueprint, SaProt-T generates amino acid sequence",
    "Pinal creates multiple blueprints, designs best sequence, scores final design"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}