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    "headline": "The Protein World Model Learns One Mutation at a Time",
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    "dek": "ProTEUS combines mutation-biased attention, a fast geometry-constrained predictor and experimental feedback for closed-loop evolution.",
    "summary": "ProTEUS combines mutation-biased attention, a fast geometry-constrained predictor and experimental feedback for closed-loop evolution.",
    "body_text": "ProTEUS treats protein variants as states and individual mutations as actions whose functional effects can be predicted and revised with experimental feedback. Its MutationFormer combines sequence, evolutionary and structural information, while a geometry-constrained all-atom predictor is reported to run more than ten times faster than AlphaFold3 with enforced physical validity. In two closed-loop campaigns on aldehyde dehydrogenase and levansucrase, the authors report variants with more than tenfold higher activity than wild type. Those campaign results establish utility in the tested proteins, not a universal protein-engineering guarantee.",
    "why_it_matters": "ProTEUS combines mutation-biased attention, a fast geometry-constrained predictor and experimental feedback for closed-loop evolution.",
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      "Those campaign results establish utility in the tested proteins, not a universal protein-engineering guarantee."
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    "tags": [
      "protein",
      "evolution",
      "world-model",
      "biology"
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      "title": "arXiv:2611.13348v1",
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    "title": "The Protein World Model Learns One Mutation at a Time",
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