research
The Protein World Model Learns One Mutation at a Time
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.
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.
Limits and context
- Those campaign results establish utility in the tested proteins, not a universal protein-engineering guarantee.
Key claims
ProTEUS combines mutation-biased attention, a fast geometry-constrained predictor and experimental feedback for closed-loop evolution.
Qualification: Those campaign results establish utility in the tested proteins, not a universal protein-engineering guarantee.
Evidence: source-2026-11-12-009
Sources
- arXiv:2611.13348v1arXiv / Cornell University · primary research
Corrections
No corrections have been recorded for this story.