Breakthrough Tracker record

AI plus evolutionary constraints produced compact, active RNA-guided genome editors

Researchers combined machine-learning inverse folding with evolutionary and structural constraints to design synthetic TnpB nucleases. Experimentally screened variants matched or exceeded natural TnpB activity in bacterial, plant and human cells, and cryo-EM revealed stabilizing contacts created by the designs.

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Stable ID
science-2026-ai-designed-tnpb-nucleases
Revision
science-2026-ai-designed-tnpb-nucleases.v1
Field
Biology · Protein design and genome editing
Evidence
Tier 2 · Peer reviewed: Yes
Record state
Current · Peer-reviewed laboratory result
Last checked

AI role

Machine-learning inverse folding was a core sequence-design method, combined with evolutionary and structural constraints.

Record details

Problem or result
Many powerful genome editors are difficult to package and deliver, while unconstrained AI protein designs often fail when tested experimentally.
Authors
Petr Skopintsev, Isabel Esain-Garcia, Evan C. DeTurk et al.
Institutions
Innovative Genomics Institute; University of California, Berkeley; University of California, Los Angeles
Result date
July 16, 2026

Why it matters

The study closes a credible loop from AI sequence generation to cell activity and structural explanation while producing editors smaller than commonly used Cas9 proteins.

Limits

This is laboratory and cell evidence, not a therapeutic result. Editing activity does not establish genome-wide specificity, delivery, immunogenicity or safety, and only a small fraction of generated candidates passed experimental screening.

Sources

  1. Primary: Science primary paper
  2. Primary: Preprint and methods context
  3. Primary: Cryo-EM structure record
  4. Independent: Nature specialist reporting

Correction and revision history

  1. 2025-12-08 — Preprint posted.
  2. 2026-07-16 — Peer-reviewed version published in Science.

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