AI-designed proteins can produce evolution paths that outperform natural ones
EricTopol · x · 2026-07-22
A new study argues that AI can design protein starting points that are better than natural ones for subsequent evolution.
The paper says the authors used AI-based protein sequence design to redesign three botulinum proteases, then ran side-by-side evolution campaigns against wild-type proteins. The redesigned proteins produced more active and more stable evolved variants, and one route achieved 79× greater selected specificity for ataxin‑2 than the best wild-type-derived variant.
The takeaway is a practical workflow: use AI-redesigned proteins as starting points to unlock evolutionary paths that are otherwise hard to reach.
More from Research
- Token Collapse: Study Finds Graph Soft Prompting Tokens in LLMs Fail to Encode Structure — Sapienza · 2026-10-27
- Mathathon 2.0 launches with Terence Tao's SAIR, $1.5M in compute for teams — _sathvikr · 2026-10-27
- The 'sweet lesson' is real: two kinds of ML problems need opposite strategies — mike64_t · 2026-10-27
- PixGR: Pixel-space generative decoding for fast feed-forward 3D reconstruction — kwangmoo_yi · 2026-10-27
- UniAction: unified action conditioning lets one world model handle mixed action types — ZeYanjie · 2026-10-27
- Infini-AI Lab open-sources Hermit, a framework for agent harness evolution and continual learning — BeidiChen · 2026-10-27