AI that designs enzymes—and explains why

Kari Jaaskelainen

25 Nov 2025 — 1 min read

AI that designs enzymes—and explains why

Researchers introduce Genie-CAT, an agentic large language model that acts like a digital lab partner for protein design.

Reads the literature via retrieval to ground its ideas.
Parses 3D structures from the Protein Data Bank.
Computes electrostatic potentials to assess physics.
Uses machine learning to predict redox properties.

Working on metalloproteins such as ferredoxins, Genie-CAT links sequence, structure, and function to propose mechanistic, testable edits—like specific amino acid changes near iron–sulfur clusters to tune redox potential. In demos, it reproduced expert hypotheses in a fraction of the time.

Why it matters: instead of a chatty assistant, this shows how tool-using AI agents can couple language-based reasoning with numerical simulation, turning LLMs into partners for computational discovery.

Paper: https://arxiv.org/abs/2511.19423v1

Paper: https://arxiv.org/abs/2511.19423v1

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AI AgenticAI LLM ProteinDesign EnzymeDesign ComputationalBiology Metalloproteins Redox Science

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