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arXiv · 2511.19423

Beyond Protein Language Models: An Agentic LLM Framework for Mechanistic Enzyme Design

Bruno Jacob·Khushbu Agarwal·Marcel Baer·Peter Rice·Simone Raugei
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Abstract

We present Genie-CAT, a tool-augmented large-language-model (LLM) system designed to accelerate scientific hypothesis generation in protein design. Using metalloproteins (e.g., ferredoxins) as a case study, Genie-CAT integrates four capabilities -- literature-grounded reasoning through retrieval-augmented generation (RAG), structural parsing of Protein Data Bank files, electrostatic potential calculations, and machine-learning prediction of redox properties -- into a unified agentic workflow. By coupling natural-language reasoning with data-driven and physics-based computation, the system generates mechanistically interpretable, testable hypotheses linking sequence, structure, and function. In proof-of-concept demonstrations, Genie-CAT autonomously identifies residue-level modifications near [Fe--S] clusters that affect redox tuning, reproducing expert-derived hypotheses in a fraction of the time. The framework highlights how AI agents combining language models with domain-specific tools can bridge symbolic reasoning and numerical simulation, transforming LLMs from conversational assistants into partners for computational discovery.

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Bruno Jacob, Khushbu Agarwal, Marcel Baer, Peter Rice, Simone Raugei. 2025-11-24. Beyond Protein Language Models: An Agentic LLM Framework for Mechanistic Enzyme Design. https://arxiv.org/abs/2511.19423

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