Beyond Protein Language Models: An Agentic LLM Framework for Mechanistic Enzyme Design
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911283963494400 |
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| author | Jacob, Bruno Agarwal, Khushbu Baer, Marcel Rice, Peter Raugei, Simone |
| author_facet | Jacob, Bruno Agarwal, Khushbu Baer, Marcel Rice, Peter Raugei, Simone |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19423 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Protein Language Models: An Agentic LLM Framework for Mechanistic Enzyme Design Jacob, Bruno Agarwal, Khushbu Baer, Marcel Rice, Peter Raugei, Simone Quantitative Methods Artificial Intelligence 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. |
| title | Beyond Protein Language Models: An Agentic LLM Framework for Mechanistic Enzyme Design |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2511.19423 |