Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation

• URL: http://arxiv.org/abs/2511.22311v1
• Date: Thu, 27 Nov 2025 10:42:52 GMT
• Title: Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation
• Authors: Fiona Y. Wang, Di Sheng Lee, David L. Kaplan, Markus J. Buehler,
• Abstract summary: Large language model (LLM) agents operate in parallel, each assigned to a specific residue position.<n>This position-wise, decentralized coordination enables emergent design of diverse, well-defined sequences.<n>Our method achieves efficient, objective-directed designs within a few GPU-hours and operates entirely without fine-tuning or specialized training.
• Score: 0.9332987715848714
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Designing proteins de novo with tailored structural, physicochemical, and functional properties remains a grand challenge in biotechnology, medicine, and materials science, due to the vastness of sequence space and the complex coupling between sequence, structure, and function. Current state-of-the-art generative methods, such as protein language models (PLMs) and diffusion-based architectures, often require extensive fine-tuning, task-specific data, or model reconfiguration to support objective-directed design, thereby limiting their flexibility and scalability. To overcome these limitations, we present a decentralized, agent-based framework inspired by swarm intelligence for de novo protein design. In this approach, multiple large language model (LLM) agents operate in parallel, each assigned to a specific residue position. These agents iteratively propose context-aware mutations by integrating design objectives, local neighborhood interactions, and memory and feedback from previous iterations. This position-wise, decentralized coordination enables emergent design of diverse, well-defined sequences without reliance on motif scaffolds or multiple sequence alignments, validated with experiments on proteins with alpha helix and coil structures. Through analyses of residue conservation, structure-based metrics, and sequence convergence and embeddings, we demonstrate that the framework exhibits emergent behaviors and effective navigation of the protein fitness landscape. Our method achieves efficient, objective-directed designs within a few GPU-hours and operates entirely without fine-tuning or specialized training, offering a generalizable and adaptable solution for protein design. Beyond proteins, the approach lays the groundwork for collective LLM-driven design across biomolecular systems and other scientific discovery tasks.

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