Protein Design with Agent Rosetta: A Case Study for Specialized Scientific Agents

Jacopo Teneggi, Tanya Marwah, Alberto Bietti, P. Douglas Renfrew, Vikram Khipple Mulligan, Siavash Golkar

Track: Track 1: Original Research/Position/Education/Attention Track

Keywords: AI agents, protein design, Rosetta Macromolecular Modeling Suite

Abstract: Large language models (LLMs) are increasingly capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex scientific tasks. Protein design provides a natural case study: existing deep learning models achieve strong results, but they are typically restricted to canonical amino acids and narrow objectives, leaving space for a generalist tool for broad design pipelines. We introduce Agent Rosetta, an LLM agent built on top of the Rosetta suite---the leading physics-based software for heteropolymer design, capable of modeling non-canonical building blocks and geometries. Agent Rosetta is a single-agent, multi-turn framework that iteratively refines heteropolymers to achieve the goals of a user-defined task brief, combining the biophysical knowledge of modern LLMs with the accuracy of Rosetta's physics-based methods. In evaluations, Agent Rosetta achieves performance comparable to specialized deep learning models, especially when combined with inference-time techniques such as best-of-n sampling. Interestingly, we find that prompt engineering alone is insufficient for reliably producing RosettaScripts actions. This underscores the need for building a comprehensive environment that, for example, simplifies the most challenging aspects of RosettaScripts syntax. These results demonstrate that combining frontier LLMs with established domain-specific scientific tools can yield flexible agentic frameworks that not only lower barriers to use but also achieve performance competitive with specialized deep learning models.

Submission Number: 279