Validation of an LLM-based Multi-Agent Framework for Protein Engineering in Dry Lab and Wet Lab

TL;DR

This paper validates the effectiveness of the TourSynbio-Agent, an LLM-based multi-agent framework, in automating and improving both computational and experimental protein engineering tasks through five diverse case studies.

Contribution

It provides a comprehensive validation of the TourSynbio-Agent framework, demonstrating its practical utility in real-world protein engineering applications.

Findings

Successfully predicted mutations and protein folding.

Engineered P450 proteins with 70% improved selectivity.

Developed reductases with 3.7x enhanced catalytic efficiency.

Abstract

Recent advancements in Large Language Models (LLMs) have enhanced efficiency across various domains, including protein engineering, where they offer promising opportunities for dry lab and wet lab experiment workflow automation. Previous work, namely TourSynbio-Agent, integrates a protein-specialized multimodal LLM (i.e. TourSynbio-7B) with domain-specific deep learning (DL) models to streamline both computational and experimental protein engineering tasks. While initial validation demonstrated TourSynbio-7B's fundamental protein property understanding, the practical effectiveness of the complete TourSynbio-Agent framework in real-world applications remained unexplored. This study presents a comprehensive validation of TourSynbio-Agent through five diverse case studies spanning both computational (dry lab) and experimental (wet lab) protein engineering. In three computational case studies, we evaluate the TourSynbio-Agent's capabilities in mutation prediction, protein folding, and protein design. Additionally, two wet-lab validations demonstrate TourSynbio-Agent's practical utility: engineering P450 proteins with up to 70% improved selectivity for steroid 19-hydroxylation, and developing reductases with 3.7x enhanced catalytic efficiency for alcohol conversion. Our findings from the five case studies establish that TourSynbio-Agent can effectively automate complex protein engineering workflows through an intuitive conversational interface, potentially accelerating scientific discovery in protein engineering.