Chen, Z., Liu, Y., Wang, Y.G., & Shen, Y. (2024). Validation of an LLM-based Multi-Agent Framework for Protein Engineering in Dry Lab and Wet Lab. arXiv preprint arXiv:2411.06029v1.
This study aims to validate the practical utility and effectiveness of TourSynbio-Agent, a novel LLM-based multi-agent framework, in automating diverse protein engineering tasks across both computational (dry lab) and experimental (wet lab) settings.
The researchers designed five case studies to evaluate TourSynbio-Agent's capabilities: three computational and two experimental. The computational studies assessed the framework's performance in mutation effect prediction, protein folding, and protein design. The experimental studies involved engineering P450 proteins for enhanced steroid 19-hydroxylation selectivity and optimizing reductases for improved catalytic efficiency in alcohol compound synthesis.
TourSynbio-Agent effectively bridges the gap between computational protein engineering and experimental validation, offering a powerful tool to accelerate scientific discovery in the field. Its intuitive interface and automated workflow management capabilities make advanced protein engineering techniques more accessible to researchers.
This research highlights the transformative potential of LLMs in automating and accelerating protein engineering workflows. The successful development and validation of TourSynbio-Agent pave the way for more efficient and effective protein engineering strategies across various applications, including therapeutic development and industrial biocatalysis.
Future research directions include establishing standardized evaluation metrics for LLM-based protein engineering frameworks, expanding TourSynbio-Agent's knowledge base and integrated datasets for broader applicability, and exploring its potential for autonomous experimental design and optimization.
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by Zan Chen, Yu... at arxiv.org 11-12-2024
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