Advancing microbial production through artificial intelligence-aided biology

• Published in: Biotechnology advances 2024-09, Vol.74, p.108399, Article 108399
• Main Authors: Gong, Xinyu, Zhang, Jianli, Gan, Qi, Teng, Yuxi, Hou, Jixin, Lyu, Yanjun, Liu, Zhengliang, Wu, Zihao, Dai, Runpeng, Zou, Yusong, Wang, Xianqiao, Zhu, Dajiang, Zhu, Hongtu, Liu, Tianming, Yan, Yajun
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Artificial intelligence (AI); Artificial protein design; Enzyme function prediction; Genome annotation; Large language models (LLMs); Metabolic Engineering - methods; Microbial production; Pathway prediction; Protein Engineering - methods; Synthetic biology; Synthetic Biology - methods
• ISSN: 0734-9750; 1873-1899; 1873-1899
• DOI: 10.1016/j.biotechadv.2024.108399

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production. •The integration of artificial intelligence (AI) and biology accelerates the progress of microbial production.•Machine learning have been used in genome mining, protein discovery, protein design, and biosynthetic pathway design.•Large language models (LLMs) represent a novel and efficient approach to interpreting biological language.•Significant challenges remain, but the future of AI-aided microbial production is foreseeable.