1Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
2School of Biological Engineering, Tianjin University of Science and Technology, Tianjin, 300457, China
| Received 28 May 2025 |
Accepted 13 Jul 2025 |
Published 26 Jul 2025 |
With the rapid advancements in sustainable development and green chemistry, biotransformation has become increasingly pivotal in the synthesis of bulk chemicals and high-value products. Because natural evolution predominantly favors cellular survival, many valuable compounds, such as 2,4-dihydroxybutanoic acid and 1,2-butanediol, lack corresponding biosynthetic pathways in nature. This limitation calls for the development of fully nonnatural metabolic pathways. By enabling modular design and incorporating novel reactions, such pathways allow efficient de novo synthesis of compounds without known natural biosynthetic pathways. Nonetheless, their implementation may introduce new challenges, such as increased metabolic burden and the accumulation of toxic intermediates. Expanding the scope and efficiency of biotransformation through rational nonnatural pathways has become a key challenge. To address this, researchers have developed various computational methods for nonnatural pathway design, and two major types of methods, template-based and template-free methods, are reviewed here. We evaluate their practical applications in guiding the construction of microbial cell factories and analyze their effectiveness. Additionally, we compiled 55 experimentally validated nonnatural pathways from recent literature to establish a dataset for evaluating the strengths and limitations of these pathway design methods. By simulating a wide range of experimentally verified pathways, we highlight the gaps between computational predictions and empirical feasibility. Finally, we propose potential strategies to bridge these gaps, offering theoretical insights and practical guidance for integrating computational tools with experimental synthetic biology.
