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New Phytol., Jul 30 2024, | https://doi.org/10.1111/nph.20012
READRetro: natural product biosynthesis predicting with retrieval-augmented dual-view retrosynthesis
Taein Kim1*, Seul Lee2*, Yejin Kwak3, Min-Soo Choi1, Jeongbin Park3, Sung Ju Hwang2,4 andSang-Gyu Kim1
1Department of Biological Sciences, KAIST, Daejeon, 34141, Korea;
2Kim Jaechul Graduate School of AI, KAIST, Daejeon, 34141, Korea;
3Department of BioMedical ConvergenceEngineering, Pusan National University, Yangsan, 50612, Korea;
4School of Computing, KAIST, Daejeon, 34141, Korea
*These authors contributed equally to this work.
Authors for correspondence: Sung Ju Hwang, Sang-Gyu Kim
Abstract
Plants, as a sessile organism, produce various secondary metabolites to interact with the environment. These chemicals have fascinated the plant science community because of their ecological significance and notable biological activity. However, predicting the complete biosynthetic pathways from target molecules to metabolic building blocks remains a challenge. Here, we propose retrieval-augmented dual-view retrosynthesis (READRetro) as a practical bio-retrosynthesis tool to predict the biosynthetic pathways of plant natural products. Conventional bio-retrosynthesis models have been limited in their ability to predict biosynthetic pathways for natural products. READRetro was optimized for the prediction of complex metabolic pathways by incorporating cutting-edge deep learning architectures, an ensemble approach, and two retrievers. Evaluation of single- and multi-step retrosynthesis showed that each component of READRetro significantly improved its ability to predict biosynthetic pathways. READRetro was also able to propose the known pathways of secondary metabolites such as monoterpene indole alkaloids and the unknown pathway of menisdaurilide, demonstrating its applicability to real-world bio-retrosynthesis of plant natural products. For researchers interested in the biosynthesis and production of secondary metabolites, a user-friendly website (https://readretro.net) and the open-source code of READRetro have been made available.
논문정보
- Research article
- 2024년 07월 (BRIC 등록일 2024-08-01)
- 국내 연구진
- 생명과학 > 식물학
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