Generic prediction of natural product biosynthetic pathways from large-scale measurement data

2019 Fiscal Year Final Research Report

• Project/Area Number: 17K07260
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: System genome science
• Research Institution: The University of Tokyo (2018-2019); Tokyo Institute of Technology (2017)
• Principal Investigator: Kotera Masaaki 東京大学, 大学院工学系研究科(工学部), 准教授 (90643669)
• Project Period (FY): 2017-04-01 – 2020-03-31
• Keywords: 代謝化合物

Outline of Final Research Achievements

In this study, we developed a predictive workflow named the Metabolic Disassembler that automatically disassembles the target molecule structure into relevant biosynthetic units (BUs), which are the substructures that correspond to the starting materials in the biosynthesis pathway. This workflow uses a biosynthetic unit library (BUL), which contains starting materials, key intermediates, and their derivatives. We obtained the starting materials from the KEGG PATHWAY database, and 765 BUs were registered in the BUL. We then examined the proposed workflow to optimize the combination of the BUs. To evaluate the performance of the proposed Metabolic Disassembler workflow, we used 943 molecules that are included in the secondary metabolism maps of KEGG PATHWAY. About 95.8% of them (903 molecules) were correctly disassembled by our proposed workflow. In addition, for 90.7% of molecules, our workflow finished the calculation within one minute.

Free Research Field

化学情報学

Academic Significance and Societal Importance of the Research Achievements

本ワークフローは、正しさと計算時間の両面で天然物の効率的な分解を可能にしました。また、ユーザーが計算結果を理解しやすいように、BNに対応する部分構造を自動的に色分けして出力します。利用者は、出発分子を事前に指定する必要がなく、データベースにない分子であっても、任意のターゲット分子を入力することができます。このワークフローは、天然物の生合成の理解や予測に大いに役立つと考えています。