Author

Hideaki Oikawa

Hokkaido UniversityORCID 0000-0002-6947-3397Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:protein structure prediction· 2 Approach:deep learning· 1 Assay:protein sequence· 1 Assay:protein structure· 1 Approach:statistical model· 1

Frequent Co-Authors

Atsushi MinamiiD· 1 shared Bin Zhu· 1 shared Cuiping Xing· 1 shared Guoliang ZhuiD· 1 shared Jie ZhangiD· 1 shared Kangjie Lv· 1 shared Kenichi MatsudaiD· 1 shared Keying Lan· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperBioinformatics

Atsushi MinamiORCID

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2 papers

Recent·Most cited

TRACER navigates rearrangement-driven sesterterpene chemical space via multimodal enzyme-product representation learning

Cuiping Xing, Kangjie Lv, Weiyan Zhang, Yuwei Chen, Keying Lan, Guoliang Zhu, Bin Zhu, Shou‐Mao Shen, Xinjun Zhang, Yucheng Gu, Yue‐Wei Guo, Hideaki Oikawa, Tom Hsiang, Lixin Zhang, Youyuan Li, , Xueting Liu

TRACER predicts terpene skeletal rearrangements by learning enzyme-product associations, identifying novel carbon skeletons and mechanistic insights.

bioRxiv · Aug 19, 2026 · PreprintSynthetic Biology

High-throughput prediction of protein-protein interactions uncovers hidden molecular networks in biosynthetic gene clusters

Yoshitaka Moriwaki, Taro Shiraishi, Yohei Katsuyama, Kenichi Matsuda, Jie Zhang, Toyoyuki Ose, Atsushi Minami, Hideaki Oikawa, Tomohisa Kuzuyama, Ryuichiro Ishitani, Tohru Terada

The paper predicts protein-protein interactions in biosynthetic gene clusters to uncover hidden molecular networks and functional enzyme complexes.

Computational and Structural Biotechnology Journal · Mar 1, 2026Peer-reviewedBioinformatics