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Daisuke Kihara photo

Daisuke Kihara

Professor of Biological Sciences

Professor of Computer Science

Joined department: 2003


BS, University of Tokyo, Biochemistry (1994)
MS, Kyoto University, Bioinformatics (1996)
PhD, Kyoto University, Bioinformatics (1999)

Dr. Kihara's research interest is in the area of bioinformatics. In the last decade, a large amount of biological data, such as genome/protein sequences, protein 3D structures, and pathway data have become available. This data now enables us to employ comprehensive analysis of relationship between protein sequence, structure and function, interactions, evolution of protein families, pathways, and organisms. Especially, he is focusing on developing computational methods to predict and analyze protein structure/function, protein-protein docking, pathway structure, and their applications in genome-scale or pathway/network scale. He has worked recently on protein structure prediction, protein global/local shape comparison, development of prediction method of transmembrane proteins, and its application to genome sequences.


Selected Publications

Xiao Wang, Eman Alnabati, Tunde W. Aderinwale, Sai Raghavendra Maddhuri Venkata Subramaniya, Genki Terashi, & Daisuke Kihara, Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning. Nature Communications, 12: 2302 (2021)

Xusi Han, Genki Terashi, Charles Christoffer, Siyang Chen, & Daisuke Kihara, VESPER: global and local cryo-EM map alignment using local density vectors. Nature Communications, 12: 2090.

Sai Raghavendra Maddhuri Venkata Subramaniya, Genki Terashi, & Daisuke Kihara, Protein secondary structure detection in intermediate-resolution cyor-EM maps using deep learning. Nature Methods, 16: 911-917 (2019)

Xiao Wang, Sean Flannery, & Daisuke Kihara, Protein docking model evaluation by graph neural networks. Frontiers in Molecular Biosciences, 8: 647915 (2021)

Sai Raghavendra Maddhuri Venkata Subramaniya, Genki Terashi, Aashish Jain, Yuki Kagaya, & Daisuke Kihara, Protein contact map refinement for improving protein structure prediction using generative adversarial networks. Bioinformatics, 37(19):3168-3174 (2021)

Last Updated: Dec 4, 2020 1:55 PM

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