We conduct bioinformatics research with a focus on structural and functional prediction of proteins and genome/transcriptome analysis.
Development of protein interaction prediction systems
The followings are the three approaches to study protein interactions in our laboratory.
- Interaction prediction: To predict whether a given protein interacts with other molecules
- Binding-site prediction: To predict residues in a given protein that interact with other molecules and when the structure of a protein is known, to predict the binding site location Docking prediction: To predict the structure of complexes
In 2018, we developed a system for predicting the protein―protein, ―DNA, ―RNA, ―lipid, and ―metal binding sites and achieved the world’s highest prediction accuracy for each of them. By integrating techniques for protein interaction prediction/binding-site prediction that we have developed or are currently developing, we are constructing a system which enables us to simultaneously predict whether a given protein interacts with proteins, nucleic acids, sugars, lipids, or metals, and if the protein indeed interacts, predict its interaction site. We also analyze physical interactions using molecular simulation.
- K. Shimizu, W. Cao, G. Saad, M. Shoji, and T. Terada: Comparative analysis of membrane protein structure databases, BBA - Biomembranes, 1860, 1077-1091 (2018). (K.S. and W.C. are equal contribution.)
- C. Fang, Y. Moriwaki, A. Tian, C. Li, and K. Shimizu: Identifying short disorder-to-order binding regions in disordered proteins with a deep convolutional neural network method, Journal of Bioinformatics and Computational Biology, doi: 10.1142/s0219720019500045 (2018).
- S. Zhao, J. Sun, K. Shimizu, and K. Kadota: Silhouette scores for arbitrary defined groups in gene expression data and insights into differential expression results, Biological Procedures Online, 20, 5 (2018).
- X. Fu, J. Sun, E. Tan, K. Shimizu, Md. S. Reza, S. Watabe, and S. Asakawa: High-throughput sequencing of the expressed torafugu (Takifugu rubripes) antibody sequences distinguishes IgM and IgT repertoires and reveals evidence of convergent evolution, Frontiers in Immunology, 9, 251 (2018).
- C. Fang, A. Tian, and K. Shimizu: Identifying MoRFs in Disordered Protein Using Enlarged Conserved Features, 2018 6th International Conference on Bioinformatics and Computational Biology, 2351-2366 (2018).