Theme

We conduct bioinformatics research with a focus on structural and functional prediction of proteins and genome/transcriptome analysis.

About Research

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.

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 various molecules, and if the protein indeed interacts, predict its interaction site.

Publication

  1. 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.)
  2. 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).
  3. 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).
  4. 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).
  5. 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).