Laboratory of Informatics of Molecular Functions (AIST)

AIST（ODAIBA)

Visiting Professor Kentaro TOMII
AIST（ODAIBA）
E-mail: k-tomii{at}aist.go.jp
Lab HP

Introduction

【Key Words】Protein structure prediction, Structure comparison, Protein function prediction, Drug discovery, Sequence analysis

Rapid accumulation of sequence and structure data of biological macromolecules has increased the need for rapid computational analysis of those data. Our laboratory develops new methods used for analysis of those data to acquire new biological knowledge. Most of our work is related to computational structural biology and protein bioinformatics, but we also cross into a wide range of academic fields.

Structure prediction/modeling of proteins and protein complexes

Our lab has developed and released FORTE (Fig. 1)[1], which implements a profile-profile comparison method that is applicable to predict protein and protein complex structures [2]. We have applied this method in model building and refinement of the TOM complex [3,4]. We have also developed DeepECA, which is a novel approach of end-to-end learning for protein contact prediction [5].

Protein ligand-binding site comparison

We have developed a method for performing an exhaustive pairwise comparison of known and putative ligand-binding sites in PDB. We have created a database, called PoSSuM (https://possum.cbrc.pj.aist.go.jp/PoSSuM/) to compile comparison results [6].

Developing tools for computational biology

We have proposed an efficient amino acid substitution matrix, called MIQS [7], employed by FAMSA, DECIPHER and LAST. We have also developed fundamental tools, such as MAFFT (https://mafft.cbrc.jp/alignment/software/mpi.html) [8] and MitoFates (http://mitf.cbrc.jp/MitoFates/) [9], for computational biology and bioinformatics, in collaborative research.

References

K. Tomii et al., FORTE: a profile-profile comparison tool for protein fold recognition. Bioinformatics (2004). DOI: 10.1093/bioinformatics/btg474
T. Nakamura et al., Template-based quaternary structure prediction of proteins using enhanced profile-profile alignments. Proteins. (2017). DOI: 10.1002/prot.25432
T. Shiota et al., Molecular architecture of the active mitochondrial protein gate. Science (2015). DOI: 10.1126/science.aac6428
H. Takeda et al., Mitochondrial sorting and assembly machinery operates by β-barrel switching. Nature (2021). DOI: 10.1038/s41586-020-03113-7
H. Fukuda et al., DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment. BMC Bioinformatics (2020). DOI: 10.1186/s12859-019-3190-x
J. Ito et al., PoSSuM v.2.0: data update and a new function for investigating ligand analogs and target proteins of small-molecule drugs. NAR (2015). DOI: 10.1093/nar/gku1144
K. Yamada et al., Revisiting amino acid substitution matrices for identifying distantly related proteins. Bioinformatics (2014). DOI: 10.1093/bioinformatics/btt694
T. Nakamura et al., Parallelization of MAFFT for large-scale multiple sequence alignments. Bioinformatics (2018). DOI: 10.1093/bioinformatics/bty121
K. D. Yamada et al., Application of the MAFFT sequence alignment program to large data—reexamination of the usefulness of chained guide trees. Bioinformatics (2016). DOI: 10.1093/bioinformatics/btw412
K. Tomii et al., Meta-Align: A Novel HMM-based Algorithm for Pairwise Alignment of Error-Prone Sequencing Reads. bioRxiv (2020). DOI: 10.1101/2020.05.11.087676
Y. Fukasawa et al., MitoFates: improved prediction of mitochondrial targeting sequences and their cleavage sites. Mol Cell Proteomics (2015). DOI: 10.1074/mcp.M114.043083
H. Kaneko et al., Global observation of plankton communities from space. bioRxiv (2022). DOI: 10.1101/2022.09.23.508961

Fig. 1: Protein structure prediction using FORTE