Computational Biology Group／Inter-Institute Cooperative LaboratoriesTomii Laboratory
(Informatics of Molecular Functions Group, AIST)
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Visiting Professor TOMII Kentaro
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.
- Research
keywords - Protein structure prediction, Structure comparison, Protein function prediction, Drug discovery, Sequence analysis
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.
Fig. 1: Protein structure prediction using FORTE
