Biomedical subjects

Yukimitsu Yabuki

Publications and source records attributed to Yukimitsu Yabuki.

3 recordsLinked to original sources

PubMed2006

TMBETA-GENOME: database for annotated beta-barrel membrane proteins in genomic sequences.

We have developed the database, TMBETA-GENOME, for annotated beta-barrel membrane proteins in genomic sequences using statistical methods and machine learning algorithms. The statistical methods are based on amino acid composition, reside pair preference and motifs. In machine learning techniques, the combination of amino acid and dipeptide compositions has been used as main attributes. In addition, annotations have been made using the criterion based on the identification of beta-barrel membrane proteins and exclusion of globular and transmembrane helical proteins. A web interface has been developed for identifying the annotated beta-barrel membrane proteins in all known genomes. The users have the feasibility of selecting the genome from the three kingdoms of life, archaea, bacteria and eukaryote, and five different methods. Further, the statistics for all genomes have been provided along with the links to different algorithms and related databases. It is freely available at http://tmbeta-genome.cbrc.jp/annotation/.

Algorithms↗

PubMed2005

GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model.

We describe a novel system, GRIFFIN (G-protein and Receptor Interaction Feature Finding INstrument), that predicts G-protein coupled receptor (GPCR) and G-protein coupling selectivity based on a support vector machine (SVM) and a hidden Markov model (HMM) with high sensitivity and specificity. Based on our assumption that whole structural segments of ligands, GPCRs and G-proteins are essential to determine GPCR and G-protein coupling, various quantitative features were selected for ligands, GPCRs and G-protein complex structures, and those parameters that are the most effective in selecting G-protein type were used as feature vectors in the SVM. The main part of GRIFFIN includes a hierarchical SVM classifier using the feature vectors, which is useful for Class A GPCRs, the major family. For the opsins and olfactory subfamilies of Class A and other minor families (Classes B, C, frizzled and smoothened), the binding G-protein is predicted with high accuracy using the HMM. Applying this system to known GPCR sequences, each binding G-protein is predicted with high sensitivity and specificity (>85% on average). GRIFFIN (http://griffin.cbrc.jp/) is freely available and allows users to easily execute this reliable prediction of G-proteins.

Artificial Intelligence↗

PubMed2004

GENIUS II: a high-throughput database system for linking ORFs in complete genomes to known protein three-dimensional structures.

GENIUS II is an automated database system in which open reading frames (ORFs) in complete genomes are assigned to known protein three-dimensional (3D) structures. The system uses the multiple intermediate sequence search method in which query and target sequences are linked by intermediate sequences gathered by PSI-BLAST search. By applying the system to 129 complete genomes, 43.8% on average of the ORFs in the genomes were assigned to known 3D structures and the results are available for free at GENIUS II web site.

Algorithms↗