TMBETA-NET: discrimination and prediction of membrane spanning β-strands in outer membrane proteins

We have developed a web-server, TMBETA-NET for discriminating outer membrane proteins and predicting their membrane spanning β-strand segments. The amino acid compositions of globular and outer membrane proteins have been systematically analyzed and a statistical method has been proposed for discriminating outer membrane proteins. The prediction of membrane spanning segments is mainly based on feed forward neural network and refined with β-strand length. Our program takes the amino acid sequence as input and displays the type of the protein along with membrane-spanning β-strand segments as a stretch of highlighted amino acid residues. Further, the probability of residues to be in transmembrane β-strand has been provided with a coloring scheme. We observed that outer membrane proteins were discriminated with an accuracy of 89% and their membrane spanning β-strand segments at an accuracy of 73% just from amino acid sequence information. The prediction server is available at

A Research on School Principals’ Recognition of Current Situations and Issues in Regard to the Promotion of ICT Use at Schools

　本稿の目的は，校長を対象とした調査研究を通じて，教育の情報化の現状と課題に関する校長の認識を把握し，教育の情報化の推進に寄与しうる基礎的知見を得ることである。そのために，具体的には以下の点について検討した。① ICT の利用に関する現任校や自身の現状に対する校長の認識，②学校全体としてICT の活用を進めていくための改善の必要性に対する校長の認識，③ ICT の利用に関する現状認識の差異による改善の必要性に関する認識の差異，④教育の情報化を進めたり，ICT を活用したりするなかで，当初予想していなかった成果・効果，⑤教育の情報化を進めたり，ICT を活用したりするなかで，当初予想していなかった課題・問題

Structural Basis for a Broad But Selective Ligand Spectrum of a Mouse Olfactory Receptor: Mapping the Odorant-Binding Site

The olfactory receptor (OR) superfamily provides a basis for the remarkable ability to recognize and discriminate a large number of odorants. In mice, the superfamily includes ∼1000 members, and they recognize overlapping sets of odorants with distinct affinities and specificities. To address the molecular basis of odor discrimination by the mammalian OR superfamily, we performed functional analysis on a series of site-directed mutants and performed ligand docking simulation studies to define the odorant-binding site of a mouse OR. Our results indicate that several amino acids in the transmembrane domains formed a ligand-binding pocket. Although other G-protein-coupled receptors (GPCRs) recognize biogenic ligands mainly with ionic or hydrogen bonding interactions, ORs recognize odorants mostly via hydrophobic and van der Waals interactions. This accounts for the broad but selective binding by ORs as well as their relatively low ligand-binding affinities. Furthermore, we succeeded in rational receptor design, inserting point mutations in the odorant-binding site that resulted in predicted changes in ligand specificity and antagonist activity. This ability to rationally design the receptor validated the binding site structure that was deduced with our mutational and ligand docking studies. Such broad and specific sensitivity suggests an evolutionary process during which mutations in the active site led to an enormous number of ORs with a wide range of ligand specificity. The current study reveals the molecular environment of the odorant-binding site, and it further advances the understanding of GPCR pharmacology

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 () is freely available and allows users to easily execute this reliable prediction of G-proteins

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

We have developed the database, TMBETA-GENOME, for annotated β-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 β-barrel membrane proteins and exclusion of globular and transmembrane helical proteins. A web interface has been developed for identifying the annotated β-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

Odorant Receptor Map in the Mouse Olfactory Bulb: In Vivo Sensitivity and Specificity of Receptor-Defined Glomeruli

Odorant identity is represented in the olfactory bulb (OB) by the glomerular activity pattern, which reflects a combination of activated odorant receptors (ORs) in the olfactory epithelium. To elucidate this neuronal circuit at the molecular level, we established a functional OR identification strategy based on glomerular activity by combining in vivo Ca^(2+) imaging, retrograde dye labeling, and single-cell RT-PCR. Spatial and functional mapping of OR-defined glomeruli revealed that the glomerular positional relationship varied considerably between individual animals, resulting in different OR maps in the OB. Notably, OR-defined glomeruli exhibited different ligand spectra and far higher sensitivity compared to the in vitro pharmacological properties of corresponding ORs. Moreover, we found that the olfactory mucus was an important factor in the regulation of in vivo odorant responsiveness. Our results provide a methodology to examine in vivo glomerular responses at the receptor level and further help address the long-standing issues of olfactory sensitivity and specificity under physiological conditions

Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones

The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology