Biomedical subjects

Makiko Suwa

Publications and source records attributed to Makiko Suwa.

At least 19 recordsLinked to original sources

PubMed2006

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.

Animals↗

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↗

PubMed2006

Influence of amino acid properties for discriminating outer membrane proteins at better accuracy.

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying outer membrane proteins from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this work, we have analyzed the influence of physico-chemical, energetic and conformational properties of amino acid residues for discriminating outer membrane proteins using different machine learning algorithms, such as, Bayes rules, Logistic functions, Neural networks, Support vector machines, Decision trees, etc. We observed that most of the properties have discriminated the OMPs with similar accuracy. The neural network method with the property, free energy change could discriminate the OMPs from other folding types of globular and membrane proteins at the 5-fold cross-validation accuracy of 94.4% in a dataset of 1,088 proteins, which is better than that obtained with amino acid composition. The accuracy of discriminating globular proteins is 94.3% and that of transmembrane helical (TMH) proteins is 91.8%. Further, the neural network method is tested with globular proteins belonging to 30 major folding types and it could successfully exclude 99.4% of the considered 1612 non-redundant proteins. These accuracy levels are comparable to or better than other methods in the literature. We suggest that this method could be effectively used to discriminate OMPs and for detecting OMPs in genomic sequences.

Algorithms↗

PubMed2006

Discrimination of outer membrane proteins using machine learning algorithms.

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this work, we have analyzed the performance of different methods, based on Bayes rules, logistic functions, neural networks, support vector machines, decision trees, etc. for discriminating OMPs. We found that most of the machine learning techniques discriminate OMPs with similar accuracy. The neural network-based method could discriminate the OMPs from other proteins [globular/transmembrane helical (TMH)] at the fivefold cross-validation accuracy of 91.0% in a dataset of 1,088 proteins. The accuracy of discriminating globular proteins is 88.8% and that of TMH proteins is 93.7%. Further, the neural network method is tested with globular proteins belonging to 30 different folding types and it could successfully exclude 95% of the considered proteins. The proteins with SAM domain such as knottins, rubredoxin, and thioredoxin folds are eliminated with 100% accuracy. These accuracy levels are comparable to or better than other methods in the literature. We suggest that this method could be effectively used to discriminate OMPs and for detecting OMPs in genomic sequences.

Algorithms↗

PubMed2006

Statistical analysis and prediction of functional residues effective for GPCR-G-protein coupling selectivity.

One of the important issues in G-protein-coupled receptor (GPCR) functional analysis is the mechanism of GPCR-G-protein coupling selectivity. G-proteins are classified into Gi/o, Gq/11 and Gs families. Although several experimental and computational analyses have been attempted, the mechanism remains unknown to this day. In this study, we have analyzed the multiple sequence alignments of GPCRs of known coupling selectivities by mapping onto the tertiary structure of rhodopsin. We identified several functional residue sites in GPCRs related to coupling selectivity, which are located mainly at the intracellular loops, and found that the occurrence of positively/negatively charged amino acids of the characteristic residues varies depending on the G-protein coupling selectivity. Especially, the occurrence of positively charged amino acids in receptors coupling to Gs family is less than that in receptors coupling to Gi/o and Gq/11 families. It is interesting that some characteristic residues are located near the extracellular terminus of transmembrane helices, which is far from the GPCR/G-protein binding interface. In most of the receptors coupling to Gs family, the occurrence of proline on the position corresponding to the 170th residue on rhodopsin is rare. These findings are vital to improving our understanding of the mechanism of G-protein coupling selectivity.

Amino Acid Sequence↗

PubMed2006

Automated classification of alternative splicing and transcriptional initiation and construction of visual database of classified patterns.

MOTIVATION: Large-scale detection and classification of alternative splicing and transcriptional initiation (ASTI) is the first step towards detailed studies of the functional implication and mechanisms of these phenomena. RESULTS: We have developed an algorithm that classifies all observed units of ASTI into an extendable set of distinct types (e.g. cassette type) by converting a collection of alignments between a genomic DNA sequence and cDNA sequences into binary description. This description system can uniquely and compactly encode not only typical patterns but also any rare patterns that are usually collectively assigned to 'others.' More than 150 distinct ASTI types were found when this system was applied to genome-wide detection of ASTI units in human and five other eukaryotes. AVAILABILITY: The data detected by this system are available through ASTRA (http://alterna.cbrc.jp/), a database equipped with a Java-based browser that can interactively reorganize the order of displayed splicing patterns on demand.

Algorithms↗

PubMed2005

Species-specific variation of alternative splicing and transcriptional initiation in six eukaryotes.

The genome-wide detection of alternative splicing and transcriptional initiation (ASTI) was conducted in six eukaryotes (human, mouse, fruit fly, nematode, cress and rice) whose genome sequencing has been completed or nearly completed. Transcriptional isoforms were collected by mapping a batch of full-length cDNA sequences onto the respective cognate genomic sequences. Isoforms mapped on the same gene locus were compared pair-wise, ASTI patterns were segmented into minimal spans, and then the minimal patterns (ASTI units) were classified into unique types, such as the cassette type or the alternative donor site. All these procedures were performed automatically under the same conditions so that the results obtained from different species could be compared directly. The fraction of loci that underwent ASTI of the total mapped loci was the largest for mammals and fruit fly, and the smallest for plants. Exactly the same trend was observed for the number of unique ASTI types found in each species. The observed fractional representations of the ASTI types were similar between evolutionarily close species, such as human and mouse or cress and rice. On the other hand, the relative orders of abundance in individual ASTI type were considerably different between evolutionarily distant species, such as between mammals and plants. In human and mouse, alternative splicing other than the retained introns tended to occur within the protein coding sequence (CDS) regions rather than within the untranslated regions (UTRs), whereas this tendency was obscure in the other four species. In all the species examined, the difference in alternative exon lengths was most likely in multiples of three, and this tendency was most prominent when the alternative exons were embedded within the CDSs. These observations are generally consistent with the idea that higher organisms utilize the ASTI mechanisms more extensively and in a more complicated manner than lower organisms, and that ASTI actively participates in the enhancement of the functional and structural diversity of products generated from a limited number of genes on a genome.

3' Untranslated Regions↗

PubMed2005

Discrimination of outer membrane proteins using support vector machines.

MOTIVATION: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for dissecting outer membrane proteins (OMPs) from genomic sequences and for the successful prediction of their secondary and tertiary structures. RESULTS: We have developed a method based on support vector machines using amino acid composition and residue pair information. Our approach with amino acid composition has correctly predicted the OMPs with a cross-validated accuracy of 94% in a set of 208 proteins. Further, this method has successfully excluded 633 of 673 globular proteins and 191 of 206 alpha-helical membrane proteins. We obtained an overall accuracy of 92% for correctly picking up the OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins. Furthermore, residue pair information improved the accuracy from 92 to 94%. This accuracy of discriminating OMPs is higher than that of other methods in the literature, which could be used for dissecting OMPs from genomic sequences. AVAILABILITY: Discrimination results are available at http://tmbeta-svm.cbrc.jp.

Algorithms↗

PubMed2005

Automatic gene collection system for genome-scale overview of G-protein coupled receptors in eukaryotes.

We have developed an automatic system for identifying GPCR (G-protein coupled receptor) genes from various kinds of genomes, which is finally deposited in the SEVENS database (http://sevens.cbrc.jp/), by integrating such software as a gene finder, a sequence alignment tool, a motif and domain assignment tool, and a transmembrane helix predictor. SEVENS enables us to perform a genome-scale overview of the "GPCR universe" using sequences that are identified with high accuracy (99.4% sensitivity and 96.6% specificity). Using this system, we surveyed the complete genomes of 7 eukaryotes and 224 prokaryotes, and found that there are 4 to 1016 GPCR genes in the 7 eukaryotes, and only a total of 16 GPCR genes in all the prokaryotes. Our preliminary results indicate that 11 subfamilies of the Class A family, the Class 2(B) family, the Class 3(C) family and the fz/smo family are commonly found among human, fly, and nematode genomes. We also analyzed the chromosomal locations of the GPCR genes with the Kolmogorov-Smirnov test, and found that species-specific families, such as olfactory, taste, and chemokine receptors in human and nematode chemoreceptor in worm, tend to form clusters extensively, whereas no significant clusters were detected in fly and plant genomes.

Animals↗

PubMed2005

Pivotal function for cytoplasmic protein FROUNT in CCR2-mediated monocyte chemotaxis.

Ligation of the chemokine receptor CCR2 on monocytes and macrophages with its ligand CCL2 results in activation of the cascade consisting of phosphatidylinositol-3-OH kinase (PI(3)K), the small G protein Rac and lamellipodium protrusion. We show here that a unique clathrin heavy-chain repeat homology protein, FROUNT, directly bound activated CCR2 and formed clusters at the cell front during chemotaxis. Overexpression of FROUNT amplified the chemokine-elicited PI(3)K-Rac-lamellipodium protrusion cascade and subsequent chemotaxis. Blocking FROUNT function by using a truncated mutant or antisense strategy substantially diminished signaling via CCR2. In a mouse peritonitis model, suppression of endogenous FROUNT markedly prevented macrophage infiltration. Thus, FROUNT links activated CCR2 to the PI(3)K-Rac-lamellipodium protrusion cascade and could be a therapeutic target in chronic inflammatory immune diseases associated with macrophage infiltration.

Amino Acid Sequence↗

PubMed2005

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

We have developed a web-server, TMBETA-NET for discriminating outer membrane proteins and predicting their membrane spanning beta-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 beta-strand length. Our program takes the amino acid sequence as input and displays the type of the protein along with membrane-spanning beta-strand segments as a stretch of highlighted amino acid residues. Further, the probability of residues to be in transmembrane beta-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 beta-strand segments at an accuracy of 73% just from amino acid sequence information. The prediction server is available at http://psfs.cbrc.jp/tmbeta-net/.

Amino Acids↗

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↗

PubMed2005

Large-scale analysis of human alternative protein isoforms: pattern classification and correlation with subcellular localization signals.

We investigated human alternative protein isoforms of >2600 genes based on full-length cDNA clones and SwissProt. We classified the isoforms and examined their co-occurrence for each gene. Further, we investigated potential relationships between these changes and differential subcellular localization. The two most abundant patterns were the one with different C-terminal regions and the one with an internal insertion, which together account for 43% of the total. Although changes of the N-terminal region are less common than those of the C-terminal region, extension of the C-terminal region is much less common than that of the N-terminal region, probably because of the difficulty of removing stop codons in one isoform. We also found that there are some frequently used combinations of co-occurrence in alternative isoforms. We interpret this as evidence that there is some structural relationship which produces a repertoire of isoformal patterns. Finally, many terminal changes are predicted to cause differential subcellular localization, especially in targeting either peroxisomes or mitochondria. Our study sheds new light on the enrichment of the human proteome through alternative splicing and related events. Our database of alternative protein isoforms is available through the internet.

Alternative Splicing↗

PubMed2005

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 approximately 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.

Allosteric Regulation↗

PubMed2005

Structural analysis of residues involving cation-pi interactions in different folding types of membrane proteins.

Cation-pi interactions play an important role to the stability of protein structures. In our earlier work, we have analyzed the influence and energetic contribution of cation-pi interactions in three-dimensional structures of membrane proteins. In this work, we investigate the characteristic features of residues that are involved in cation-pi interactions. We have computed several parameters, such as surrounding hydrophobicity, number of long-range contacts, conservation score and normalized B-factor for all these residues and identified their location, whether in the membrane or at surface. We found that the cation-pi interactions are mainly formed by long-range interactions. The cationic residues involved in cation-pi interactions have higher surrounding hydrophobicity than their average values in the whole dataset and an opposite trend is observed for aromatic residues. In transmembrane helical proteins, except Phe, all other residues that are responsible for cation-pi interactions are highly conserved with other related protein sequences whereas in transmembrane strand proteins, an appreciable conservation is observed only for Arg. The analysis on the flexibility of residues reveals that the cation-pi interaction forming residues are more stable than other residues. The results obtained in the present study would be helpful to understand the role of cation-pi interactions in the structure and folding of membrane proteins.

Amino Acids↗

PubMed2005

Application of residue distribution along the sequence for discriminating outer membrane proteins.

Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important problem both for detecting outer membrane proteins from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this work, we have systematically analyzed the distribution of amino acid residues in the sequences of globular and outer membrane proteins. We observed that the occurrence of two neighboring aliphatic and polar residues is significantly higher in outer membrane proteins than in globular proteins. From the information about the dipeptide composition we have devised a statistical method for discriminating outer membrane proteins from other globular and membrane proteins. Our approach correctly picked up the outer membrane proteins with an accuracy of 95% for the training set of 337 proteins. On the other hand, our method has correctly excluded the globular proteins at an accuracy of 79% in a non-redundant dataset of 674 proteins. Furthermore, the present method is able to correctly exclude alpha-helical membrane proteins up to an accuracy of 87%. These accuracy levels are comparable to other methods in the literature. The influence of protein size and structural class for discrimination is discussed.

Amino Acid Sequence↗

PubMed2004

A simple statistical method for discriminating outer membrane proteins with better accuracy.

MOTIVATION: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for identifying outer membrane proteins from genomic sequences and for the successful prediction of their secondary and tertiary structures. RESULTS: We have systematically analyzed the amino acid composition of globular proteins from different structural classes and outer membrane proteins. We found that the residues, Glu, His, Ile, Cys, Gln, Asn and Ser, show a significant difference between globular and outer membrane proteins. Based on this information, we have devised a statistical method for discriminating outer membrane proteins from other globular and membrane proteins. Our approach correctly picked up the outer membrane proteins with an accuracy of 89% for the training set of 337 proteins. On the other hand, our method has correctly excluded the globular proteins at an accuracy of 79% in a non-redundant dataset of 674 proteins. Furthermore, the present method is able to correctly exclude alpha-helical membrane proteins up to an accuracy of 80%. These accuracy levels are comparable to other methods in the literature, and this is a simple method, which could be used for dissecting outer membrane proteins from genomic sequences. The influence of protein size, structural class and specific residues for discrimination is discussed.

Algorithms↗

PubMed2004

Neural network-based prediction of transmembrane beta-strand segments in outer membrane proteins.

Prediction of transmembrane beta-strands in outer membrane proteins (OMP) is one of the important problems in computational chemistry and biology. In this work, we propose a method based on neural networks for identifying the membrane-spanning beta-strands. We introduce the concept of "residue probability" for assigning residues in transmembrane beta-strand segments. The performance of our method is evaluated with single-residue accuracy, correlation, specificity, and sensitivity. Our predicted segments show a good agreement with experimental observations with an accuracy level of 73% solely from amino acid sequence information. Further, the predictive power of N- and C-terminal residues in each segments, number of segments in each protein, and the influence of cutoff probability for identifying membrane-spanning beta-strands will be discussed. We have developed a Web server for predicting the transmembrane beta-strands from the amino acid sequence, and the prediction results are available at http://psfs.cbrc.jp/tmbeta-net/.

Algorithms↗