Ca2+ Dependent Formation/Collapse of Cylindrical Ca2+-ATPase Crystals in Scallop Sarcoplasmic Reticulum (SR) Vesicles: A Possible Dynamic Role of SR in Regulation of Muscle Contraction.

[Ca2+]-dependent crystallization of the Ca2+-ATPase molecules in sarcoplasmic reticulum (SR) vesicles isolated from scallop striated muscle elongated the vesicles in the absence of ATP, and ATP stabilized the crystals. Here, to determine the [Ca2+]-dependence of vesicle elongation in the presence of ATP, SR vesicles in various [Ca2+] environments were imaged using negative stain electron microscopy. The images obtained revealed the following phenomena. (i) Crystal-containing elongated vesicles appeared at ≤1.4 µM Ca2+ and almost disappeared at ≥18 µM Ca2+, where ATPase activity reaches its maximum. (ii) At ≥18 µM Ca2+, almost all SR vesicles were in the round form and covered by tightly clustered ATPase crystal patches. (iii) Round vesicles dried on electron microscopy grids occasionally had cracks, probably because surface tension crushed the solid three-dimensional spheres. (iv) [Ca2+]-dependent ATPase crystallization was rapid (<1 min) and reversible. These data prompt the hypothesis that SR vesicles autonomously elongate or contract with the help of a calcium-sensitive ATPase network/endoskeleton and that ATPase crystallization may modulate physical properties of the SR architecture, including the ryanodine receptors that control muscle contraction.

Elongation and Contraction of Scallop Sarcoplasmic Reticulum (SR): ATP Stabilizes Ca2+-ATPase Crystalline Array Elongation of SR Vesicles.

The Ca2+-ATPase is an integral transmembrane Ca2+ pump of the sarcoplasmic reticulum (SR). Crystallization of the cytoplasmic surface ATPase molecules of isolated scallop SR vesicles was studied at various calcium concentrations by negative stain electron microscopy. In the absence of ATP, round SR vesicles displaying an assembly of small crystalline patches of ATPase molecules were observed at 18 µM [Ca2+]. These partly transformed into tightly elongated vesicles containing ATPase crystalline arrays at low [Ca2+] (≤1.3 µM). The arrays were classified as ''tetramer'', "two-rail" (like a railroad) and ''monomer''. Their crystallinity was low, and they were unstable. In the presence of ATP (5 mM) at a low [Ca2+] of ~0.002 µM, "two-rail" arrays of high crystallinity appeared more frequently in the tightly elongated vesicles and the distinct tetramer arrays disappeared. During prolonged (~2.5 h) incubation, ATP was consumed and tetramer arrays reappeared. A specific ATPase inhibitor, thapsigargin, prevented both crystal formation and vesicle elongation in the presence of ATP. Together with the second part of this study, these data suggest that the ATPase forms tetramer units and longer tetramer crystalline arrays to elongate SR vesicles, and that the arrays transform into more stable "two-rail" forms in the presence of ATP at low [Ca2+].

Ca2+-ATPase Molecules as a Calcium-Sensitive Membrane-Endoskeleton of Sarcoplasmic Reticulum.

The Ca2+-transport ATPase of sarcoplasmic reticulum (SR) is an integral, transmembrane protein. It sequesters cytoplasmic calcium ions released from SR during muscle contraction, and causes muscle relaxation. Based on negative staining and transmission electron microscopy of SR vesicles isolated from rabbit skeletal muscle, we propose that the ATPase molecules might also be a calcium-sensitive membrane-endoskeleton. Under conditions when the ATPase molecules scarcely transport Ca2+, i.e., in the presence of ATP and ≤ 0.9 nM Ca2+, some of the ATPase particles on the SR vesicle surface gathered to form tetramers. The tetramers crystallized into a cylindrical helical array in some vesicles and probably resulted in the elongated protrusion that extended from some round SRs. As the Ca2+ concentration increased to 0.2 µM, i.e., under conditions when the transporter molecules fully carry out their activities, the ATPase crystal arrays disappeared, but the SR protrusions remained. In the absence of ATP, almost all of the SR vesicles were round and no crystal arrays were evident, independent of the calcium concentration. This suggests that ATP induced crystallization at low Ca2+ concentrations. From the observed morphological changes, the role of the proposed ATPase membrane-endoskeleton is discussed in the context of calcium regulation during muscle contraction.

Identification of a binding element for the cytoplasmic regulator FROUNT in the membrane-proximal C-terminal region of chemokine receptors CCR2 and CCR5.

Chemokine receptors mediate the migration of leucocytes during inflammation. The cytoplasmic protein FROUNT binds to chemokine receptors CCR2 [chemokine (C-C motif) receptor 2] and CCR5, and amplifies chemotactic signals in leucocytes. Although the interaction between FROUNT and chemokine receptors is important for accurate chemotaxis, the interaction mechanism has not been elucidated. In the present study we identified a 16-amino-acid sequence responsible for high-affinity binding of FROUNT at the membrane-proximal C-terminal intracellular region of CCR2 (CCR2 Pro-C) by yeast two-hybrid analysis. Synthesized peptides corresponding to the CCR2 Pro-C sequence directly interacted with FROUNT in vitro. CCR2 Pro-C was predicted to form an amphipathic helix structure. Residues on the hydrophobic side are completely conserved among FROUNT-binding receptors, suggesting that the hydrophobic side is the responsible element for FROUNT binding. The L316T mutation to the hydrophobic side of the predicted helix decreased the affinity for FROUNT. Co-immunoprecipitation assays revealed that the CCR2 L316T mutation diminished the interaction between FROUNT and full-length CCR2 in cells. Furthermore, this mutation impaired the ability of the receptor to mediate chemotaxis. These findings provide the first description of the functional binding element in helix 8 of CCR2 for the cytosolic regulator FROUNT that mediates chemotactic signalling.

Dynamics of bovine opsin bound to G-protein fragments.

G protein-coupled receptors (GPCRs) are a large class of membrane proteins that mediate communication of the cell with the outer environment. Upon activation by an agonist, GPCRs undergo large-scale conformational changes that enable binding of the G protein to the receptor. A key open question concerns the mechanism of the long-distance coupling between the agonist-binding site and the cytoplasmic site where G protein binds. Here we address this question by exploring the molecular dynamics of bovine opsin bound to three different fragments of G-proteins. We find that an extended network of hydrogen bonds connects the agonist retinal binding site to the G protein binding site via conserved amino acid residues. The dynamics of the hydrogen-bonding network inside opsin couples to interactions at the G protein binding site.

Bioinformatics tools for predicting GPCR gene functions.

The automatic classification of GPCRs by bioinformatics methodology can provide functional information for new GPCRs in the whole 'GPCR proteome' and this information is important for the development of novel drugs. Since GPCR proteome is classified hierarchically, general ways for GPCR function prediction are based on hierarchical classification. Various computational tools have been developed to predict GPCR functions; those tools use not simple sequence searches but more powerful methods, such as alignment-free methods, statistical model methods, and machine learning methods used in protein sequence analysis, based on learning datasets. The first stage of hierarchical function prediction involves the discrimination of GPCRs from non-GPCRs and the second stage involves the classification of the predicted GPCR candidates into family, subfamily, and sub-subfamily levels. Then, further classification is performed according to their protein-protein interaction type: binding G-protein type, oligomerized partner type, etc. Those methods have achieved predictive accuracies of around 90 %. Finally, I described the future subject of research of the bioinformatics technique about functional prediction of GPCR.

Discrimination of Golgi type II membrane proteins based on their hydropathy profiles and the amino acid propensities of their transmembrane regions.

Membrane proteins in the Golgi apparatus play important roles in biological functions, predominantly as catalysts related to post-translational modification of protein oligosaccharides. We succeeded in extracting the characteristics of Golgi type II membrane proteins computationally by comparison with those of Golgi no retention proteins, which are mainly localized in the plasma membrane. Golgi type II membrane proteins were detected by combining hydropathy alignment and a position-specific score matrix of the amino acid propensities around the transmembrane region. We achieved 96.2% sensitivity, 93.5% specificity, and a 0.949 success rate in a self-consistency test. In a 5-fold cross-validation test, 88.0% sensitivity, 85.5% specificity, and a 0.867 success rate were achieved.

TMFunction: database for functional residues in membrane proteins.

We have developed the database TMFunction, which is a collection of more than 2900 experimentally observed functional residues in membrane proteins. Each entry includes the numerical values for the parameters IC50 (measure of the effectiveness of a compound in inhibiting biological function), V(max) (maximal velocity of transport), relative activity of mutants with respect to wild-type protein, binding affinity, dissociation constant, etc., which are important for understanding the sequence-structure-function relationship of membrane proteins. In addition, we have provided information about name and source of the protein, Uniprot and Protein Data Bank codes, mutational and literature information. Furthermore, TMFunction is linked to related databases and other resources. We have set up a web interface with different search and display options so that users have the ability to get the data in several ways. TMFunction is freely available at http://tmbeta-genome.cbrc.jp/TMFunction/.

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.

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/.

TMBETADISC-RBF: Discrimination of beta-barrel membrane proteins using RBF networks and PSSM profiles.

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. We have developed a method based on radial basis function networks and position specific scoring matrix (PSSM) profiles generated by PSI-BLAST and non-redundant protein database. Our approach with PSSM profiles has correctly predicted the OMPs with a cross-validated accuracy of 96.4% in a set of 1251 proteins, which contain 206 OMPs, 667 globular proteins and 378 alpha-helical inner membrane proteins. Furthermore, we applied our method on a dataset containing 114 OMPs, 187 TMH proteins and 195 globular proteins obtained with less than 20% sequence identity and obtained the cross-validated accuracy of 95%. This accuracy of discriminating OMPs is higher than other methods in the literature and our method could be used as an effective tool for dissecting OMPs from genomic sequences. We have developed a prediction server, TMBETADISC-RBF, which is available at http://rbf.bioinfo.tw/~sachen/OMP.html.

SEVENS: a database for comprehensive GPCR genes obtained from genomes: -Update to 68 eukaryotes.

We report the development of the SEVENS database, which contains information on G-protein coupled receptor (GPCR) genes that are identified with high confidence levels (A, B, C, and D) from various eukaryotic genomes, by using a pipeline comprising bioinformatics softwares, including a gene finder, a sequence alignment tool, a motif and domain assignment tool, and a transmembrane helix predictor. SEVENS compiles detailed information on GPCR genes, such as chromosomal mapping position, phylogenetic tree, sequence similarity to known genes, and protein function described by motif/domain and transmembrane helices. They are presented in a user-friendly interface. Because of the comprehensive gene findings from genomes, SEVENS contains a larger data set than that of previous databases and enables the performance of a genome-scale overview of all the GPCR genes. We surveyed the complete genomes of 68 eukaryotes, and found that there were between 6 and 3,470 GPCR genes for each genome (Level A data). Within these genes, the number of receptors for various molecules, including biological amines, peptides, and lipids, were conserved in mammals, birds, and fishes, whereas the numbers of odorant receptors and pheromone receptors were highly diverse in mammals. SEVENS is freely available at http://sevens.cbrc.jp or http://sevens.chem.aoyama.ac.jp.

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.

Current developments on beta-barrel membrane proteins: sequence and structure analysis, discrimination and prediction.

beta-barrel membrane proteins perform a variety of functions, such as mediating non-specific, passive transport of ions and small molecules, selectively passing the molecules like maltose and sucrose and are involved in voltage dependent anion channels. Understanding the structural features of beta-barrel membrane proteins and detecting them in genomic sequences are challenging tasks in structural and functional genomics. In this review, with the survey of experimentally known amino acid sequences and structures, the characteristic features of amino acid residues in beta-barrel membrane proteins and novel parameters for understanding their folding and stability will be described. The development of statistical methods and machine learning techniques for discriminating beta-barrel membrane proteins from other folding types of globular and membrane proteins will be explained along with their relative importance. Further, different methods including hydrophobicity profiles, rule based approach, amino acid properties, neural networks, hidden Markov models etc. for predicting membrane spanning segments of beta-barrel membrane proteins will be discussed. In addition, the applications of discrimination techniques for detecting beta-barrel membrane proteins in genomic sequences will be outlined. In essence, this comprehensive review would provide an overall picture about beta-barrel membrane proteins starting from the construction of datasets to genome-wide applications.

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/.

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.

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.

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.

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.

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.