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.

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.

Development of a machine learning method to predict membrane protein-ligand binding residues using basic sequence information.

Locating ligand binding sites and finding the functionally important residues from protein sequences as well as structures became one of the challenges in understanding their function. Hence a Naïve Bayes classifier has been trained to predict whether a given amino acid residue in membrane protein sequence is a ligand binding residue or not using only sequence based information. The input to the classifier consists of the features of the target residue and two sequence neighbors on each side of the target residue. The classifier is trained and evaluated on a nonredundant set of 42 sequences (chains with at least one transmembrane domain) from 31 alpha-helical membrane proteins. The classifier achieves an overall accuracy of 70.7% with 72.5% specificity and 61.1% sensitivity in identifying ligand binding residues from sequence. The classifier performs better when the sequence is encoded by psi-blast generated PSSM profiles. Assessment of the predictions in the context of three-dimensional structures of proteins reveals the effectiveness of this method in identifying ligand binding sites from sequence information. In 83.3% (35 out of 42) of the proteins, the classifier identifies the ligand binding sites by correctly recognizing more than half of the binding residues. This will be useful to protein engineers in exploiting potential residues for functional assessment.

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.

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.

Folding elastic transmembrane helices to fit in a low-resolution image by electron microscopy.

Structure prediction of membrane proteins could be constrained and thereby improved by introducing data of the observed molecular shape. We studied a coarse-grained molecular model that relied on residue-based dummy atoms to fold the transmembrane helices of a protein in the observed molecular shape. Based on the inter-residue potential, the α-helices were folded to contact each other in a simulated annealing protocol to search optimized conformation. Fitting the model into a three-dimensional volume was tested for proteins with known structures and resulted in a fairly reasonable arrangement of helices. In addition, the constraint to the packing transmembrane helix with the two-dimensional region was tested and found to work as a very similar folding guide. The obtained models nicely represented α-helices with the desired slight bend. Our structure prediction method for membrane proteins well demonstrated reasonable folding results using a low-resolution structural constraint introduced from recent cell-surface imaging techniques.

Structural elements of the signal propagation pathway in squid rhodopsin and bovine rhodopsin.

Squid and bovine rhodopsins are G-protein coupled receptors (GPCRs) that activate Gq- and Gt-type G-proteins, respectively. To understand the structural elements of the signal propagation pathway, we performed molecular dynamics (MD) simulations of squid and bovine rhodopsins plus a detailed sequence analysis of class A GPCRs. The computations indicate that although the geometry of the retinal is similar in bovine and squid rhodopsins, the important interhelical hydrogen bond networks are different. In squid rhodopsin, an extended hydrogen bond network that spans ∼13 Što Tyr315 on the cytoplasmic site is present regardless of the protonation state of Asp80. In contrast, the extended hydrogen bond network is interrupted at Tyr306 in bovine rhodopsin. Those differences in the hydrogen bond network may play significant functional roles in the signal propagation from the retinal binding site to the cytoplasmic site, including transmembrane helix (TM) 6 to which the G-protein binds. The MD calculations demonstrate that the elongated conformation of TM6 in squid rhodopsin is stabilized by salt bridges formed with helix (H) 9. Together with the interhelical hydrogen bonds, the salt bridges between TM6 and H9 stabilize the protein conformation of squid rhodopsin and may hinder the occurrence of large conformational changes that are observed upon activation of bovine rhodopsin.

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.

Computational overview of GPCR gene universe to support reverse chemical genomics study.

In order to support high-throughput screening for ligands of G-protein coupled receptors (GPCRs) by using bioinformatics technology, we introduce a database (SEVENS) with genome-scale annotation and software (GRIFFIN) that can simulate GPCR function. SEVENS ( http://sevens.cbrc.jp/ ) is an integrated database that includes GPCR genes that are identified with high accuracy (99.4% sensitivity and 96.6% specificity) from various types of genomes, by a pipeline that integrates such software as a gene finder, a sequence alignment tool, a motif and domain assignment tool, and a transmembrane helix (TMH) predictor. SEVENS provides the user a genome-scale overview of the "GPCR universe" with detailed information of chromosomal mapping, phylogenetic tree, protein sequence and structure, and experimental evidence, all of which are accessible via a user-friendly interface. GRIFFIN ( http://griffin.cbrc.jp/ ) can predict GPCR and G-protein coupling selectivity induced by ligand binding with high sensitivity and specificity (more than 87% on average), based on the support vector machine (SVM) and hidden Markov Model (HMM). SEVENS and GRIFFIN are expected to contribute to revealing the function of orphan and unknown GPCRs.

Highly selective tuning of a silkworm olfactory receptor to a key mulberry leaf volatile.

BACKGROUND: The olfactory system plays an important role in the recognition of leaf volatiles during the search of folivore insects for a suitable plant host. For example, volatiles emitted by mulberry leaves trigger chemotaxis behavior in the silkworms Bombyx mori, and as a consequence, they preferentially reside on and consume mulberry leaves. Here, we aimed to identify natural chemoattractants and their corresponding olfactory receptors (Ors) involved in silkworm behavior to mulberry leaves. RESULTS: Chemotaxis behavioral assays for headspace volatiles detected by gas chromatography-mass spectroscopy analysis revealed that among the volatiles that were emitted by mulberry leaves, cis-jasmone was the most potent attractant for silkworms, working at a threshold of 30 pg from [corrected] 20 cm distance. Among a total of 66 Ors identified in the B. mori genome, we found that 23 were expressed in the olfactory organs during larval stages. Functional analysis of all the larvae-expressed Ors in Xenopus oocytes revealed that one Or, termed BmOr-56, showed a high sensitivity to cis-jasmone. In addition, the ligand-receptor activity of BmOr-56 reflected the chemotaxis behavioral response of silkworms. CONCLUSIONS: We identified cis-jasmone as a potent attractant in mulberry leaves for silkworms and provide evidence that a highly tuned receptor, BmOr-56, may mediate this behavioral attraction. The current study sheds light on the mechanism of the correlation between olfactory perception in folivore insects and chemotaxis behavior to a natural volatile emitted by green leaves.

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/.

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.

TMB finding pipeline: novel approach for detecting beta-barrel membrane proteins in genomic sequences.

We have developed a novel approach for dissecting transmembrane beta-barrel proteins (TMBs) in genomic sequences. The features include (i) the identification of TMBs using the preference of residue pairs in globular, transmembrane helical (TMH) and TMBs, (ii) elimination of globular/TMH proteins that show sequence identity of more than 70% for the coverage of 80% residues with known structures, (iii) elimination of globular/TMH proteins that have sequence identity of more than 60% with known sequences in SWISS-PROT, and (iv) exclusion of TMH proteins using SOSUI, a prediction system for TMH proteins. Our approach picked up 7% TMBs in all the considered genomes. The comparison between the identified TMBs in E. coli genome and available experimental data demonstrated that the new approach could correctly identify all the 11 known TMBs, whose crystal structures are available. Further, it revealed the presence of 19 TMBs, homology with known structures, 60 TMBs similar to well annotated sequences, and 54 TMBs that have high sequence similarity with Escherichia coli beta-barrel proteins deposited in Transport Classification Database (TCDB). Interestingly, the present approach identified TMBs from all 15 families in TCDB. In human genome, the occurrence of TMBs varies from 0 to 3% in different chromosomes. We suggest that our approach could lead to a step forward in the advancement of structural and functional genomics.

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/.

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.

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.

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.