UNLABELLED: We previously reported the multi-modal Dynamic Cross Correlation (mDCC) method for analyzing molecular dynamics trajectories. This method quantifies the correlation coefficients of atomic motions with complex multi-modal behaviors by using a Bayesian-based pattern recognition technique that can effectively capture transiently formed, unstable interactions. Here, we present an open source toolkit for performing the mDCC analysis, including pattern recognitions, complex network analyses and visualizations. We include a tutorial document that thoroughly explains how to apply this toolkit for an analysis, using the example trajectory of the 100 ns simulation of an engineered endothelin-1 peptide dimer. AVAILABILITY AND IMPLEMENTATION: The source code is available for free at http://www.protein.osaka-u.ac.jp/rcsfp/pi/mdcctools/, implemented in C ++ and Python, and supported on Linux. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Omokage search is a service to search the global shape similarity of biological macromolecules and their assemblies, in both the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB). The server compares global shapes of assemblies independent of sequence order and number of subunits. As a search query, the user inputs a structure ID (PDB ID or EMDB ID) or uploads an atomic model or 3D density map to the server. The search is performed usually within 1 min, using one-dimensional profiles (incremental distance rank profiles) to characterize the shapes. Using the gmfit (Gaussian mixture model fitting) program, the found structures are fitted onto the query structure and their superimposed structures are displayed on the Web browser. Our service provides new structural perspectives to life science researchers. AVAILABILITY AND IMPLEMENTATION: Omokage search is freely accessible at http://pdbj.org/omokage/.

MOTIVATION: Kotai Antibody Builder is a Web service for tertiary structural modeling of antibody variable regions. It consists of three main steps: hybrid template selection by sequence alignment and canonical rules, 3D rendering of alignments and CDR-H3 loop modeling. For the last step, in addition to rule-based heuristics used to build the initial model, a refinement option is available that uses fragment assembly followed by knowledge-based scoring. Using targets from the Second Antibody Modeling Assessment, we demonstrate that Kotai Antibody Builder generates models with an overall accuracy equal to that of the best-performing semi-automated predictors using expert knowledge. AVAILABILITY AND IMPLEMENTATION: Kotai Antibody Builder is available at http://kotaiab.org CONTACT: [email protected].

MOTIVATION: Functional similarity between proteins is evident at both the sequence and structure levels. SeSAW is a web-based program for identifying functionally or evolutionarily conserved motifs in protein structures by locating sequence and structural similarities, and quantifying these at the level of individual residues. Results can be visualized in 2D, as annotated alignments, or in 3D, as structural superpositions. An example is given for both an experimentally determined query structure and a homology model. AVAILABILITY AND IMPLEMENTATION: The web server is located at http://www.pdbj.org/SeSAW/.

MOTIVATION: All eukaryotic proteomes are characterized by a significant percentage of proteins of unknown function. Comp-utational function prediction methods are therefore essential as initial steps in the function annotation process. This article describes an annotation method (PiRaNhA) for the prediction of RNA-binding residues (RBRs) from protein sequence information. A series of sequence properties (position specific scoring matrices, interface propensities, predicted accessibility and hydrophobicity) are used to train a support vector machine. This method is then evaluated for its potential to be applied to RNA-binding function prediction at the level of the complete protein. RESULTS: The 5-fold cross-validation of PiRaNhA on a dataset of 81 RNA-binding proteins achieves a Matthews Correlation Coefficient (MCC) of 0.50 and accuracy of 87.2%. When used to predict RBRs in 42 proteins not used in training, PiRaNhA achieves an MCC of 0.41 and accuracy of 84.5%. Decision values from the PiRaNhA predictions were used in a second SVM to make predictions of RNA-binding function at the protein level, achieving an MCC of 0.53 and accuracy of 76.1%. The PiRaNhA RBR predictions allow experimentalists to perform more targeted experiments for function annotation; and the prediction of RNA-binding function at the protein level shows promise for proteome-wide annotations. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at www.bioinformatics.sussex.ac.uk/PIRANHA or http://piranha.protein.osaka-u.ac.jp. SUPPLEMENTARY INFORMATION: Supplementary data are available at the Bioinformatics online.

The Protein Data Bank Japan (PDBj) curates, edits and distributes protein structural data as a member of the worldwide Protein Data Bank (wwPDB) and currently processes approximately 25-30% of all deposited data in the world. Structural information is enhanced by the addition of biological and biochemical functional data as well as experimental details extracted from the literature and other databases. Several applications have been developed at PDBj for structural biology and biomedical studies: (i) a Java-based molecular graphics viewer, jV; (ii) display of electron density maps for the evaluation of structure quality; (iii) an extensive database of molecular surfaces for functional sites, eF-site, as well as a search service for similar molecular surfaces, eF-seek; (iv) identification of sequence and structural neighbors; (v) a graphical user interface to all known protein folds with links to the above applications, Protein Globe. Recent examples are shown that highlight the utility of these tools in recognizing remote homologies between pairs of protein structures and in assigning putative biochemical functions to newly determined targets from structural genomics projects.

Parkinson's disease is a brain disease whose medical treatment and remedy have not been established yet, although a cause of this disease is considered to be the insufficient formation and action of dopamine, which plays a role of great importance in brain activity. For this reason, there is an increasing demand on the clinical database pertaining to Parkinson's disease which multiple hospitals and research institutions can share to accumulate knowledge and know-how on the disease. In this paper, we present a clinical database for Parkinson's disease research and diagnosis which we have preliminary built, especially focusing on the access control and data filtering mechanisms built in hope that medical Grid services are seamlessly integrated to the database in future.

BACKGROUND: Structure alignment methods offer the possibility of measuring distant evolutionary relationships between proteins that are not visible by sequence-based analysis. However, the question of how structural differences and similarities ought to be quantified in this regard remains open. In this study we construct a training set of sequence-unique CATH and SCOP domains, from which we develop a scoring function that can reliably identify domains with the same CATH topology and SCOP fold classification. The score is implemented in the ASH structure alignment package, for which the source code and a web service are freely available from the PDBj website http://www.pdbj.org/ASH/. RESULTS: The new ASH score shows increased selectivity and sensitivity compared with values reported for several popular programs using the same test set of 4,298,905 structure pairs, yielding an area of .96 under the receiver operating characteristic (ROC) curve. In addition, weak sequence homologies between similar domains are revealed that could not be detected by BLAST sequence alignment. Also, a subset of domain pairs is identified that exhibit high similarity, even though their CATH and SCOP classification differs. Finally, we show that the ranking of alignment programs based solely on geometric measures depends on the choice of the quality measure. CONCLUSION: ASH shows high selectivity and sensitivity with regard to domain classification, an important step in defining distantly related protein sequence families. Moreover, the CPU cost per alignment is competitive with the fastest programs, making ASH a practical option for large-scale structure classification studies.

Recent advances in grid infrastructure and middleware development have enabled various types of applications in science and engineering to be deployed on the grid. The characteristics of these applications and the diverse infrastructure and middleware solutions developed, utilized or adapted by PRAGMA member institutes are summarized. The applications include those for climate modeling, computational chemistry, bioinformatics and computational genomics, remote control of instruments, and distributed databases. Many of the applications are deployed to the PRAGMA grid testbed in routine basis experiments. Strategies for deploying applications without modifications, and those taking advantage of new programming models on the grid are explored and valuable lessons learned are reported. Comprehensive end to end solutions from PRAGMA member institutes that provide important grid middleware components and generalized models of integrating applications and instruments on the grid are also described.

UNLABELLED: PreDs is a WWW server that predicts the dsDNA-binding sites on protein molecular surfaces generated from the atomic coordinates in a PDB format. The prediction was done by evaluating the electrostatic potential, the local curvature and the global curvature on the surfaces. Results of the prediction can be interactively checked with our original surface viewer. AVAILABILITY: PreDs is available free of charge from http://pre-s.protein.osaka-u.ac.jp/~preds/ CONTACT: [email protected].

Abstract Summary: The Protein Data Bank (PDB) has recently released versions of the PDB Exchange dictionary and the PDB archival data files in XML format collectively named PDBML. The automated generation of these XML files is driven by the data dictionary infrastructure in use at the PDB. The correspondences between the PDB dictionary and the XML schema metadata are described as well as the XML representations of PDB dictionaries and data files. Availability: The current software translated XML schema file is located at http://deposit.pdb.org/pdbML/pdbx-v1.000.xsd, and on the PDB mmCIF resource page at http://deposit.pdb.org/mmcif/. PDBML files are stored on the PDB beta ftp site at ftp://beta.rcsb.org/pub/pdb/uniformity/data/XML Contact: [email protected]

BACKGROUND: Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies. RESULTS: In this study, we use a combination of 3 genomic features -- structurally known interacting Pfam domains, Gene Ontology annotations and sequence homology -- as a means to assign reliability to the protein-protein interactions in Saccharomyces cerevisiae determined by high-throughput experiments. Using Bayesian network approaches, we show that protein-protein interactions from high-throughput data supported by one or more genomic features have a higher likelihood ratio and hence are more likely to be real interactions. Our method has a high sensitivity (90%) and good specificity (63%). We show that 56% of the interactions from high-throughput experiments in Saccharomyces cerevisiae have high reliability. We use the method to estimate the number of true interactions in the high-throughput protein-protein interaction data sets in Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens to be 27%, 18% and 68% respectively. Our results are available for searching and downloading at http://helix.protein.osaka-u.ac.jp/htp/. CONCLUSION: A combination of genomic features that include sequence, structure and annotation information is a good predictor of true interactions in large and noisy high-throughput data sets. The method has a very high sensitivity and good specificity and can be used to assign a likelihood ratio, corresponding to the reliability, to each interaction.

BACKGROUND: We introduce GASH, a new, publicly accessible program for structural alignment and superposition. Alignments are scored by the Number of Equivalent Residues (NER), a quantitative measure of structural similarity that can be applied to any structural alignment method. Multiple alignments are optimized by conjugate gradient maximization of the NER score within the genetic algorithm framework. Initial alignments are generated by the program Local ASH, and can be supplemented by alignments from any other program. RESULTS: We compare GASH to DaliLite, CE, and to our earlier program Global ASH on a difficult test set consisting of 3,102 structure pairs, as well as a smaller set derived from the Fischer-Eisenberg set. The extent of alignment crossover, as well as the completeness of the initial set of alignments are examined. The quality of the superpositions is evaluated both by NER and by the number of aligned residues under three different RMSD cutoffs (2,4, and 6A). In addition to the numerical assessment, the alignments for several biologically related structural pairs are discussed in detail. CONCLUSION: Regardless of which criteria is used to judge the superposition accuracy, GASH achieves the best overall performance, followed by DaliLite, Global ASH, and CE. In terms of CPU usage, DaliLite CE and GASH perform similarly for query proteins under 500 residues, but for larger proteins DaliLite is faster than GASH or CE. Both an http interface and a simple object application protocol (SOAP) interface to the GASH program are available at http://www.pdbj.org/GASH/.

UNLABELLED: The electrostatic-surface of functional site (eF-site) is a database for the molecular surfaces of protein functional sites. To enable browsing of each molecular surface along with the atomic model, we have developed a new three-dimensional interactive viewer, PDBjViewer, that can be used both as an applet and as a stand-alone program. AVAILABILITY: The eF-site database and PDBjViewer are freely available from http://www.pdbj.org/eF-site/