Self-adapted clustering of solute atoms into a confined two-dimensional prismatic platelet with an ellipse-like quasi-unit cell.

This paper reports a new structured prismatic platelet, self-assembled by an ellipse-like quasi-unit cell, precipitated in Mg-In-Yb and Mg-In-Ca ternary alloys and aged isothermally at 200°C using aberration-corrected high-angle annular dark-field scanning transmission electron microscopy combined with density functional theory computations. The ordered stacking of solute atoms along the [0001]α direction based on elliptically shaped self-adapted clustering leads to the generation of the quasi-unit cell. The bonding of these ellipse-like quasi-unit-cell rods by the Mg atomic columns along the 〈〉α directions formed a two-dimensional planar structure, which has three variants with a {}α habit plane and full coherence with the α-Mg matrix. This finding is important for understanding the clustering and stacking behaviors of solute atoms in condensed matter, and is expected to guide the future design of novel high-strength Mg alloys strengthened by such high-density prismatic platelets.

Self-Assembly of Two Unit Cells into a Nanodomain Structure Containing Five-Fold Symmetry.

Five-fold symmetry was forbidden for the periodic crystals until the discovery of the Al-Mn icosahedral quasicrystal. We report a kind of precipitated rod-shaped nanophase containing five-fold symmetry but not belonging to any crystals or quasicrystals discovered so far. These metastable nanodomain phases, which precipitated in Mg-6Zn alloy during isothermal aging at 200 °C, contain two separate unit cells in the 2D plane perpendicular to the five-fold axis but with periodic atom arrangement along the five-fold axis, that is, 72° rhombus structure and 72° equilateral hexagon structure. The self-assembly of two unit cells under some geometrical constraints into a nanodomain contains the 2D five-fold, C14, and C15 structures. This finding confirms the existence of solid matters in a special structure between the crystals and quasicrystals, and it is expected to provide a way to understand the atomic arrangement and stacking behavior in condensed matters.

GeneclusterViz: a tool for conserved gene cluster visualization, exploration and analysis.

MOTIVATION: Gene clusters are arrangements of functionally related genes on a chromosome. In bacteria, it is expected that evolutionary pressures would conserve these arrangements due to the functional advantages they provide. Visualization of conserved gene clusters across multiple genomes provides key insights into their evolutionary histories. Therefore, a software tool that enables visualization and functional analyses of gene clusters would be a great asset to the biological research community. RESULTS: We have developed GeneclusterViz, a Java-based tool that allows for the visualization, exploration and downstream analyses of conserved gene clusters across multiple genomes. GeneclusterViz combines an easy-to-use exploration interface for gene clusters with a host of other analysis features such as multiple sequence alignments, phylogenetic analyses and integration with the KEGG pathway database. AVAILABILITY: http://biohealth.snu.ac.kr/GeneclusterViz/; http://microbial.informatics.indiana.edu/GeneclusterViz/

A platform to standardize, store, and visualize proteomics experimental data.

With the development of functional genomics research, large-scale proteomics studies are now widespread, presenting significant challenges for data storage, exchange, and analysis. Here we present the Integrated Proteomics Exploring Database (IPED) as a platform for managing proteomics experimental data (both process and result data). IPED is based on the schema of the Proteome Experimental Data Repository (PEDRo), and complies with the General Proteomics Standard (GPS) drafted by the Proteomics Standards Committee of the Human Proteome Organization. In our work, we developed three components for the IPED platform: the IPED client editor, IPED server software, and IPED web interface. The client editor collects experimental data and generates an extensible markup language (XML) data file compliant with PEDRo and GPS; the server software parses the XML data file and loads information into a core database; and the web interface displays experimental results, to provide a convenient graphic representation of data. Given software convenience and data abundance, IPED is a powerful platform for data exchange and presents an important resource for the proteomics community. In its current release, IPED is available at http://www.biosino.org/iped2.

Sys-BodyFluid: a systematical database for human body fluid proteome research.

Recently, body fluids have widely become an important target for proteomic research and proteomic study has produced more and more body fluid related protein data. A database is needed to collect and analyze these proteome data. Thus, we developed this web-based body fluid proteome database Sys-BodyFluid. It contains eleven kinds of body fluid proteomes, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk and amniotic fluid. Over 10,000 proteins are presented in the Sys-BodyFluid. Sys-BodyFluid provides the detailed protein annotations, including protein description, Gene Ontology, domain information, protein sequence and involved pathways. These proteome data can be retrieved by using protein name, protein accession number and sequence similarity. In addition, users can query between these different body fluids to get the different proteins identification information. Sys-BodyFluid database can facilitate the body fluid proteomics and disease proteomics research as a reference database. It is available at http://www.biosino.org/bodyfluid/.

Predicting protein N-glycosylation by combining functional domain and secretion information.

Protein N-glycosylation plays an important role in protein function. Yet, at present, few computational methods are available for the prediction of this protein modification. This prompted our development of a support vector machine (SVM)-based method for this task, as well as a partial least squares (PLS) regression based prediction method for comparison. A functional domain feature space was used to create SVM and PLS models, which achieved accuracies of 83.91% and 79.89%, respectively, as evaluated by a leave-one-out cross-validation. Subsequently, SVM and PLS models were developed based on functional domain and protein secretion information, which yielded accuracies of 89.13% and 86%, respectively. This analysis demonstrates that the protein functional domain and secretion information are both efficient predictors of N-glycosylation.

Predicting the protein SUMO modification sites based on Properties Sequential Forward Selection (PSFS).

Protein SUMO modification is an important post-translational modification and the optimization of prediction methods remains a challenge. Here, by using Support Vector Machines algorithm (SVM), a novel computational method was developed for SUMO modification site prediction based on Sequential Forward Selection (SFS) of hundreds of amino acid properties, which are collected by Amino Acid Index database (http://www.genome.jp/aaindex). Our method also compares with the 0/1 system, in which the 20 amino acids are represented by 20-dimensional vectors (A = 00000000000000000001, C = 00000000000000000010 and so on). The overall accuracy of leave-one-out cross-validation for our method reaches 89.18%, which is higher than 0/1 system. It indicated that the SUMO modification prediction process is highly related to the amino acid property and this approach here provide a helpful tool for further investigation of the SUMO modification and identification of sumoylation sites in proteins. The software is available at http://www.biosino.org/sumo.

Predicting O-glycosylation sites in mammalian proteins by using SVMs.

O-glycosylation is one of the most important, frequent and complex post-translational modifications. This modification can activate and affect protein functions. Here, we present three support vector machines models based on physical properties, 0/1 system, and the system combining the above two features. The prediction accuracies of the three models have reached 0.82, 0.85 and 0.85, respectively. The accuracies of the three SVMs methods were evaluated by 'leave-one-out' cross validation. This approach provides a useful tool to help identify the O-glycosylation sites in mammalian proteins. An online prediction web server is available at http://www.biosino.org/Oglyc.

MPSS: an integrated database system for surveying a set of proteins.

SUMMARY: We design and implement an integrated database system called 'multi-protein survey system' (MPSS), which provides a platform to retrieve information about many proteins at a time. This system integrates several important and widely used databases including SwissProt, TrEMBL, PDB and InterPro, plus useful references such as GO and KEGG to other databases. Users may submit a group of protein IDs, entry names, SwissProt/TrEMBL accession numbers or GenBank GIs through MPSS' web interface, and obtain protein annotation information from public databases and pre-computed molecular properties speedily. MPSS can also supply comprehensive information about query proteins, including 3D structures, domains, pathway, gene ontology and visual presentation of mapping to the GO tree and KEGG pathway, to provide an up-to-date view of available knowledge with regard to the structures and molecular functions of proteins under study. AVAILABILITY: MPSS is freely accessible at http://www.scbit.org/mpss/

Putative hAPN receptor binding sites in SARS_CoV spike protein.

AIM: To obtain the information of ligand-receptor binding between the S protein of SARS-CoV and CD13, identify the possible interacting domains or motifs related to binding sites, and provide clues for studying the functions of SARS proteins and designing anti-SARS drugs and vaccines. METHODS: On the basis of comparative genomics, the homology search, phylogenetic analyses, and multi-sequence alignment were used to predict CD13 related interacting domains and binding sites in the S protein of SARS-CoV. Molecular modeling and docking simulation methods were employed to address the interaction feature between CD13 and S protein of SARS-CoV in validating the bioinformatics predictions. RESULTS: Possible binding sites in the SARS-CoV S protein to CD13 have been mapped out by using bioinformatics analysis tools. The binding for one protein-protein interaction pair (D757-R761 motif of the SARS-CoV S protein to P585-A653 domain of CD13) has been simulated by molecular modeling and docking simulation methods. CONCLUSION: CD13 may be a possible receptor of the SARS-CoV S protein, which may be associated with the SARS infection. This study also provides a possible strategy for mapping the possible binding receptors of the proteins in a genome.