Annotation of glycomics MS and MS/MS spectra using the GlycoWorkbench software tool.

The GlycoWorkbench software tool allows users to semiautomatically annotate glycomics MS and MS/MS spectra and MS glycoproteomics spectra. The GlycanBuilder software tool is embedded within GlycoWorkbench allowing users to draw glycan structures and export images of the drawn structures. This chapter demonstrates to users how to draw glycan structures within GlycoWorkbench using the GlycanBuilder software tool. This chapter also demonstrates how to use GlycoWorkbench to import MS and MS/MS glycomics spectra and use the cascading annotation feature to annotate both the MS and MS/MS spectra with a single command.

The GlycanBuilder and GlycoWorkbench glycoinformatics tools: updates and new developments.

During the EUROCarbDB project our group developed the GlycanBuilder and GlycoWorkbench glycoinformatics tools. This short communication summarizes the capabilities of these two tools and updates which have been made since the original publications in 2007 and 2008. GlycanBuilder is a tool that allows for the fast and intuitive drawing of glycan structures; this tool can be used standalone, embedded in web pages and can also be integrated into other programs. GlycoWorkbench has been designed to semi-automatically annotate glycomics data. This tool can be used to annotate mass spectrometry (MS) and MS/MS spectra of free oligosaccharides, N and O-linked glycans, GAGs (glycosaminoglycans) and glycolipids, as well as MS spectra of glycoproteins.

EUROCarbDB: An open-access platform for glycoinformatics.

The EUROCarbDB project is a design study for a technical framework, which provides sophisticated, freely accessible, open-source informatics tools and databases to support glycobiology and glycomic research. EUROCarbDB is a relational database containing glycan structures, their biological context and, when available, primary and interpreted analytical data from high-performance liquid chromatography, mass spectrometry and nuclear magnetic resonance experiments. Database content can be accessed via a web-based user interface. The database is complemented by a suite of glycoinformatics tools, specifically designed to assist the elucidation and submission of glycan structure and experimental data when used in conjunction with contemporary carbohydrate research workflows. All software tools and source code are licensed under the terms of the Lesser General Public License, and publicly contributed structures and data are freely accessible. The public test version of the web interface to the EUROCarbDB can be found at http://www.ebi.ac.uk/eurocarb.

Glycoproteomics: past, present and future.

This invited paper reviews the study of protein glycosylation, commonly known as glycoproteomics, beginning with the origins of the subject area in the early 1970s shortly after mass spectrometry was first applied to protein sequencing. We go on to describe current analytical approaches to glycoproteomic analyses, with exemplar projects presented in the form of the complex story of human glycodelin and the characterisation of blood group H eptitopes on the O-glycans of gp273 from Unio elongatulus. Finally, we present an update on the latest progress in the field of automated and semi-automated interpretation and annotation of these data in the form of GlycoWorkBench, a powerful informatics tool that provides valuable assistance in unravelling the complexities of glycoproteomic studies.

Characterizing the glycome of the mammalian immune system.

The outermost layer of all immune cells, the glycocalyx, is composed of a complex mixture of glycoproteins, glycolipids and lectins, which specifically recognize particular glycan epitopes. As the glycocalyx is the cell's primary interface with the external environment many biologically significant events can be attributed to glycan recognition. For this reason the rapidly expanding glycomics field is being increasingly recognized as an important component in our quest to better understand the functioning of the immune system. In this review, we highlight the current status of immune cell glycomics, with particular attention being paid to T- and B-lymphocytes and dendritic cells. We also describe the strategies and methodologies used to define immune cell glycomes.

GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans.

Mass spectrometry is the main analytical technique currently used to address the challenges of glycomics as it offers unrivalled levels of sensitivity and the ability to handle complex mixtures of different glycan variations. Determination of glycan structures from analysis of MS data is a major bottleneck in high-throughput glycomics projects, and robust solutions to this problem are of critical importance. However, all the approaches currently available have inherent restrictions to the type of glycans they can identify, and none of them have proved to be a definitive tool for glycomics. GlycoWorkbench is a software tool developed by the EUROCarbDB initiative to assist the manual interpretation of MS data. The main task of GlycoWorkbench is to evaluate a set of structures proposed by the user by matching the corresponding theoretical list of fragment masses against the list of peaks derived from the spectrum. The tool provides an easy to use graphical interface, a comprehensive and increasing set of structural constituents, an exhaustive collection of fragmentation types, and a broad list of annotation options. The aim of GlycoWorkbench is to offer complete support for the routine interpretation of MS data. The software is available for download from: http://www.eurocarbdb.org/applications/ms-tools.

Software tool for the structural determination of glycosaminoglycans by mass spectrometry.

Structural elucidation of glycosaminoglycans (GAGs) is one of the major challenges in biochemical analysis. This is mainly because of the diversity of GAG sulfation and N-acetylation patterns and variations in uronate isomers. ESI-MS and recently MALDI-MS methodologies are important strategies for investigating the molecular structure of GAGs. However, the interpretation of MS data produced by these strategies must take into account a large number of variables (including the number of monosaccharide residues, acetylations, sulfate groups, multiple charges, and exchanges between different cations). We have developed a bioinformatics tool to assist this complex interpretation task. The software is based on GlycoWorkbench, a tool for semiautomatic interpretation of glycan MS data. The tool generates the sugar backbones in all their variants (GAG family, composition, acetylation positions, and number of sulfates) and automatically matches them with the selected MS peaks. The backbones corresponding to a given peak are validated against the selected MS/MS peaks by generating all possible fragmentations. Native chondroitin sulfate and heparin oligosaccharides as well as chemically modified heparin oligomers have been successfully analyzed by MALDI- and ESI-MS and MS/MS, and the results of the semiautomated annotation of these mass spectra are presented here.

The GlycanBuilder: a fast, intuitive and flexible software tool for building and displaying glycan structures.

BACKGROUND: Carbohydrates play a critical role in human diseases and their potential utility as biomarkers for pathological conditions is a major driver for characterization of the glycome. However, the additional complexity of glycans compared to proteins and nucleic acids has slowed the advancement of glycomics in comparison to genomics and proteomics. The branched nature of carbohydrates, the great diversity of their constituents and the numerous alternative symbolic notations, make the input and display of glycans not as straightforward as for example the amino-acid sequence of a protein. Every glycoinformatic tool providing a user interface would benefit from a fast, intuitive, appealing mechanism for input and output of glycan structures in a computer readable format. RESULTS: A software tool for building and displaying glycan structures using a chosen symbolic notation is described here. The "GlycanBuilder" uses an automatic rendering algorithm to draw the saccharide symbols and to place them on the drawing board. The information about the symbolic notation is derived from a configurable graphical model as a set of rules governing the aspect and placement of residues and linkages. The algorithm is able to represent a structure using only few traversals of the tree and is inherently fast. The tool uses an XML format for import and export of encoded structures. CONCLUSION: The rendering algorithm described here is able to produce high-quality representations of glycan structures in a chosen symbolic notation. The automated rendering process enables the "GlycanBuilder" to be used both as a user-independent component for displaying glycans and as an easy-to-use drawing tool. The "GlycanBuilder" can be integrated in web pages as a Java applet for the visual editing of glycans. The same component is available as a web service to render an encoded structure into a graphical format. Finally, the "GlycanBuilder" can be integrated into other applications to create intuitive and appealing user interfaces: an example is the "GlycoWorkbench", a software tool for assisted annotation of glycan mass spectra. The "GlycanBuilder" represent a flexible, reliable and efficient solution to the problem of input and output of glycan structures in any glycomic tool or database.

Classification of small molecules by two- and three-dimensional decomposition kernels.

MOTIVATION: Several kernel-based methods have been recently introduced for the classification of small molecules. Most available kernels on molecules are based on 2D representations obtained from chemical structures, but far less work has focused so far on the definition of effective kernels that can also exploit 3D information. RESULTS: We introduce new ideas for building kernels on small molecules that can effectively use and combine 2D and 3D information. We tested these kernels in conjunction with support vector machines for binary classification on the 60 NCI cancer screening datasets as well as on the NCI HIV data set. Our results show that 3D information leveraged by these kernels can consistently improve prediction accuracy in all datasets. AVAILABILITY: An implementation of the small molecule classifier is available from http://www.dsi.unifi.it/neural/src/3DDK.

Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks.

Accurate predictions of metal-binding sites in proteins by using sequence as the only source of information can significantly help in the prediction of protein structure and function, genome annotation, and in the experimental determination of protein structure. Here, we introduce a method for identifying histidines and cysteines that participate in binding of several transition metals and iron complexes. The method predicts histidines as being in either of two states (free or metal bound) and cysteines in either of three states (free, metal bound, or in disulfide bridges). The method uses only sequence information by utilizing position-specific evolutionary profiles as well as more global descriptors such as protein length and amino acid composition. Our solution is based on a two-stage machine-learning approach. The first stage consists of a support vector machine trained to locally classify the binding state of single histidines and cysteines. The second stage consists of a bidirectional recurrent neural network trained to refine local predictions by taking into account dependencies among residues within the same protein. A simple finite state automaton is employed as a postprocessing in the second stage in order to enforce an even number of disulfide-bonded cysteines. We predict histidines and cysteines in transition-metal-binding sites at 73% precision and 61% recall. We observe significant differences in performance depending on the ligand (histidine or cysteine) and on the metal bound. We also predict cysteines participating in disulfide bridges at 86% precision and 87% recall. Results are compared to those that would be obtained by using expert information as represented by PROSITE motifs and, for disulfide bonds, to state-of-the-art methods.

DISULFIND: a disulfide bonding state and cysteine connectivity prediction server.

DISULFIND is a server for predicting the disulfide bonding state of cysteines and their disulfide connectivity starting from sequence alone. Optionally, disulfide connectivity can be predicted from sequence and a bonding state assignment given as input. The output is a simple visualization of the assigned bonding state (with confidence degrees) and the most likely connectivity patterns. The server is available at http://disulfind.dsi.unifi.it/.

Learning protein secondary structure from sequential and relational data.

We propose a method for sequential supervised learning that exploits explicit knowledge of short- and long-range dependencies. The architecture consists of a recursive and bi-directional neural network that takes as input a sequence along with an associated interaction graph. The interaction graph models (partial) knowledge about long-range dependency relations. We tested the method on the prediction of protein secondary structure, a task in which relations due to beta-strand pairings and other spatial proximities are known to have a significant effect on the prediction accuracy. In this particular task, interactions can be derived from knowledge of protein contact maps at the residue level. Our results show that prediction accuracy can be significantly boosted by the integration of interaction graphs.