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PubMed2004

The multiassembly problem: reconstructing multiple transcript isoforms from EST fragment mixtures.

Recent evidence of abundant transcript variation (e.g., alternative splicing, alternative initiation, alternative polyadenylation) in complex genomes indicates that cataloging the complete set of transcripts from an organism is an important project. One challenge is the fact that most high-throughput experimental methods for characterizing transcripts (such as EST sequencing) give highly detailed information about short fragments of transcripts or protein products, instead of a complete characterization of a full-length form. We analyze this "multiassembly problem"-reconstructing the most likely set of full-length isoform sequences from a mixture of EST fragment data-and present a graph-based algorithm for solving it. In a variety of tests, we demonstrate that this algorithm deals appropriately with coupling of distinct alternative splicing events, increasing fragmentation of the input data and different types of transcript variation (such as alternative splicing, initiation, polyadenylation, and intron retention). To test the method's performance on pure fragment (EST) data, we removed all mRNA sequences, and found it produced no errors in 40 cases tested. Using this algorithm, we have constructed an Alternatively Spliced Proteins database (ASP) from analysis of human expressed and genomic sequences, consisting of 13,384 protein isoforms of 4422 genes, yielding an average of 3.0 protein isoforms per gene.

Algorithms↗

PubMed2003

Predicting gene function from patterns of annotation.

The Gene Ontology (GO) Consortium has produced a controlled vocabulary for annotation of gene function that is used in many organism-specific gene annotation databases. This allows the prediction of gene function based on patterns of annotation. For example, if annotations for two attributes tend to occur together in a database, then a gene holding one attribute is likely to hold the other as well. We modeled the relationships among GO attributes with decision trees and Bayesian networks, using the annotations in the Saccharomyces Genome Database (SGD) and in FlyBase as training data. We tested the models using cross-validation, and we manually assessed 100 gene-attribute associations that were predicted by the models but that were not present in the SGD or FlyBase databases. Of the 100 manually assessed associations, 41 were judged to be true, and another 42 were judged to be plausible.

Animals↗

PubMed2003

GANESH: software for customized annotation of genome regions.

GANESH is a software package designed to support the genetic analysis of regions of human and other genomes. It provides a set of components that may be assembled to construct a self-updating database of DNA sequence, mapping data, and annotations of possible genome features. Once one or more remote sources of data for the target region have been identified, all sequences for that region are downloaded, assimilated, and subjected to a (configurable) set of standard database-searching and genome-analysis packages. The results are stored in compressed form in a relational database, and are updated automatically on a regular schedule so that they are always immediately available in their most up-to-date versions. A Java front-end, executed as a stand alone application or web applet, provides a graphical interface for navigating the database and for viewing the annotations. There are facilities for importing and exporting data in the format of the Distributed Annotation System (DAS), enabling a GANESH database to be used as a component of a DAS configuration. The system has been used to construct databases for about a dozen regions of human chromosomes and for three regions of mouse chromosomes.

Animals↗

PubMed2003

Profile-profile comparisons by COMPASS predict intricate homologies between protein families.

Recently we proposed a novel method of alignment-alignment comparison, COMPASS (the tool for COmparison of Multiple Protein Alignments with Assessment of Statistical Significance). Here we present several examples of the relations between PFAM protein families that were detected by COMPASS and that lead to the predictions of presently unresolved protein structures. We discuss relatively straightforward COMPASS predictions that are new and interesting to us, and that would require a substantial time and effort to justify even for a skilled PSI-BLAST user. All of the presented COMPASS hits are independently confirmed by other methods, including the ab initio structure-prediction method ROSETTA. The tertiary structure predictions made by ROSETTA proved to be useful for improving sequence-derived alignments, because they are based on a reasonable folding of the polypeptide chain rather than on the information from sequence databases. The ability of COMPASS to predict new relations within the PFAM database indicates the high sensitivity of COMPASS searches and substantiates its potential value for the discovery of previously unknown similarities between protein families.

Amino Acid Sequence↗

PubMed2004

Protein structure and fold prediction using tree-augmented naive Bayesian classifier.

For determining the structure class and fold class of Protein Structure, computer-based techniques have became essential considering the large volume of the data. Several techniques based on sequence similarity. Neural Networks, SVMs, etc have been applied. This paper presents a framework using the Tree-Augmented Networks (TAN) based on the theory of learning Bayesian networks but with less restrictive assumptions than the naive Bayesian networks. In order to enhance TAN's performance, pre-processing of data is done by feature discretization and post-processing is done by using Mean Probability Voting (MPV) scheme. The advantage of using Bayesian approach over other learning methods is that the network structure is intuitive. In addition, one can read off the TAN structure probabilities to determine the significance of each feature (say, Hydrophobicity) for each class, which help to further understand the mystery of protein structure. Experimental results and comparison with other works over two databases show the effectiveness of our TAN based framework. The idea is implemented as the BAYESPROT web server and it is available at http://www-appn.comp.nus.edu.sg/-bioinfo/bayesprot/Default.htm.

Artificial Intelligence↗

PubMed2006

An amino acid substitution matrix for protein conformation identification.

Amino acid substitution matrices play an essential role in protein sequence alignment, a fundamental task in bioinformatics. Most widely used matrices, such as PAM matrices derived from homologous sequences and BLOSUM matrices derived from aligned segments of PROSITE, did not integrate conformation information in their construction. There are a few structure-based matrices, which are derived from limited data of structure alignment. Using databases PDB_SELECT and DSSP, we create a database of sequence-conformation blocks which explicitly represent sequence-structure relationship. Members in a block are identical in conformation and are highly similar in sequence. From this block database, we derive a conformation-specific amino acid substitution matrix CBSM60. The matrix shows an improved performance in conformational segment search and homolog detection.

Amino Acid Sequence↗

PubMed2001

Docking molecules by families to increase the diversity of hits in database screens: computational strategy and experimental evaluation.

Molecular docking programs screen chemical databases for novel ligands that fit protein binding sites. When one compound fits the site well, close analogs typically do the same. Therefore, many of the compounds that are found in such screens resemble one another. This reduces the variety and novelty of the compounds suggested. In an attempt to increase the diversity of docking hit lists, the Available Chemicals Directory was grouped into families of related structures. All members of every family were docked and scored, but only the best scoring molecule of a high-ranking family was allowed in the hit list. The identity and scores of the other members of these families were recorded as annotations to the best family member, but they were not independently ranked. This family-based docking method was compared with molecule-by-molecule docking in screens against the structures of thymidylate synthase, dihydrofolate reductase (DHFR), and the cavity site of the mutant T4 lysozyme Leu99 --> Ala (L99A). In each case, the diversity of the hit list increased, and more families of known ligands were found. To investigate whether the newly identified hits were sensible, we tested representative examples experimentally for binding to L99A and DHFR. Of the six compounds tested against L99A, five bound to the internal cavity. Of the seven compounds tested against DHFR, six inhibited the enzyme with apparent K(i) values between 0.26 and 100 microM. The segregation of potential ligands into families of related molecules is a simple technique to increase the diversity of candidates suggested by database screens. The general approach should be applicable to most docking methods. Proteins 2001;42:279-293.

Algorithms↗

PubMed2005

The human platelet proteome mapped by peptide-centric proteomics: a functional protein profile.

Several studies have been published in which holistic approaches were used to characterise the proteome and transcriptome of human platelets. The key intent being that a deeper understanding of the normal and aberrant physiological functions of platelets can only be achieved if most biomolecular building blocks are mapped. Here we present the application of recently developed novel technologies that overcome some of the shortcomings of gel-based proteomics. Central in our approach is the so-called combined fractional diagonal chromatography (COFRADIC)-technology in which sets of representative peptides are sorted in a diagonal RP chromatographic system through a specific modification of their side chain. In this study we combined three different COFRADIC sorting techniques to analyse the proteome of human platelets. Methionyl, cysteinyl and amino terminal peptides were isolated and analysed by MS/MS. Merging the peptide identifications obtained after database searching resulted in a core set of 641 platelet proteins, which comprises the largest set identified today. In comparison to previously published platelet proteomes, we identified 404 novel platelet proteins containing a high number of hydrophobic membrane proteins and hypothetical proteins. Furthermore we discuss the observed characteristics and potential benefits of each of the different COFRADIC technologies for proteome analysis and highlight important issues that need to be considered when searching sequence databases using data obtained in peptide-centric, non-gel proteomics studies.

Animals↗

PubMed2006

Toward a comprehensive quantitative proteome database: protein expression map of lymphoid neoplasms by 2-D DIGE and MS.

Using 2-D DIGE, we constructed a quantitative 2-D database including 309 proteins corresponding to 389 protein spots across 42 lymphoid neoplasm cell lines. The proteins separated by 2-D PAGE were identified by MS and assigned to the expression data obtained by 2-D DIGE. The cell lines were categorized into four groups: those from Hodgkin's lymphoma (HL) (4 cell lines), B cell malignancies (19 cell lines), T cell malignancies (16 cell lines), and natural killer (NK) cell malignancies (3 cell lines). We characterized the proteins in the database by classifying them according to their expression level. We found 28 proteins with more than a 2-fold difference between the cell line groups. We also noted the proteins that allowed multidimensional separation to be achieved (1) between HL cells and other cells, (2) between the cells derived from B cells, T cells and NK cells, and (3) between HL cells and anaplastic large cell lymphoma cells. Decision tree classification identified five proteins that could be used to classify the 42 cell lines according to differentiation. These results suggest that the quantitative 2-D database using 2-D DIGE will be a useful resource for studying the mechanisms underlying the differentiation phenotypes of lymphoid neoplasms.

Algorithms↗

PubMed2005

Protein identification using 2D-LC-MS/MS.

Multidimensional liquid chromatography techniques have been coupled to tandem mass spectrometry to provide a robust method to identify proteins in complex mixtures. Data acquisition is interfaced directly with search algorithms for identification through cross-correlation with databases. This review describes the most recent advances in methodologies for protein identification by mass spectrometry and describes the limitations of the application of the technologies.

Algorithms↗

PubMedDate not supplied

InterRett--The application of bioinformatics to International Rett syndrome research.

InterRett, International Rett Syndrome Association (IRSA) Rett Phenotype Database, is a unique international project funded by the IRSA, which brings together child neurologists, geneticists, paediatricians, epidemiologists, researchers and families of affected children. The principal aim of InterRett is to increase the clinical understanding of Rett syndrome throughout the world and with the statistical power of large case numbers, determine any correlations between genotype and the phenotypic characteristics. Since establishment of the database in January 2003, InterRett has registered 286 cases from 24 countries, with family questionnaire data submitted on 242 cases and clinician data on 116 cases. Collated de-identified data gathered from families and clinicians have been incorporated into a searchable online database allowing simple and complex interrogation of the clinical phenotype information. InterRett will also serve as a clearing house for data to encourage inter-country collaboration between researchers and negotiations are in place with several countries for data contributions in excess of 1000 cases. The resulting online database will be an invaluable resource for understanding the nature and management of Rett syndrome, as well as providing a model for other rare childhood disorders.

Child↗

PubMed2004

DBSubLoc: database of protein subcellular localization.

We have built a protein subcellular localization annotation database, the DBSubLoc database, which is available at http://www.bioinfo.tsinghua. edu.cn/dbsubloc.html. Annotations were taken from primary protein databases, model organism genome projects and literature texts, and then were analyzed to dig out the subcellular localization features of the proteins. The proteins are also classified into different categories. Based on sequence alignment, non-redundant subsets of the database have been built, which may provide useful information for subcellular localization prediction. The database now contains >60,000 protein sequences including approximately 30,000 protein sequences in the non-redundant data sets. Online download, search and Blast tools are also available.

Animals↗

PubMed2005

ProbCons: Probabilistic consistency-based multiple sequence alignment.

To study gene evolution across a wide range of organisms, biologists need accurate tools for multiple sequence alignment of protein families. Obtaining accurate alignments, however, is a difficult computational problem because of not only the high computational cost but also the lack of proper objective functions for measuring alignment quality. In this paper, we introduce probabilistic consistency, a novel scoring function for multiple sequence comparisons. We present ProbCons, a practical tool for progressive protein multiple sequence alignment based on probabilistic consistency, and evaluate its performance on several standard alignment benchmark data sets. On the BAliBASE, SABmark, and PREFAB benchmark alignment databases, ProbCons achieves statistically significant improvement over other leading methods while maintaining practical speed. ProbCons is publicly available as a Web resource.

Algorithms↗

PubMed2006

Computational analysis and prediction of the binding motif and protein interacting partners of the Abl SH3 domain.

Protein-protein interactions, particularly weak and transient ones, are often mediated by peptide recognition domains, such as Src Homology 2 and 3 (SH2 and SH3) domains, which bind to specific sequence and structural motifs. It is important but challenging to determine the binding specificity of these domains accurately and to predict their physiological interacting partners. In this study, the interactions between 35 peptide ligands (15 binders and 20 non-binders) and the Abl SH3 domain were analyzed using molecular dynamics simulation and the Molecular Mechanics/Poisson-Boltzmann Solvent Area method. The calculated binding free energies correlated well with the rank order of the binding peptides and clearly distinguished binders from non-binders. Free energy component analysis revealed that the van der Waals interactions dictate the binding strength of peptides, whereas the binding specificity is determined by the electrostatic interaction and the polar contribution of desolvation. The binding motif of the Abl SH3 domain was then determined by a virtual mutagenesis method, which mutates the residue at each position of the template peptide relative to all other 19 amino acids and calculates the binding free energy difference between the template and the mutated peptides using the Molecular Mechanics/Poisson-Boltzmann Solvent Area method. A single position mutation free energy profile was thus established and used as a scoring matrix to search peptides recognized by the Abl SH3 domain in the human genome. Our approach successfully picked ten out of 13 experimentally determined binding partners of the Abl SH3 domain among the top 600 candidates from the 218,540 decapeptides with the PXXP motif in the SWISS-PROT database. We expect that this physical-principle based method can be applied to other protein domains as well.

Amino Acid Motifs↗

PubMed2002

Genome annotation techniques: new approaches and challenges.

As more of the human genome draft sequence is finished, and genomes from other organisms begin to be sequenced, the demand for accurate and reliable genome annotation will increase significantly. To facilitate this industrial-scale genome annotation, automated bioinformatics solutions are increasingly required. As a result, automatic genome annotation systems have become more important in gene discovery within recent years. The design of such large-scale bioinformatics systems is an evolving and dynamic field, based on central cores of bioinformatics software tools and relational databases. Not only must these systems efficiently manage and integrate large volumes of genomic data, but they must also deliver accurate gene predictions and effectively distribute annotation data to the biosciences community.

Computational Biology↗

PubMed2005

Beyond the clause: extraction of phosphorylation information from medline abstracts.

MOTIVATION: Phosphorylation is an important biochemical reaction that plays a critical role in signal transduction pathways and cell-cycle processes. A text mining system to extract the phosphorylation relation from the literature is reported. The focus of this paper is on the new methods developed and implemented to connect and merge pieces of information about phosphorylation mentioned in different sentences in the text. The effectiveness and accuracy of the system as a whole as well as that of the methods for extraction beyond a clause/sentence is evaluated using an independently annotated dataset, the Phospho.ELM database. The new methods developed to merge pieces of information from different sentences are shown to be effective in significantly raising the recall without much difference in precision.

Artificial Intelligence↗

PubMed2006

Hierarchical strategy for identifying active chemotype classes in compound databases.

A general methodology is presented for analyzing patterns of activity in compound databases, which is based on the use of structural chemotypes and provides a focused, hierarchical classification of active compounds. Each node in the hierarchical tree corresponds to a specific chemotype and is labeled by a unique code or identifier. All chemotypes at a given level of the hierarchy define equivalence classes, and those of higher structural resolution have a strict parent-child (i.e. subset) relationship to those of lower resolution. Active chemotypes contain a relatively high proportion of actives and are characterized through the use of enrichment plots. These plots show the relationship of occupancy to activity enrichment for a set of chemotypes at a given level of structural resolution. Paths through the hierarchy from chemotypes of lower to those of higher structural resolution (e.g. reduced cyclic system skeletons --> cyclic system skeletons --> cyclic systems --> complete molecules) are unique. Unique paths in the hierarchy that only pass through active chemotypes are called chains or paths of actives. These chains provide links for identifying structurally related active compounds at increasing levels of structural resolution. Analysis of actives can also be carried out at any specific level of structural resolution deemed appropriate by the investigator. Chemotype codes can be used to search compound databases for new molecules possessing these codes or sets of hierarchically related codes. An example, based on the NCI AIDS database, is presented that illustrates the general approach and provides a more detailed description of several interesting classes of active chemotypes and their inter-relationships.

Chemistry Techniques, Analytical↗

PubMed2002

Prediction of MHC class I binding peptides, using SVMHC.

BACKGROUND: T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested. RESULTS: Here, we present a novel approach, SVMHC, based on support vector machines to predict the binding of peptides to MHC class I molecules. This method seems to perform slightly better than two profile based methods, SYFPEITHI and HLA_BIND. The implementation of SVMHC is quite simple and does not involve any manual steps, therefore as more data become available it is trivial to provide prediction for more MHC types. SVMHC currently contains prediction for 26 MHC class I types from the MHCPEP database or alternatively 6 MHC class I types from the higher quality SYFPEITHI database. The prediction models for these MHC types are implemented in a public web service available at http://www.sbc.su.se/svmhc/. CONCLUSIONS: Prediction of MHC class I binding peptides using Support Vector Machines, shows high performance and is easy to apply to a large number of MHC class I types. As more peptide data are put into MHC databases, SVMHC can easily be updated to give prediction for additional MHC class I types. We suggest that the number of binding peptides needed for SVM training is at least 20 sequences.

Animals↗