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PubMed2001

Bioinformatic assessment of mass spectrometric chemical derivatisation techniques for proteome database searching.

Identification of proteins from the mass spectra of peptide fragments generated by proteolytic cleavage using database searching has become one of the most powerful techniques in proteome science, capable of rapid and efficient protein identification. Using computer simulation, we have studied how the application of chemical derivatisation techniques may improve the efficiency of protein identification from mass spectrometric data. These approaches enhance ion yield and lead to the promotion of specific ions and fragments, yielding additional database search information. The impact of three alternative techniques has been assessed by searching representative proteome databases for both single proteins and simple protein mixtures. For example, by reliably promoting fragmentation of singly-charged peptide ions at aspartic acid residues after homoarginine derivatisation, 82% of yeast proteins can be unambiguously identified from a single typical peptide-mass datum, with a measured mass accuracy of 50 ppm, by using the associated secondary ion data. The extra search information also provides a means to confidently identify proteins in protein mixtures where only limited data are available. Furthermore, the inclusion of limited sequence information for the peptides can compensate and exceed the search efficiency available via high accuracy searches of around 5 ppm, suggesting that this is a potentially useful approach for simple protein mixtures routinely obtained from two-dimensional gels.

Animals↗

PubMed2005

Computational analysis of protein tyrosine phosphatases: practical guide to bioinformatics and data resources.

The exponential growth of sequence data has become a challenge to database curators and end-users alike and biologists seeking to utilize the data effectively are faced with numerous analysis methods. Here, with practical examples from our bioinformatics analysis of the protein tyrosine phosphatases (PTPs), we show how computational analysis can be exploited to fuel hypothesis-driven experimental research through the exploration of online databases. We cover the following elements: (i) similarity searches and strategies to collect a non-redundant database of tyrosine-specific PTP domains; (ii) utilization of this database to classify human, fly, and worm PTPs (based on alignments and phylogenetic analysis); (iii) three-dimensional structural analysis to identify conserved regions (structure-function) and non-conserved selectivity-determining regions (substrate specificity); and (iv) genomic analysis, including mapping of exon structure, identification of pseudogenes, and exploration of disease databases. We discuss the importance of manual curation, illustrating examples in which pseudogenes give rise to predicted proteins in GenBank and note that domain servers, such as PFAM and SMART, erroneously include dual-specificity and lipid phosphatases in their collection of tyrosine-specific PTPs. To capitalize on our annotated set of 402 PTP domains (from 47 species and five phyla), we identify sequence conservation across taxonomic categories and explore structure-function relationships among tandem domain receptor-like PTPs. We define three Src homology 2 domain-containing PTP genes in stingray, zebrafish, and fugu and speculate on their evolutionary relationship with human pseudogenes. Our annotated sequences, along with a web service for phylogenetic classification of PTP domains, are available online (http://ptp.cshl.edu and http://science.novonordisk.com/ptp).

Amino Acid Sequence↗

PubMed2005

The Gene Set Builder: collation, curation, and distribution of sets of genes.

BACKGROUND: In bioinformatics and genomics, there are many applications designed to investigate the common properties for a set of genes. Often, these multi-gene analysis tools attempt to reveal sequential, functional, and expressional ties. However, while tremendous effort has been invested in developing tools that can analyze a set of genes, minimal effort has been invested in developing tools that can help researchers compile, store, and annotate gene sets in the first place. As a result, the process of making or accessing a set often involves tedious and time consuming steps such as finding identifiers for each individual gene. These steps are often repeated extensively to shift from one identifier type to another; or to recreate a published set. In this paper, we present a simple online tool which - with the help of the gene catalogs Ensembl and GeneLynx - can help researchers build and annotate sets of genes quickly and easily. DESCRIPTION: The Gene Set Builder is a database-driven, web-based tool designed to help researchers compile, store, export, and share sets of genes. This application supports the 17 eukaryotic genomes found in version 32 of the Ensembl database, which includes species from yeast to human. User-created information such as sets and customized annotations are stored to facilitate easy access. Gene sets stored in the system can be "exported" in a variety of output formats - as lists of identifiers, in tables, or as sequences. In addition, gene sets can be "shared" with specific users to facilitate collaborations or fully released to provide access to published results. The application also features a Perl API (Application Programming Interface) for direct connectivity to custom analysis tools. A downloadable Quick Reference guide and an online tutorial are available to help new users learn its functionalities. CONCLUSION: The Gene Set Builder is an Ensembl-facilitated online tool designed to help researchers compile and manage sets of genes in a user-friendly environment. The application can be accessed via http://www.cisreg.ca/gsb/.

Computational Biology↗

PubMed2003

IMGT-ONTOLOGY for immunogenetics and immunoinformatics.

IMGT, the international ImMunoGeneTics information system(R) (http://imgt.cines.fr), is a high quality integrated knowledge resource specializing in immunoglobulins (IG), T cell receptors (TR), major histocompatibility complex (MHC) and related proteins of the immune system (RPI) of human and other vertebrates, created in 1989, by the Laboratoire d'ImmunoGenetique Moleculaire LIGM. IMGT provides a common access to standardized data which include nucleotide and protein sequences, oligonucleotide primers, gene maps, genetic polymorphisms, specificities, 2D and 3D structures. IMGT consists of several sequence databases (IMGT/LIGM-DB, IMGT/MHC-DB, IMGT/PRIMER-DB), one genome database (IMGT/GENE-DB) and one three-dimensional structure database (IMGT/3Dstructure-DB), interactive tools for sequence analysis (IMGT/V-QUEST, IMGT/JunctionAnalysis, IMGT/PhyloGene, IMGT/Allele-Align), for genome analysis (IMGT/GeneSearch, IMGT/GeneView, IMGT/LocusView) and for 3D structure analysis (IMGT/StructuralQuery), and Web resources ("IMGT Marie-Paule page") comprising 8000 HTML pages. IMGT other accesses include SRS, FTP, search by BLAST, etc. By its high quality and its easy data distribution, IMGT has important implications in medical research (repertoire in autoimmune diseases, AIDS, leukemias, lymphomas, myelomas), veterinary research, genome diversity and genome evolution studies of the adaptive immune responses, biotechnology related to antibody engineering (scFv, phage displays, combinatorial libraries) and therapeutical approaches (grafts, immunotherapy). IMGT is freely available at http://imgt.cines.fr.

Animals↗

PubMed2005

Annotated proteome of a human T-cell lymphoma.

As the reliable identification of proteins by tandem mass spectrometry becomes increasingly common, the full characterization of large data sets of proteins remains a difficult challenge. Our goal was to survey the proteome of a human T-cell lymphoma-derived cell line in a single set of experiments and present an automated method for the annotation of lists of proteins. A downstream application of these data includes the identification of novel pathogenetic and candidate diagnostic markers of T-cell lymphoma. Total protein isolated from cytoplasmic, membrane, and nuclear fractions of the SUDHL-1 T-cell lymphoma cell line was resolved by SDS-PAGE, and the entire gel lanes digested and analyzed by tandem mass spectrometry. Acquired data files were searched against the UniProt protein database using the SEQUEST algorithm. Search results for each subcellular fraction were analyzed using INTERACT and ProteinProphet. All protein identifications with an error rate of less than 10% were directly exported into excel and analyzed using GOMiner (NIH/NCI). The Gene ontology molecular function and cell location data were summarized for the identified proteins and results exported as user-interactive directed acyclic graphs. A total of 1105 unique proteins were identified and fully annotated, including numerous proteins that had not been previously characterized in lymphoma, in functional categories such as cell adhesion, migration, signaling, and stress response. This study demonstrates the utility of currently available bioinformatics tools for the robust identification and annotation of large numbers of proteins in a batchwise fashion.

Algorithms↗

PubMed1998

Towards detection of orthologues in sequence databases.

MOTIVATION: Numerous homologous sequences from diverse species can be retrieved from databases using programs such as BLAST. However, due to multigene families, evolutionary relationship often cannot be easily determined and proper functional assignment becomes difficult. Thus, discrimination between orthologues and paralogues within BLAST output lists of homologous sequences becomes more and more important. RESULT: We therefore developed a method that attempts to construct a reconciled tree from a gene tree of selected sequences and its corresponding phylogenetic tree of the species involved (species tree). An interface on the Web is developed to enable users to analyse the BLAST result. BLAST outputs are parsed and, for the selected sequences, multiple alignments are constructed either globally or for local regions. Bootstrapped trees are returned and compared with the expected species tree. In cases of discrepancies, gene duplications are assumed and a reconciled tree is computed. The reconciled tree shows probable orthologues and paralogues as predicted.

Computational Biology↗

PubMed2006

Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information.

MOTIVATION: Human single nucleotide polymorphisms (SNPs) are the most frequent type of genetic variation in human population. One of the most important goals of SNP projects is to understand which human genotype variations are related to Mendelian and complex diseases. Great interest is focused on non-synonymous coding SNPs (nsSNPs) that are responsible of protein single point mutation. nsSNPs can be neutral or disease associated. It is known that the mutation of only one residue in a protein sequence can be related to a number of pathological conditions of dramatic social impact such as Alzheimer's, Parkinson's and Creutzfeldt-Jakob's diseases. The quality and completeness of presently available SNPs databases allows the application of machine learning techniques to predict the insurgence of human diseases due to single point protein mutation starting from the protein sequence. RESULTS: In this paper, we develop a method based on support vector machines (SVMs) that starting from the protein sequence information can predict whether a new phenotype derived from a nsSNP can be related to a genetic disease in humans. Using a dataset of 21 185 single point mutations, 61% of which are disease-related, out of 3587 proteins, we show that our predictor can reach more than 74% accuracy in the specific task of predicting whether a single point mutation can be disease related or not. Our method, although based on less information, outperforms other web-available predictors implementing different approaches. AVAILABILITY: A beta version of the web tool is available at http://gpcr.biocomp.unibo.it/cgi/predictors/PhD-SNP/PhD-SNP.cgi

Algorithms↗

PubMed2006

Structure modeling of all identified G protein-coupled receptors in the human genome.

G protein-coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global C(alpha) root-mean-squared deviation from native of 4.6 angstroms, with a root-mean-squared deviation in the transmembrane helix region of 2.1 angstroms. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. All predicted GPCR models are freely available for noncommercial users on our Web site (http://www.bioinformatics.buffalo.edu/GPCR).

Algorithms↗

PubMed2004

Biomedical informatics: development of a comprehensive data warehouse for clinical and genomic breast cancer research.

The Windber Research Institute is an integrated high-throughput research center employing clinical, genomic and proteomic platforms to produce terabyte levels of data. We use biomedical informatics technologies to integrate all of these operations. This report includes information on a multi-year, multi-phase hybrid data warehouse project currently under development in the Institute. The purpose of the warehouse is to host the terabyte-level of internal experimentally generated data as well as data from public sources. We have previously reported on the phase I development, which integrated limited internal data sources and selected public databases. Currently, we are completing phase II development, which integrates our internal automated data sources and develops visualization tools to query across these data types. This paper summarizes our clinical and experimental operations, the data warehouse development, and the challenges we have faced. In phase III we plan to federate additional manual internal and public data sources and then to develop and adapt more data analysis and mining tools. We expect that the final implementation of the data warehouse will greatly facilitate biomedical informatics research.

Breast Neoplasms↗

PubMed2001

A genomic perspective on human proteases.

Over 400 human proteases documented in secondary databases can already be delineated in genomic sequence. A Genome Ontology annotation of 30585 sequences in the provisional human proteome set recognises 498 proteases, i.e. 1.6%. Homology searches against finished sequence and comparisons between mouse and zebrafish are likely to increase this total. However, the data already indicate that the mechanistic class, sequence family and domain distribution of the genomic complement of proteases is unlikely to shift significantly from that already observed in the transcript data. Genomically derived novel sequences will require bioinformatic analysis and biochemical verification. The increasing availability of annotated genomic data will enable studies of splice variants, transcriptional control, polymorphisms, pseudogenes, inactive homologues and evolution. Comparative work on complete human protease families should produce a more integrated picture of their biochemistry and physiology. Genomic data will also lead to the identification of new protease involvement in disease processes and their evaluation as drug targets.

Alternative Splicing↗

PubMed2004

Proteomic analysis of native metabotropic glutamate receptor 5 protein complexes reveals novel molecular constituents.

We used a proteomic approach to identify novel proteins that may regulate metabotropic glutamate receptor 5 (mGluR5) responses by direct or indirect protein interactions. This approach does not rely on the heterologous expression of proteins and offers the advantage of identifying protein interactions in a native environment. The mGluR5 protein was immunoprecipitated from rat brain lysates; co-immunoprecipitating proteins were analyzed by mass spectrometry and identified peptides were matched to protein databases to determine the correlating parent proteins. This proteomic approach revealed the interaction of mGluR5 with known regulatory proteins, as well as novel proteins that reflect previously unidentified molecular constituents of the mGluR5-signaling complex. Immunoblot analysis confirmed the interaction of high confidence proteins, such as phosphofurin acidic cluster sorting protein 1, microtubule-associated protein 2a and dynamin 1, as mGluR5-interacting proteins. These studies show that a proteomic approach can be used to identify candidate interacting proteins. This approach may be particularly useful for neurobiology applications where distinct protein interactions within a signaling complex can dramatically alter the outcome of the response to neurotransmitter release, or the disruption of normal protein interactions can lead to severe neurological and psychiatric disorders.

Algorithms↗

PubMed1999

Dictionary building via unsupervised hierarchical motif discovery in the sequence space of natural proteins.

Using Teiresias, a pattern discovery method that identifies all motifs present in any given set of protein sequences without requiring alignment or explicit enumeration of the solution space, we have explored the GenPept sequence database and built a dictionary of all sequence patterns with two or more instances. The entries of this dictionary, henceforth named seqlets, cover 98.12% of all amino acid positions in the input database and in essence provide a comprehensive finite set of descriptors for protein sequence space. As such, seqlets can be effectively used to describe almost every naturally occurring protein. In fact, seqlets can be thought of as building blocks of protein molecules that are a necessary (but not sufficient) condition for function or family equivalence memberships. Thus, seqlets can either define conserved family signatures or cut across molecular families and previously undetected sequence signals deriving from functional convergence. Moreover, we show that seqlets also can capture structurally conserved motifs. The availability of a dictionary of seqlets that has been derived in such an unsupervised, hierarchical manner is generating new opportunities for addressing problems that range from reliable classification and the correlation of sequence fragments with functional categories to faster and sensitive engines for homology searches, evolutionary studies, and protein structure prediction.

Amino Acid Motifs↗

PubMed2001

Comparison of ARM and HEAT protein repeats.

ARM and HEAT motifs are tandemly repeated sequences of approximately 50 amino acid residues that occur in a wide variety of eukaryotic proteins. An exhaustive search of sequence databases detected new family members and revealed that at least 1 in 500 eukaryotic protein sequences contain such repeats. It also rendered the similarity between ARM and HEAT repeats, believed to be evolutionarily related, readily apparent. All the proteins identified in the database searches could be clustered by sequence similarity into four groups: canonical ARM-repeat proteins and three groups of the more divergent HEAT-repeat proteins. This allowed us to build improved sequence profiles for the automatic detection of repeat motifs. Inspection of these profiles indicated that the individual repeat motifs of all four classes share a common set of seven highly conserved hydrophobic residues, which in proteins of known three-dimensional structure are buried within or between repeats. However, the motifs differ at several specific residue positions, suggesting important structural or functional differences among the classes. Our results illustrate that ARM and HEAT-repeat proteins, while having a common phylogenetic origin, have since diverged significantly. We discuss evolutionary scenarios that could account for the great diversity of repeats observed.

Amino Acid Motifs↗

PubMed2004

Integrative analysis of cancer-related data using CAP.

The development of human cancer is a highly complex process and can be considered the result of several combined events, such as genetic alterations, disturbance of signal transduction, or failure of immunological surveillance. Cancer-related databases usually focus on specific fields of research, e.g., cancer genetics or cancer immunology, whereas the complexity of cancer genesis requires an integrated analysis of heterogeneous data from several sources. Here we present the cancer-associated protein database (CAP), a novel analysis system for cancer-related data. CAP integrates data from multiple external databases, augments these data with functional annotations, and offers tools for statistical analysis of these data. We have employed CAP to analyze genes that have been found to cause an autoimmune response in cancer. In particular, we explored the connection between the autoimmune response, mutations, and overexpression of these genes. Our preliminary results suggest that mutations are not significant contributors to raising an antibody response against tumor antigens, whereas overexpression seems to play a more important role. We hereby demonstrate how different types of data can be integrated and analyzed successfully, providing interesting results. As the amount of available data is growing rapidly, a combined analysis will play an important role in exploring the genetic and immunological basis of cancer. CAP is freely available at the following web site: http://www.bioinf.uni-sb.de/CAP/.

Autoimmunity↗

PubMed2003

Efficiency improvement of peptide identification for an organism without complete genome sequence, using expressed sequence tag database and tandem mass spectral data.

We compared peptide identification by database (DB) search methods with de novo sequencing results for proteomics study in an organism without genome sequence information. When the former was done by searching the Expressed Sequence Tag (EST) DB of the sample organism or the NCBI nonredundant (nr) protein DB of green plants using either the MASCOT or SEQUEST software program, it was confirmed that the former is as accurate as the latter. Peptides identified from EST DB were twice as many as those from the nr protein DB, in spite of the fact that the EST DB has less data (26 222 EST) than the NCBI nr protein DB (224 238). This study demonstrates that EST DB with tandem mass spectra can be used reliably for high-throughput proteomics studies in an organism without genome information.

Algorithms↗

PubMed2005

Protein identification from product ion spectra of peptides validated by correlation between measured and predicted elution times in liquid chromatography/mass spectrometry.

Reversed-phase liquid chromatography (LC) directly coupled with electrospray-tandem mass spectrometry (MS/MS) is a successful choice to obtain a large number of product ion spectra from a complex peptide mixture. We describe a search validation program, ScoreRidge, developed for analysis of LC-MS/MS data. The program validates peptide assignments to product ion spectra resulting from usual probability-based searches against primary structure databases. The validation is based only on correlation between the measured LC elution time of each peptide and the deduced elution time from the amino acid sequence assigned to product ion spectra obtained from the MS/MS analysis of the peptide. Sufficient numbers of probable assignments gave a highly correlative curve. Any peptide assignments within a certain tolerance from the correlation curve were accepted for the following arrangement step to list identified proteins. Using this data validation program, host protein candidates responsible for interaction with human hepatitis B virus core protein were identified from a partially purified protein mixture. The present simple and practical program complements protein identification from usual product ion search algorithms and reduces manual interpretation of the search result data. It will lead to more explicit protein identification from complex peptide mixtures such as whole proteome digests from tissue samples.

Chromatography, Liquid↗

PubMed2005

ExonHunter: a comprehensive approach to gene finding.

MOTIVATION: We present ExonHunter, a new and comprehensive gene finding system that outperforms existing systems and features several new ideas and approaches. Our system combines numerous sources of information (genomic sequences, expressed sequence tags and protein databases of related species) into a gene finder based on a hidden Markov model in a novel and systematic way. In our framework, various sources of information are expressed as partial probabilistic statements about positions in the sequence and their annotation. We then combine these into the final prediction via a quadratic programming method, which we show to be an extension of existing methods. Allowing only partial statements is key to our transparent handling of missing information and coping with the heterogeneous character of individual sources of information. In addition, we give a new method for modeling the length distribution of intergenic regions in hidden Markov models. RESULTS: On a commonly used test set, ExonHunter performs significantly better than the existing gene finders ROSETTA, SLAM and TWINSCAN, with more than two-thirds of genes predicted completely correctly. AVAILABILITY: Supplementary material available at http://www.bioinformatics.uwaterloo.ca/supplements/05eh/

Animals↗

PubMed2006

Combining bioinformatics and phylogenetics to identify large sets of single-copy orthologous genes (COSII) for comparative, evolutionary and systematic studies: a test case in the euasterid plant clade.

We report herein the application of a set of algorithms to identify a large number (2869) of single-copy orthologs (COSII), which are shared by most, if not all, euasterid plant species as well as the model species Arabidopsis. Alignments of the orthologous sequences across multiple species enabled the design of "universal PCR primers," which can be used to amplify the corresponding orthologs from a broad range of taxa, including those lacking any sequence databases. Functional annotation revealed that these conserved, single-copy orthologs encode a higher-than-expected frequency of proteins transported and utilized in organelles and a paucity of proteins associated with cell walls, protein kinases, transcription factors, and signal transduction. The enabling power of this new ortholog resource was demonstrated in phylogenetic studies, as well as in comparative mapping across the plant families tomato (family Solanaceae) and coffee (family Rubiaceae). The combined results of these studies provide compelling evidence that (1) the ancestral species that gave rise to the core euasterid families Solanaceae and Rubiaceae had a basic chromosome number of x=11 or 12.2) No whole-genome duplication event (i.e., polyploidization) occurred immediately prior to or after the radiation of either Solanaceae or Rubiaceae as has been recently suggested.

Algorithms↗