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PubMed2006

A systematic analysis of lineage-specific evolution in metabolic pathways.

In a search for the lineage-specific evolution of pathways between human, chimpanzee, mouse, and rat, orthologous gene families were generated from genome sequences. For each family, a model-based ratio of nonsynonymous to synonymous nucleotide substitution rates was calculated. Where the free-ratio model of individual ratios on each branch was supported, these families were mapped to two databases of metabolic pathways (KEGG and BioCyc) and the lineage-specific evolution of pathways was evaluated. The most similar pathway evolution was seen between mouse and rat, while the evolutionary pattern between human and chimpanzee was less correlated. Individual pathways in the human lineage were observed to evolve in a faster, lineage-specific manner, including the pathway involving arachidonic acid metabolism (identified through the KEGG analysis) and pyrimidine metabolism (identified through both analyses).

Animals↗

PubMed2003

A transition probability model for amino acid substitutions from blocks.

Substitution matrices have been useful for sequence alignment and protein sequence comparisons. The BLOSUM series of matrices, which had been derived from a database of alignments of protein blocks, improved the accuracy of alignments previously obtained from the PAM-type matrices estimated from only closely related sequences. Although BLOSUM matrices are scoring matrices now widely used for protein sequence alignments, they do not describe an evolutionary model. BLOSUM matrices do not permit the estimation of the actual number of amino acid substitutions between sequences by correcting for multiple hits. The method presented here uses the Blocks database of protein alignments, along with the additivity of evolutionary distances, to approximate the amino acid substitution probabilities as a function of actual evolutionary distance. The PMB (Probability Matrix from Blocks) defines a new evolutionary model for protein evolution that can be used for evolutionary analyses of protein sequences. Our model is directly derived from, and thus compatible with, the BLOSUM matrices. The model has the additional advantage of being easily implemented.

Amino Acid Substitution↗

PubMed2002

InterPro: an integrated documentation resource for protein families, domains and functional sites.

The exponential increase in the submission of nucleotide sequences to the nucleotide sequence database by genome sequencing centres has resulted in a need for rapid, automatic methods for classification of the resulting protein sequences. There are several signature and sequence cluster-based methods for protein classification, each resource having distinct areas of optimum application owing to the differences in the underlying analysis methods. In recognition of this, InterPro was developed as an integrated documentation resource for protein families, domains and functional sites, to rationalise the complementary efforts of the individual protein signature database projects. The member databases - PRINTS, PROSITE, Pfam, ProDom, SMART and TIGRFAMs - form the InterPro core. Related signatures from each member database are unified into single InterPro entries. Each InterPro entry includes a unique accession number, functional descriptions and literature references, and links are made back to the relevant member database(s). Release 4.0 of InterPro (November 2001) contains 4,691 entries, representing 3,532 families, 1,068 domains, 74 repeats and 15 sites of post-translational modification (PTMs) encoded by different regular expressions, profiles, fingerprints and hidden Markov models (HMMs). Each InterPro entry lists all the matches against SWISS-PROT and TrEMBL (2,141,621 InterPro hits from 586,124 SWISS-PROT and TrEMBL protein sequences). The database is freely accessible for text- and sequence-based searches.

Algorithms↗

PubMed2006

The structures of Escherichia coli O-polysaccharide antigens.

Escherichia coli is usually a non-pathogenic member of the human colonic flora. However, certain strains have acquired virulence factors and may cause a variety of infections in humans and in animals. There are three clinical syndromes caused by E. coli: (i) sepsis/meningitis; (ii) urinary tract infection and (iii) diarrhoea. Furthermore the E. coli causing diarrhoea is divided into different 'pathotypes' depending on the type of disease, i.e. (i) enterotoxigenic; (ii) enteropathogenic; (iii) enteroinvasive; (iv) enterohaemorrhagic; (v) enteroaggregative and (vi) diffusely adherent. The serotyping of E. coli based on the somatic (O), flagellar (H) and capsular polysaccharide antigens (K) is used in epidemiology. The different antigens may be unique for a particular serogroup or antigenic determinants may be shared, resulting in cross-reactions with other serogroups of E. coli or even with other members of the family Enterobacteriacea. To establish the uniqueness of a particular serogroup or to identify the presence of common epitopes, a database of the structures of O-antigenic polysaccharides has been created. The E. coli database (ECODAB) contains structures, nuclear magnetic resonance chemical shifts and to some extent cross-reactivity relationships. All fields are searchable. A ranking is produced based on similarity, which facilitates rapid identification of strains that are difficult to serotype (if known) based on classical agglutinating methods. In addition, results pertinent to the biosynthesis of the repeating units of O-antigens are discussed. The ECODAB is accessible to the scientific community at http://www.casper.organ.su.se/ECODAB/.

Carbohydrate Sequence↗

PubMed2004

Identification of trace element-containing proteins in genomic databases.

Development of bioinformatics tools provided researchers with the ability to identify full sets of trace element-containing proteins in organisms for which complete genomic sequences are available. Recently, independent bioinformatics methods were used to identify all, or almost all, genes encoding selenocysteine-containing proteins in human, mouse, and Drosophila genomes, characterizing entire selenoproteomes in these organisms. It also should be possible to search for entire sets of other trace element-associated proteins, such as metal-containing proteins, although methods for their identification are still in development.

Animals↗

PubMed2005

Discovery and integrative neuroscience.

Hypothesis driven research has been shown to be an excellent model for pursuing investigations in neuroscience. The Human Genome Project demonstrated the added value of discovery research, especially in areas where large amounts of data are produced. Neuroscience has become a data rich field, and one that would be enhanced by incorporating the discovery approach. Databases, as well as analytical, modeling and simulation tools, will have to be developed, and they will need to be interoperable and federated. This paper presents an overview of the development of the field of neuroscience databases and associate tools: Neuroinformatics. The primary focus is on the impact of NIH funding of this process. The important issues of data sharing, as viewed from the perspective of the scientist and private and public funding organizations, are discussed. Neuroinformatics will provide more than just a sophisticated array of information technologies to help scientists understand and integrate nervous system data. It will make available powerful models of neural functions and facilitate discovery, hypothesis formulation and electronic collaboration.

Animals↗

PubMed2006

MUSTANG: a multiple structural alignment algorithm.

Multiple structural alignment is a fundamental problem in structural genomics. In this article, we define a reliable and robust algorithm, MUSTANG (MUltiple STructural AligNment AlGorithm), for the alignment of multiple protein structures. Given a set of protein structures, the program constructs a multiple alignment using the spatial information of the C(alpha) atoms in the set. Broadly based on the progressive pairwise heuristic, this algorithm gains accuracy through novel and effective refinement phases. MUSTANG reports the multiple sequence alignment and the corresponding superposition of structures. Alignments generated by MUSTANG are compared with several handcurated alignments in the literature as well as with the benchmark alignments of 1033 alignment families from the HOMSTRAD database. The performance of MUSTANG was compared with DALI at a pairwise level, and with other multiple structural alignment tools such as POSA, CE-MC, MALECON, and MultiProt. MUSTANG performs comparably to popular pairwise and multiple structural alignment tools for closely related proteins, and performs more reliably than other multiple structural alignment methods on hard data sets containing distantly related proteins or proteins that show conformational changes.

Algorithms↗

PubMed2005

Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates.

MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described.

Chemistry, Pharmaceutical↗

PubMed1999

Exhaustive mining of EST libraries for genes differentially expressed in normal and tumour tissues.

A four-step procedure for the efficient and systematic mining of whole EST libraries for differentially expressed genes is presented. After eliminating redundant entries from the EST library under investigation (step 1), contigs of maximal length are built upon each remaining EST using about 4 000 000 public and proprietary ESTs (step 2). These putative genes are compared against a database comprising ESTs from 16 different tissues (both normal and tumour affected) to determine whether or not they are differentially expressed (step 3; electronic northern). Fisher's exact test is used to assess the significance of differential expression. In step 4, an attempt is made to characterise the contigs obtained in the assembly through database comparison. A case study of the CGAP library NCI_CGAP_Br1.1, a library made from three (well, moderately, and poorly differentiated) invasive ductal breast tumours (2126 ESTs in total) was carried out. Of the maximal contigs, 139 were found to be significantly (alpha = 0.05) over-expressed in breast tumour tissue, while 13 appeared to be down-regulated.

Animals↗

PubMedDate not supplied

In silico analysis of the human kallikrein gene 6.

Kallikreins are a family of 15 serine proteases clustered together on the long arm of chromosome 19. Recent reports have linked kallikreins to malignancy. The human kallikrein gene 6 (KLK6) is a newly characterized member of the human kallikrein gene family. Recent work has focused on the possible role of this gene and its protein product as a tumor marker and its involvement in diseases of the central nervous system. In this study, we performed extensive in silico analyses of KLK6 expression from different databases using various bioinformatic tools. These data enabled us to construct and verify the longest transcript for this kallikrein, to identify several polymorphisms among published sequences and to summarize the 21 single-nucleotide polymorphisms of the gene. Our expressed sequence tag (EST) analyses suggest the existence of seven new splice variants of the gene, in addition to the already reported ones. Most of these variants were identified in libraries from cancerous tissues. KLK6 orthologues were identified from three other species with approximately 86% overall homology with rat and mouse orthologues. We also utilized several databases to compare KLK6 gene expression in normal and cancerous tissues. The serial analysis of gene expression and EST expression profiles showed upregulation of the gene in female genital (ovarian and uterine) and gastrointestinal (gastric, colon, esophageal and pancreatic) cancers. Significant downregulation was observed in breast cancers and brain tumors, in relation to their normal counterparts.

Brain Neoplasms↗

PubMed2004

HOMSTRAD: recent developments of the Homologous Protein Structure Alignment Database.

HOMSTRAD (http://www-cryst.bioc.cam.ac.uk/ homstrad/) is a collection of protein families, clustered on the basis of sequence and structural similarity. The database is unique in that the protein family sequence alignments have been specially annotated using the program, JOY, to highlight a wide range of structural features. Such data are useful for identifying key structurally conserved residues within the families. Superpositions of the structures within each family are also available and a sensitive structure-aided search engine, FUGUE, can be used to search the database for matches to a query protein sequence. Historically, HOMSTRAD families were generated using several key pieces of software, including COMPARER and MNYFIT, and held in a number of flat files and indexes. A new relational database version of HOMSTRAD, HOMSTRAD BETA (http://www-cryst.bioc.cam. ac.uk/homstradbeta/) is being developed using MySQL. This relational data structure provides more flexibility for future developments, reduces update times and makes data more easily accessible. Consequently it has been possible to add a number of new web features including a custom alignment facility. Altogether, this makes HOMSTRAD and its new BETA version, an excellent resource both for comparative modelling and for identifying distant sequence/structure similarities between proteins.

Amino Acid Sequence↗

PubMed2004

The Database of Interacting Proteins: 2004 update.

The Database of Interacting Proteins (http://dip.doe-mbi.ucla.edu) aims to integrate the diverse body of experimental evidence on protein-protein interactions into a single, easily accessible online database. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks.

Animals↗

PubMed2001

How to guarantee optimal stability for most representative structures in the Protein Data Bank.

We proposed recently an optimization method to derive energy parameters for simplified models of protein folding. The method is based on the maximization of the thermodynamic average of the overlap between protein native structures and a Boltzmann ensemble of alternative structures. Such a condition enforces protein models whose ground states are most similar to the corresponding native states. We present here an extensive testing of the method for a simple residue-residue contact energy function and for alternative structures generated by threading. The optimized energy function guarantees high stability and a well-correlated energy landscape to most representative structures in the PDB database. Failures in the recognition of the native structure can be attributed to the neglect of interactions between different chains in oligomeric proteins or with cofactors. When these are taken into account, only very few X-ray structures are not recognized. Most of them are short inhibitors or fragments and one is a structure that presents serious inconsistencies. Finally, we discuss the reasons that make NMR structures more difficult to recognizeCopyright 2001 Wiley-Liss, Inc.

Computational Biology↗

PubMed2000

SH3-SPOT: an algorithm to predict preferred ligands to different members of the SH3 gene family.

We have developed a procedure to predict the peptide binding specificity of an SH3 domain from its sequence. The procedure utilizes information extracted from position-specific contacts derived from six SH3/peptide or SH3/protein complexes of known structure. The framework of SH3/peptide contacts defined on the structure of the complexes is used to build a residue-residue interaction database derived from ligands obtained by panning peptide libraries displayed on filamentous phage. The SH3-specific interaction database is a multidimensional array containing frequencies of position-specific contacts. As input, SH3-SPOT requires the sequence of an SH3 domain and of a query decapeptide ligand. The array, that we call the SH3-specific matrix, is then used to evaluate the probability that the peptide would bind the given SH3 domain. This procedure is fast enough to be applied to the entire protein sequence database. Panning experiments were performed to search putative specific ligands of different SH3 domains in a database of decapeptides, or in a database of protein sequences. The procedure ranked some of the natural partners of interaction of a number of SH3 domains among the best ligands of the approximately 5. 6x10(9) different decapeptides in the SWISSPROT database. We expect the predictive power of the method to increase with the enrichment of the SH3-specific matrix by interaction data derived from new complex structures or from the characterization of new ligands. The procedure was developed using the SH3 domain family as test case but its application can easily be extended to other families of protein domains (such as, SH2, MHC, EH, PDZ, etc.).

Algorithms↗

PubMed2005

An intronic peroxisome proliferator-activated receptor-binding sequence mediates fatty acid induction of the human carnitine palmitoyltransferase 1A.

The liver plays a central role in the response to fasting. The hormonal profile in this condition, low insulin, and high concentrations of glucagon in plasma, induce the release of large amounts of fatty acids from adipose tissue. Prolonged starvation can therefore induce a dramatic change in the fatty acid oxidative capacity of liver metabolism. Modulation of gene expression by PPARalpha plays a crucial role in this response. While a major role for PPARalpha in the liver is to produce ketone bodies as fuel through beta-oxidation for peripheral tissues during fast, its participation in the control of CPT1A, the rate-limiting step of the pathway, remains controversial. Using Web-based software (VISTA) combining transcription factor binding site database searches with comparative sequence analyses, we have localized a conserved functional PPAR responsive element downstream of the transcriptional start site of the human CPT1A gene. We have shown that this sequence is fundamental for fatty acids or PGC1-induced transcriptional activation of the CPT1A gene. These results corroborate the hypothesis that PPARalpha regulates the limiting step in the oxidation of fatty acids in liver mitochondria.

Amino Acid Sequence↗

PubMed2003

Computational approaches to protein-protein interaction.

The interactions between proteins allow the cell's life. A number of experimental, genome-wide, high-throughput studies have been devoted to the determination of protein-protein interactions and the consequent interaction networks. Here, the bioinformatics methods dealing with protein-protein interactions and interaction network are overviewed. 1. Interaction databases developed to collect and annotate this immense amount of data; 2. Automated data mining techniques developed to extract information about interactions from the published literature; 3. Computational methods to assess the experimental results developed as a consequence of the finding that the results of high-throughput methods are rather inaccurate; 4. Exploitation of the information provided by protein interaction networks in order to predict functional features of the proteins; and 5. Prediction of protein-protein interactions.

Algorithms↗

PubMed2001

ParAlign: a parallel sequence alignment algorithm for rapid and sensitive database searches.

There is a need for faster and more sensitive algorithms for sequence similarity searching in view of the rapidly increasing amounts of genomic sequence data available. Parallel processing capabilities in the form of the single instruction, multiple data (SIMD) technology are now available in common microprocessors and enable a single microprocessor to perform many operations in parallel. The ParAlign algorithm has been specifically designed to take advantage of this technology. The new algorithm initially exploits parallelism to perform a very rapid computation of the exact optimal ungapped alignment score for all diagonals in the alignment matrix. Then, a novel heuristic is employed to compute an approximate score of a gapped alignment by combining the scores of several diagonals. This approximate score is used to select the most interesting database sequences for a subsequent Smith-Waterman alignment, which is also parallelised. The resulting method represents a substantial improvement compared to existing heuristics. The sensitivity and specificity of ParAlign was found to be as good as Smith-Waterman implementations when the same method for computing the statistical significance of the matches was used. In terms of speed, only the significantly less sensitive NCBI BLAST 2 program was found to outperform the new approach. Online searches are available at http://dna.uio.no/search/

Algorithms↗

PubMed2005

Integration of text- and data-mining using ontologies successfully selects disease gene candidates.

Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (+/-18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.

Anatomy↗