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At least 685 records · Page 38Linked to original sources
PubMed2004

Using GO-PseAA predictor to predict enzyme sub-class.

Enzyme function is much less conserved than anticipated, i.e., the requirement for sequence similarity that implies similarity in enzymatic function is much higher than the requirement that implies similarity in protein structure. This is because the function of an enzyme is an extremely complicated problem that may involve very subtle structural details as well as many other physical chemistry factors. Accordingly, if simply based on the sequence similarity approach, it would hardly get a decent success rate in predicting enzyme sub-class even for a dataset consisting of samples with 50% sequence identity. To cope with such a situation, the GO-PseAA predictor was adopted to identify the sub-class for each of the six main enzyme families. It has been observed that, even for the much more stringent datasets in which none of the enzymes has 25% sequence identity to any others, the overall success rates are 73-95%, suggesting that the GO-PseAA predictor can catch the core features of the statistical samples concerned and may become a useful high throughput tool in proteomics and bioinformatics.

Algorithms↗
PubMed2005

Natterins, a new class of proteins with kininogenase activity characterized from Thalassophryne nattereri fish venom.

A novel family of proteins with kininogenase activity and unique primary structure was characterized using combined pharmacological, proteomic and transcriptomic approaches of Thalassophryne nattereri fish venom. The major venom components were isolated and submitted to bioassays corresponding to its main effects: nociception and edema. These activities were mostly located in one fraction (MS3), which was further fractionated. The isolated protein, named natterin, was able to induce edema, nociception and cleave human kininogen and kininogen-derived synthetic peptides, releasing kallidin (Lys-bradykinin). The enzymatic digestion was inhibited by kallikrein inhibitors as Trasylol and TKI. Natterin N-terminal peptide showed no similarity with already known proteins present in databanks. Primary structure of natterin was obtained by a transcriptomic approach using a representative cDNA library constructed from T. nattereri venom glands. Several expressed sequence tags (ESTs) were obtained and processed by bioinformatics revealing a major group (18%) of related sequences unknown to gene or protein sequence databases. This group included sequences showing the N-terminus of isolated natterin and was named Natterin family. Analysis of this family allowed us to identify five related sequences, which we called natterin 1-4 and P. Natterin 1 and 2 sequences include the N-terminus of the isolated natterin. Furthermore, internal peptides of natterin 1-3 were found in major spots of whole venom submitted to mass spectrometry/2DGE. Similarly to the ESTs, the complete sequences of natterins did not show any significant similarity with already described tissue kallikreins, kininogenases or any proteinase, all being entirely new. These data present a new task for the knowledge of the action of kininogenases and may help in understanding the mechanisms of T. nattereri fish envenoming, which is an important medical problem in North and Northeast of Brazil.

Amino Acid Sequence↗
PubMed2006

Cyanobacterial contribution to algal nuclear genomes is primarily limited to plastid functions.

A single cyanobacterial primary endosymbiosis that occurred approximately 1.5 billion years ago is believed to have given rise to the plastid in the common ancestor of the Plantae or Archaeplastida--the eukaryotic supergroup comprising red, green (including land plants), and glaucophyte algae. Critical to plastid establishment was the transfer of endosymbiont genes to the host nucleus (i.e., endosymbiotic gene transfer [EGT]). It has been postulated that plastid-derived EGT played a significant role in plant nuclear-genome evolution, with 18% (or 4,500) of all nuclear genes in Arabidopsis thaliana having a cyanobacterial origin with about one-half of these recruited for nonplastid functions. Here, we determine whether the level of cyanobacterial gene recruitment proposed for Arabidopsis is of the same magnitude in the algal sisters of plants by analyzing expressed-sequence tag (EST) data from the glaucophyte alga Cyanophora paradoxa. Bioinformatic analysis of 3,576 Cyanophora nuclear genes shows that 10.8% of these with significant database hits are of cyanobacterial origin and one-ninth of these have nonplastid functions. Our data indicate that unlike plants, early-diverging algal groups appear to retain a smaller number of endosymbiont genes in their nucleus, with only a minor proportion of these recruited for nonplastid functions.

Cell Nucleus↗
PubMed2004

A challenge for 21st century molecular biology and biochemistry: what are the causes of obligate autotrophy and methanotrophy?

We assess the use to which bioinformatics in the form of bacterial genome sequences, functional gene probes and the protein sequence databases can be applied to hypotheses about obligate autotrophy in eubacteria. Obligate methanotrophy and obligate autotrophy among the chemo- and photo-lithotrophic bacteria lack satisfactory explanation a century or more after their discovery. Various causes of these phenomena have been suggested, which we review in the light of the information currently available. Among these suggestions is the absence in vivo of a functional alpha-ketoglutarate dehydrogenase. The advent of complete and partial genome sequences of diverse autotrophs, methylotrophs and methanotrophs makes it possible to probe the reasons for the absence of activity of this enzyme. We review the role and evolutionary origins of the Krebs cycle in relation to autotrophic metabolism and describe the use of in silico methods to probe the partial and complete genome sequences of a variety of obligate genera for genes encoding the subunits of the alpha-ketoglutarate dehydrogenase complex. Nitrosomonas europaea and Methylococcus capsulatus, which lack the functional enzyme, were found to contain the coding sequences for the E1 and E2 subunits of alpha-ketoglutarate dehydrogenase. Comparing the predicted physicochemical properties of the polypeptides coded by the genes confirmed the putative gene products were similar to the active alpha-ketoglutarate dehydrogenase subunits of heterotrophs. These obligate species are thus genomically competent with respect to this enzyme but are apparently incapable of producing a functional enzyme. Probing of the full and incomplete genomes of some cyanobacterial and methanogenic genera and Aquifex confirms or suggests the absence of the genes for at least one of the three components of the alpha-ketoglutarate dehydrogenase complex in these obligate organisms. It is recognized that absence of a single functional enzyme may not explain obligate autotrophy in all cases and may indeed be only be one of a number of controls that impose obligate metabolism. Availability of more genome sequences from obligate genera will enable assessment of whether obligate autotrophy is due to the absence of genes for a few or many steps in organic compound metabolism. This problem needs the technologies and mindsets of the present generation of molecular microbiologists to resolve it.

Archaea↗
PubMed2002

Bioinformatics methods for the analysis of expression arrays: data clustering and information extraction.

Expression arrays facilitate the monitoring of changes in the expression patterns of large collections of genes. The analysis of expression array data has become a computationally-intensive task that requires the development of bioinformatics technology for a number of key stages in the process, such as image analysis, database storage, gene clustering and information extraction. Here, we review the current trends in each of these areas, with particular emphasis on the development of the related technology being carried out within our groups.

Abstracting and Indexing↗
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↗
PubMed2006

PROFbval: predict flexible and rigid residues in proteins.

UNLABELLED: The mobility of a residue on the protein surface is closely linked to its function. The identification of extremely rigid or flexible surface residues can therefore contribute information crucial for solving the complex problem of identifying functionally important residues in proteins. Mobility is commonly measured by B-value data from high-resolution three-dimensional X-ray structures. Few methods predict B-values from sequence. Here, we present PROFbval, the first web server to predict normalized B-values from amino acid sequence. The server handles amino acid sequences (or alignments) as input and outputs normalized B-value and two-state (flexible/rigid) predictions. The server also assigns a reliability index for each prediction. For example, PROFbval correctly identifies residues in active sites on the surface of enzymes as particularly rigid. AVAILABILITY: http://www.rostlab.org/services/profbval CONTACT: profbval@rostlab.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational Biology↗
PubMed2002

Genomics of phytopathogenic fungi and the development of bioinformatic resources.

Genomic resources available to researchers studying phytopathogenic fungi are limited. Here, we briefly review the genomic and bioinformatic resources available and the current status of fungal genomics. We also describe a relational database containing sequences of expressed sequence tags (ESTs) from three phytopathogenic fungi, Blumeria graminis, Magnaporthe grisea, and Mycosphaerella graminicola, and the methods and underlying principles required for its construction. The database contains significant annotation for each EST sequence and is accessible at http://cogeme.ex.ac.uk. An easy-to-use interface allows the user to identify gene sequences by using simple text queries or homology searches. New querying functions and large sequence sets from a variety of phytopathogenic species will be incorporated in due course.

Cladosporium↗
PubMed2005

Comparative study of apoptosis-related gene loci in human, mouse and rat genomes.

Many genes are involved in mammalian cell apoptosis pathway. These apoptosis genes often contain characteristic functional domains, and can be classified into at least 15 functional groups, according to previous reports. Using an integrated bioinformatics platform for motif or domain search from three public mammalian proteomes (International Protein Index database for human, mouse, and rat), we systematically cataloged all of the proteins involved in mammalian apoptosis pathway. By localizing those proteins onto the genomes, we obtained a gene locus centric apoptosis gene catalog for human, mouse and rat. Further phylogenetic analysis showed that most of the apoptosis related gene loci are conserved among these three mammals. Interestingly, about one-third of apoptosis gene loci form gene clusters on mammal chromosomes, and exist in the three species, which indicated that mammalian apoptosis gene orders are also conserved. In addition, some tandem duplicated gene loci were revealed by comparing gene loci clusters in the three species. All data produced in this work were stored in a relational database and may be viewed at http://pcas.cbi.pku.edu.cn/database/apd.php.

Animals↗
PubMed2005

Gene set coregulated by the Saccharomyces cerevisiae nonsense-mediated mRNA decay pathway.

The nonsense-mediated mRNA decay (NMD) pathway has historically been thought of as an RNA surveillance system that degrades mRNAs with premature translation termination codons, but the NMD pathway of Saccharomyces cerevisiae has a second role regulating the decay of some wild-type mRNAs. In S. cerevisiae, a significant number of wild-type mRNAs are affected when NMD is inactivated. These mRNAs are either wild-type NMD substrates or mRNAs whose abundance increases as an indirect consequence of NMD. A current challenge is to sort the mRNAs that accumulate when NMD is inactivated into direct and indirect targets. We have developed a bioinformatics-based approach to address this challenge. Our approach involves using existing genomic and function databases to identify transcription factors whose mRNAs are elevated in NMD-deficient cells and the genes that they regulate. Using this strategy, we have investigated a coregulated set of genes. We have shown that NMD regulates accumulation of ADR1 and GAL4 mRNAs, which encode transcription activators, and that Adr1 is probably a transcription activator of ATS1. This regulation is physiologically significant because overexpression of ADR1 causes a respiratory defect that mimics the defect seen in strains with an inactive NMD pathway. This strategy is significant because it allows us to classify the genes regulated by NMD into functionally related sets, an important step toward understanding the role NMD plays in the normal functioning of yeast cells.

Binding Sites↗
PubMed2006

Incorporating background frequency improves entropy-based residue conservation measures.

BACKGROUND: Several entropy-based methods have been developed for scoring sequence conservation in protein multiple sequence alignments. High scoring amino acid positions may correlate with structurally or functionally important residues. However, amino acid background frequencies are usually not taken into account in these entropy-based scoring schemes. RESULTS: We demonstrate that using a relative entropy measure that incorporates amino acid background frequency results in improved performance in identifying functional sites from protein multiple sequence alignments. CONCLUSION: Our results suggest that the application of appropriate background frequency information may lead to more biologically relevant results in many areas of bioinformatics.

Amino Acid Sequence↗
PubMed2006

Bioinformatics and data mining in proteomics.

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.

Computational Biology↗
PubMed2003

Computational analyses of high-throughput protein-protein interaction data.

Protein-protein interactions play important roles in nearly all events that take place in a cell. High-throughput experimental techniques enable the study of protein-protein interactions at the proteome scale through systematic identification of physical interactions among all proteins in an organism. High-throughput protein-protein interaction data, with ever-increasing volume, are becoming the foundation for new biological discoveries. A great challenge to bioinformatics is to manage, analyze, and model these data. In this review, we describe several databases that store, query, and visualize protein-protein interaction data. Comparison between experimental techniques shows that each high-throughput technique such as yeast two-hybrid assay or protein complex identification through mass spectrometry has its limitations in detecting certain types of interactions and they are complementary to each other. In silico methods using protein/DNA sequences, domain and structure information to predict protein-protein interaction can expand the scope of experimental data and increase the confidence of certain protein-protein interaction pairs. Protein-protein interaction data correlate with other types of data, including protein function, subcellular location, and gene expression profile. Highly connected proteins are more likely to be essential based on the analyses of the global architecture of large-scale interaction network in yeast. Use of protein-protein interaction networks, preferably in conjunction with other types of data, allows assignment of cellular functions to novel proteins and derivation of new biological pathways. As demonstrated in our study on the yeast signal transduction pathway for amino acid transport, integration of high-throughput data with traditional biology resources can transform the protein-protein interaction data from noisy information into knowledge of cellular mechanisms.

Animals↗
PubMed2001

[Proteomic analysis: why and how ?].

The proteome, first formalized in 1995, designs all the proteins expressed by the genome of a cell, tissu, or organ at a defined time. Proteomic analysis leads to a description of the regulation of gene expression by the study of proteins and of their post-translational modifications. Proteomic analysis is based on three technologies: 1) Two-dimensional electrophoresis allowing the separation of thousands of proteins from a single mixture; 2) mass spectrometry allowing the characterization of picoquantities of polypeptides and providing data on post-translational modifications; 3) Bioinformatic which is required for the quantification of protein level and for the constitution of databases of protein expression profiles. Complementing the methods of the genomics, the use of proteomic analysis is widely spreading in the fields of fundamental biology, biomedicine and pharmacology for the identification of new biological markers and therapeutic targets.

Animals↗
PubMed2005

Fast parsers for Entrez Gene.

NCBI completed the transition of its main genome annotation database from Locuslink to Entrez Gene in Spring 2005. However, to this date few parsers exist for the Entrez Gene annotation file. Owing to the widespread use of Locuslink and the popularity of Perl programming language in bioinformatics, a publicly available high performance Entrez Gene parser in Perl is urgently needed. We present four such parsers that were developed using several parsing approaches (Parse::RecDescent, Parse::Yapp, Perl-byacc and Perl 5 regular expressions) and provide the first in-depth comparison of these sophisticated Perl tools. Our fastest parser processes the entire human Entrez Gene annotation file in under 12 min on one Intel Xeon 2.4 GHz CPU and can be of help to the bioinformatics community during and after the transition from Locuslink to Entrez Gene.

Algorithms↗
PubMedDate not supplied

Proteome annotations and identifications of the human pulmonary fibroblast.

We hereby report on a three year project initiative undertaken by our research team encompassing large-scale protein expression profiling and annotations of human primary lung fibroblast cells. An overview is given of proteomic studies of the fibroblast target cell involved in several diseases such as asthma, idiopatic pulmonary disease, and COPD. It has been the objective within our research team to map and identify the protein expressions occurring in both activated-, as well as resting cell states. The JGGL database www.2DDB.org has been built around these data, allowing advanced hypothesis building using the interactive query bioinformatic tools developed. Gene ontology has been applied to these annotations, classifying and correlating protein expressions to function. The localization as well as the biological processes involved for the annotations are being presented including an annotation-, and sequence-identification strategy, resulting in close to 2000 protein identities. Both gel based, high resolution 2D-gels, and liquid-phase separation (three-dimensional HPLC), as well as the combination of gel- and LC-based approaches (1D-gels and nano-capillary LC, reversed-phase) were utilized. Protein sequencing and structure identities were acquired by a combination of MALDI-, and electrospray-mass spectrometry techniques. Phenotypical and morphological characterizations were also made for this human disease target cell in both stimulated- and resting-cell states. The use of functional assays that demonstrate the key regulating role of growth factors and cytokine stimuli such as PDGF, TGF-beta, and EGF and the effect of ECM molecules such as Biglycan, are also presented and discussed.

Amino Acid Sequence↗
PubMedDate not supplied

Opportunities at the intersection of bioinformatics and health informatics: a case study.

This paper provides a "viewpoint discussion" based on a presentation made to the 2000 Symposium of the American College of Medical Informatics. It discusses potential opportunities for researchers in health informatics to become involved in the rapidly growing field of bioinformatics, using the activities of the Yale Center for Medical Informatics as a case study. One set of opportunities occurs where bioinformatics research itself intersects with the clinical world. Examples include the correlations between individual genetic variation with clinical risk factors, disease presentation, and differential response to treatment; and the implications of including genetic test results in the patient record, which raises clinical decision support issues as well as legal and ethical issues. A second set of opportunities occurs where bioinformatics research can benefit from the technologic expertise and approaches that informaticians have used extensively in the clinical arena. Examples include database organization and knowledge representation, data mining, and modeling and simulation. Microarray technology is discussed as a specific potential area for collaboration. Related questions concern how best to establish collaborations with bioscientists so that the interests and needs of both sets of researchers can be met in a synergistic fashion, and the most appropriate home for bioinformatics in an academic medical center.

Computational Biology↗
PubMed2003

Bioinformatics for venom and toxin sciences.

Venomous animals produce a myriad of important pharmacological components. The individual components, or venoms (toxins), are used in ion channel and receptor studies, drug discovery, and formulation of insecticides. The toxin data are scattered across public databases which provide sequence and structural descriptions, but very limited functional annotation. The exponential growth of newly identified toxin data has created a need for better data management. Venominformatics is a systematic bioinformatics approach in which classified, consolidated and cleaned venom data are stored into repositories and integrated with advanced bioinformatics tools for the analysis of structure and function of toxins. Venominformatics complements experimental studies and helps reduce the number of essential experiments.

Animals↗