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At least 1,279 records · Page 71Linked to original sources
PubMed2001

Profiling the malaria genome: a gene survey of three species of malaria parasite with comparison to other apicomplexan species.

We have undertaken the first comparative pilot gene discovery analysis of approximately 25,000 random genomic and expressed sequence tags (ESTs) from three species of Plasmodium, the infectious agent that causes malaria. A total of 5482 genome survey sequences (GSSs) and 5582 ESTs were generated from mung bean nuclease (MBN) and cDNA libraries, respectively, of the ANKA line of the rodent malaria parasite Plasmodium berghei, and 10,874 GSSs generated from MBN libraries of the Salvador I and Belem lines of Plasmodium vivax, the most geographically wide-spread human malaria pathogen. These tags, together with 2438 Plasmodium falciparum sequences present in GenBank, were used to perform first-pass assembly and transcript reconstruction, and non-redundant consensus sequence datasets created. The datasets were compared against public protein databases and more than 1000 putative new Plasmodium proteins identified based on sequence similarity. Homologs of previously characterized Plasmodium genes were also identified, increasing the number of P. vivax and P. berghei sequences in public databases at least 10-fold. Comparative studies with other species of Apicomplexa identified interesting homologs of possible therapeutic or diagnostic value. A gene prediction program, Phat, was used to predict probable open reading frames for proteins in all three datasets. Predicted and non-redundant BLAST-matched proteins were submitted to InterPro, an integrated database of protein domains, signatures and families, for functional classification. Thus a partial predicted proteome was created for each species. This first comparative analysis of Plasmodium protein coding sequences represents a valuable resource for further studies on the biology of this important pathogen.

Animals↗
PubMed2002

Toxicoproteomics -- a new preclinical tool.

The publication of the human genome has presented the scientific community with an unprecedented amount of genetic information with the potential to revolutionize the drug discovery process. This information could be used to identify novel drug targets and disease markers or could aid in the development of personalized medicines. The realization that genetic changes must ultimately influence protein function has pushed the field of proteomics further into the limelight. In this review the applications of proteomics to the field of toxicology will be discussed. It is anticipated that, in the future, toxicologists will apply a range of genomic and proteomic techniques to address issues in toxicity.

Animals↗
PubMedDate not supplied

Global proteome discovery using an online three-dimensional LC-MS/MS.

We have developed a proteomics technology featuring on-line three-dimensional liquid chromatography coupled to tandem mass spectrometry (3D LC-MS/MS). Using 3D LC-MS/MS, the yeast-soluble, urea-solubilized peripheral membrane and SDS-solubilized membrane protein samples collectively yielded 3019 unique yeast protein identifications with an average of 5.5 peptides per protein from the 6300-gene Saccharomyces Genome Database searched with SEQUEST. A single run of the urea-solubilized sample yielded 2255 unique protein identifications, suggesting high peak capacity and resolving power of 3D LC-MS/MS. After precipitation of SDS from the digested membrane protein sample, 3D LC-MS/MS allowed the analysis of membrane proteins. Among 1221 proteins containing two or more predicted transmembrane domains, 495 such proteins were identified. The improved yeast proteome data allowed the mapping of many metabolic pathways and functional categories. The 3D LC-MS/MS technology provides a suitable tool for global proteome discovery.

Amino Acid Sequence↗
PubMed2006

Classification of proteins based on minimal modular repeats: lessons from nature in protein design.

Proteins containing internal repeats within their primary sequence have received increased attention recently, as the extent of their presence in various organisms is recognized more fully, and their role in evolution is more thoroughly studied. Presented here is a technique used to detect and classify proteins based on a modular evolutionary phenomenon that results in a series of small internal repeats. The parameters chosen are based on a minimum segment of seven residues that result in simple functional scaffolds. The genomes and corresponding proteomes of a variety of eubacteria and archaea have been analyzed using an algorithm that searches prokaryotic genomes for proteins containing small conserved repeats assembled in a modular fashion similar to a recently characterized protein from the organism Nitrosomonas europaea. This analysis has revealed additional proteins present in N. europaea with similar modular characteristics. A further survey of a variety of organisms demonstrates that this evolutionary pathway has been utilized in other organisms as well, to yield a broad assortment of small modular proteins. A thorough description of the sequential characteristics of these modular proteins follows, along with a selection and discussion of the various proteins uncovered through this expanded search and analysis. Several databases of the proteins uncovered from this work and the program used to perform the search are available.

Algorithms↗
PubMed2006

Predicting the solvent accessibility of transmembrane residues from protein sequence.

In this study, we propose a novel method to predict the solvent accessible surface areas of transmembrane residues. For both transmembrane alpha-helix and beta-barrel residues, the correlation coefficients between the predicted and observed accessible surface areas are around 0.65. On the basis of predicted accessible surface areas, residues exposed to the lipid environment or buried inside a protein can be identified by using certain cutoff thresholds. We have extensively examined our approach based on different definitions of accessible surface areas and a variety of sets of control parameters. Given that experimentally determining the structures of membrane proteins is very difficult and membrane proteins are actually abundant in nature, our approach is useful for theoretically modeling membrane protein tertiary structures, particularly for modeling the assembly of transmembrane domains. This approach can be used to annotate the membrane proteins in proteomes to provide extra structural and functional information.

Cell Membrane↗
PubMed2006

Conservation of intrinsic disorder in protein domains and families: I. A database of conserved predicted disordered regions.

Many protein regions have been shown to be intrinsically disordered, lacking unique structure under physiological conditions. These intrinsically disordered regions are not only very common in proteomes, but also crucial to the function of many proteins, especially those involved in signaling, recognition, and regulation. The goal of this work was to identify the prevalence, characteristics, and functions of conserved disordered regions within protein domains and families. A database was created to store the amino acid sequences of nearly one million proteins and their domain matches from the InterPro database, a resource integrating eight different protein family and domain databases. Disorder prediction was performed on these protein sequences. Regions of sequence corresponding to domains were aligned using a multiple sequence alignment tool. From this initial information, regions of conserved predicted disorder were found within the domains. The methodology for this search consisted of finding regions of consecutive positions in the multiple sequence alignments in which a 90% or more of the sequences were predicted to be disordered. This procedure was constrained to find such regions of conserved disorder prediction that were at least 20 amino acids in length. The results of this work included 3,653 regions of conserved disorder prediction, found within 2,898 distinct InterPro entries. Most regions of conserved predicted disorder detected were short, with less than 10% of those found exceeding 30 residues in length.

Amino Acid Sequence↗
PubMed2006

A soluble 3D LC/MS/MS proteome of the filamentous cyanobacterium Nostoc punctiforme.

Nostoc punctiforme is an oxygenic photoautotrophic cyanobacterium with multiple developmental states, which can form nitrogen-fixing symbioses with a variety of terrestrial plants. 3D LC/MS/MS shotgun peptide sequencing was used to analyze the proteome when N. punctiforme is grown in continuous moderate light with ammonia as the nitrogen source. The soluble proteome includes 1575 proteins, 50% of which can be assigned to core metabolic and transport functions. Another 39% are assigned to proteins with no known function, a substantially higher fraction than in the Escherichia coli proteome. Many expressed proteins protect against oxidative and light stress. Seventy-one sensor histidine kinases, response regulators, and serine/threonine kinases, individually and as hybrid, multidomain proteins, were identified, reflecting a substantial capacity to sense and respond to environmental change. Proteins encoded by each of the five N. punctiforme plasmids were identified, as were 10 transposases, reflecting the plasticity of the N. punctiforme genome. This core proteome sets the stage for comparison with that of other developmental states.

Bacterial Proteins↗
PubMed2004

Intracellular retention of caveolin 1 in presenilin-deficient cells.

Mutations in genes encoding presenilins (PS1 and PS2) are responsible for the majority of early onset familial Alzheimer's disease. PS, a critical component of gamma-secretase, is responsible for the intramembranous cleavage of amyloid precursor protein and Notch. Other physiological functions have been assigned to PS without any clear identification of the mechanisms underlying these multiple biological roles. The early embryonic lethality of PS1 and PS2 double knock-out (PS1/2 null) mice prevents the evaluation of physiological roles of PS. To investigate new functions for presenilins, we performed a proteomic approach by using cells derived from PS1/2 null blastocysts and wild type controls. We identified a presenilin-dependent cell-surface binding of albumin. Binding of albumin depends on intact caveolae on the cellular surface. Abnormal caveolin 1 localization in PS1/2 null cells was associated with a loss of caveolae and an absence of caveolin 1 expression within lipid rafts. Expressing PS1 or PS2 but not the intracellular form of Notch1 in PS1/2 null cells restored normal caveolin 1 localization, demonstrating that presenilins are required for the subcellular trafficking of caveolin 1 independently from Notch activity. Despite an expression of both caveolin 1 and PS1 within lipid raft-enriched fractions after sucrose density centrifugation in wild type cells, no direct interaction between these two proteins was detected, implying that presenilins affect caveolin 1 trafficking in an indirect manner. We conclude that presenilins are required for caveolae formation by controlling transport of intracellular caveolin 1 to the plasma membrane.

Animals↗
PubMed2006

Trypanosoma seryl-tRNA synthetase is a metazoan-like enzyme with high affinity for tRNASec.

Trypanosomatids are important human pathogens that form a basal branch of eukaryotes. Their evolutionary history is still unclear as are many aspects of their molecular biology. Here we characterize essential components required for the incorporation of serine and selenocysteine into the proteome of Trypanosoma. First, the biological function of a putative Trypanosoma seryl-tRNA synthetase was characterized in vivo. Secondly, the molecular recognition by Trypanosoma seryl-tRNA synthetase of its cognate tRNAs was dissected in vitro. The cellular distribution of tRNA(Sec) was studied, and the catalytic constants of its aminoacylation were determined. These were found to be markedly different from those reported in other organisms, indicating that this reaction is particularly efficient in trypanosomatids. Our functional data were analyzed in the context of a new phylogenetic analysis of eukaryotic seryl-tRNA synthetases that includes Trypanosoma and Leishmania sequences. Our results show that trypanosomatid seryl-tRNA synthetases are functionally and evolutionarily more closely related to their metazoan homologous enzymes than to other eukaryotic enzymes. This conclusion is supported by sequence synapomorphies that clearly connect metazoan and trypanosomatid seryl-tRNA synthetases.

Acylation↗
PubMed2003

An ENSEMBLE machine learning approach for the prediction of all-alpha membrane proteins.

MOTIVATION: All-alpha membrane proteins constitute a functionally relevant subset of the whole proteome. Their content ranges from about 10 to 30% of the cell proteins, based on sequence comparison and specific predictive methods. Due to the paucity of membrane proteins solved with atomic resolution, the training/testing sets of predictive methods for protein topography and topology routinely include very few well-solved structures mixed with a hundred proteins known with low resolution. Moreover, available predictors fail in predicting recently crystallised membrane proteins (Chen et al., 2002). Presently the number of well-solved membrane proteins comprises some 59 chains of low sequence homology. It is therefore possible to train/test predictors only with the set of proteins known with atomic resolution and evaluate more thoroughly the performance of different methods. RESULTS: We implement a cascade-neural network (NN), two different hidden Markov models (HMM), and their ensemble (ENSEMBLE) as a new method. We train and test in cross validation the three methods and ENSEMBLE on the 59 well resolved membrane proteins. ENSEMBLE scores with a per-protein accuracy of 90% for topography and 71% for topology, outperforming the best single method of 7 and 5 percentage points, respectively. When tested on a low resolution set of 151 proteins, with no homology with the 59 proteins, the per-protein accuracy of ENSEMBLE is 76% for topography and 68% for topology. Our results also indicate that the performance of ENSEMBLE is higher than that of the best predictors presently available on the Web.

Algorithms↗
PubMed2005

Accurate extraction of functional associations between proteins based on common interaction partners and common domains.

MOTIVATION: Genomic and proteomic approaches have accumulated a huge amount of data which provide clues to protein function. However, interpreting single omic data for predicting uncharacterized protein functions has been a challenging task, because the data contain a lot of false positives. To overcome this problem, methods for integrating data from various omic approaches are needed for more accurate function prediction. RESULT: In this paper, we have developed a method which extracts functionally similar proteins with high confidence by integrating protein-protein interaction data and domain information. We used this method to analyze publicly available data from Saccharomyces cerevisiae. We identified 1042 functional associations, involving 765 proteins of which 98 (12.8%) had no previously ascribed function. Our method extracts functionally similar protein pairs more accurately than conventional methods, and predicting function for previously uncharacterized proteins can be achieved. Our method can of course be applied to protein-protein interaction data for any species.

Algorithms↗
PubMed2006

Inferring global levels of alternative splicing isoforms using a generative model of microarray data.

MOTIVATION: Alternative splicing (AS) is a frequent step in metozoan gene expression whereby the exons of genes are spliced in different combinations to generate multiple isoforms of mature mRNA. AS functions to enrich an organism's proteomic complexity and regulates gene expression. Despite its importance, the mechanisms underlying AS and its regulation are not well understood, especially in the context of global gene expression patterns. We present here an algorithm referred to as the Generative model for the Alternative Splicing Array Platform (GenASAP) that can predict the levels of AS for thousands of exon skipping events using data generated from custom microarrays. GenASAP uses Bayesian learning in an unsupervised probability model to accurately predict AS levels from the microarray data. GenASAP is capable of learning the hybridization profiles of microarray data, while modeling noise processes and missing or aberrant data. GenASAP has been successfully applied to the global discovery and analysis of AS in mammalian cells and tissues. RESULTS: GenASAP was applied to data obtained from a custom microarray designed for the monitoring of 3126 AS events in mouse cells and tissues. The microarray design included probes specific for exon body and junction sequences formed by the splicing of exons. Our results show that GenASAP provides accurate predictions for over one-third of the total events, as verified by independent RT-PCR assays. SUPPLEMENTARY INFORMATION: http://www.psi.toronto.edu/GenASAP.

Algorithms↗
PubMed2000

Molecular biology and neurosurgery in the third millennium.

The application of techniques in molecular biology to human neurosurgical conditions has led to an increased understanding of disease processes that affect the brain and to novel forms of therapy that favorably modify the natural history of many of these conditions. Molecular strategies are currently being either used or sought for brain tumors, stroke, neurodegenerative diseases, vascular malformations, spinal degenerative diseases, and congenital malformations of the central nervous system. Considering that the structure of deoxyribonucleic acid was ascertained by Watson and Crick as recently as 1953, the progress that has been made to implement molecular medicine in clinical practice has been meteoric. More than 2000 patients have been treated in approved gene therapy trials throughout the world. Many of these patients have been treated for neurological diseases for which conventional medical therapies have been of limited utility. As part of this continuing series on advances in neurosurgery in the third millennium, we first reflect on the history of the nascent field of molecular biology. We then describe the powerful techniques that have evolved from knowledge in this field and have been used in many publications in Neurosurgery, particularly within the past decade. These methods include commonly used techniques such as advanced cytogenetics, differential display, microarray technology, molecular cell imaging, yeast two-hybrid assays, gene therapy, and stem cell utilization. We conclude with a description of the rapidly growing field of bioinformatics. Because the Human Genome Project will be completed within 5 years, providing a virtual blueprint of the human race, the next frontier (and perhaps our greatest challenge) will involve the development of the field of "proteomics," in which protein structure and function are determined from the deoxyribonucleic acid blueprint. It is our conviction that neurosurgeons will continue to be at the forefront of the treatment of patients with neurological diseases using molecular strategies, by performing essential research leading to increased understanding of diseases, by conducting carefully controlled studies to test the effects of treatments on disease processes, and by directly administering (by neurosurgical, endovascular, endoscopic, or stereotactic means) the treatments to patients.

Cytogenetics↗
PubMed2005

Lotus japonicus metabolic profiling. Development of gas chromatography-mass spectrometry resources for the study of plant-microbe interactions.

Symbiotic nitrogen fixation (SNF) in legume root nodules requires differentiation and integration of both plant and bacterial metabolism. Classical approaches of biochemistry, molecular biology, and genetics have revealed many aspects of primary metabolism in legume nodules that underpin SNF. Functional genomics approaches, especially transcriptomics and proteomics, are beginning to provide a more holistic picture of the metabolic potential of nodules in model legumes like Medicago truncatula and Lotus japonicus. To extend these approaches, we have established protocols for nonbiased measurement and analysis of hundreds of metabolites from L. japonicus, using gas chromatography coupled with mass spectrometry. Following creation of mass spectral tag libraries, which represent both known and unknown metabolites, we measured and compared relative metabolite levels in nodules, roots, leaves, and flowers of symbiotic plants. Principal component analysis of the data revealed distinct metabolic phenotypes for the different organs and led to the identification of marker metabolites for each. Metabolites that were enriched in nodules included: octadecanoic acid, asparagine, glutamate, homoserine, cysteine, putrescine, mannitol, threonic acid, gluconic acid, glyceric acid-3-P, and glycerol-3-P. Hierarchical cluster analysis enabled discrimination of 10 groups of metabolites, based on distribution patterns in diverse Lotus organs. The resources and tools described here, together with ongoing efforts in the areas of genome sequencing, and transcriptome and proteome analysis of L. japonicus and Mesorhizobium loti, should lead to a better understanding of nodule metabolism that underpins SNF.

Gas Chromatography-Mass Spectrometry↗
PubMed2025

Fishing for a reelGene: evaluating gene models with evolution and machine learning.

Assembled genomes and their associated annotations have transformed our study of gene function. However, each new annotated assembly generates new gene models. Inconsistencies between annotations likely arise from biological and technical causes, including pseudogene misclassification, transposon activity, and intron retention from sequencing of unspliced transcripts. To evaluate gene model predictions, we developed reelGene, a pipeline of machine learning models focused on (1) transcription boundaries, (2) mRNA integrity, and (3) protein structure. The first two models leverage sequence characteristics and evolutionary conservation across related taxa to learn the grammar of conserved transcription boundaries and mRNA sequences, while the third uses the conserved evolutionary grammar of protein sequences to predict whether a gene can produce a protein. Evaluating 1.8 million transcript models in Zea mays ssp. mays (maize), reelGene classified 28% as incorrectly annotated or non-functional. We find that reelGene classifies 92.2% of genes in the maize proteome and 99.2% of genes within the maize classical gene list as functional. reelGene also provides a way to further investigate genome biology- for instance, reelGene indicates that 10.3% of dispensable genes in B73 are functional, and within retained duplicate genes, reelGene identifies a 30% bias toward the retention of the M1 subgenome when one copy is functional and the other is non-functional. As an annotation-evaluating tool, reelGene is directly applicable to species of the Andropogoneae tribe, including other important crops like sorghum and miscanthus. As a community resource, reelGene has been integrated onto MaizeGDB both as a browser track and as an individual Shiny App, allowing researchers to evaluate gene model accuracy and further investigate genome biology.

Machine Learning↗
PubMed2006

Virion-associated restriction endonucleases of chloroviruses.

Chloroviruses are large, double-stranded-DNA, plaque-forming viruses that infect certain eukaryotic chlorella-like green algae. The prototype of the genus is Paramecium bursaria chlorella virus 1 (PBCV-1). Chlorovirus genomes contain various amounts of methylated nucleotides due to virus-encoded DNA methyltransferases (MTases); about 25% of the MTases are associated with companion DNA site-specific (restriction) endonucleases (REases). These enzymes constitute virally encoded restriction-modification (R/M) systems. Although several of the chlorovirus R/M systems are characterized, their biological functions are unknown. The PBCV-1 proteome reveals that two virus-encoded REases, but not their companion MTases, are virion associated, suggesting that viral REases might help degrade the host DNA early in infection. To test this hypothesis, host chromosomal DNA from PBCV-1-infected cells was examined by pulsed-field gel electrophoresis. Initiation of host chromosomal DNA degradation occurred within 5 min postinfection (p.i.). The DNA degradation was insensitive to protein synthesis inhibitors or UV inactivation of virus particles, consistent with the agent being a small protein associated with the virion. Nuclease activities, including those of the two predicted REases and an uncharacterized general nuclease(s), were detected in disrupted PBCV-1 particles. The general nuclease(s) degraded both host and viral DNAs in vitro, although the viral DNA was not degraded in vivo, suggesting differential intracellular trafficking of the virion-associated nucleases. Infection with chloroviruses lacking an R/M system(s) resulted in either delayed host chromosomal DNA degradation or no detectable host chromatin changes. These immediate-early events associated with chlorovirus infections may facilitate rapid switching of the host transcriptional apparatus to viral transcription, which begins within 5 to 10 min p.i.

Animals↗
PubMed2004

Recent advances in molecular diagnosis and therapy of gastric cancer.

Gastric cancer is the fourth most common malignancy and the second most frequent cause of cancer-related death in the world. It is often diagnosed in advanced stages when treatment options are limited, leading to a poor prognosis. During the past 15 years, much has been learnt about the molecular mechanisms of gastric carcinogenesis and the development of metastases. This coincided with the development of new techniques for functional genomics, including both transcriptomics and proteomics, which significantly improve the ability to explore new molecular alterations involved in carcinogenesis and tumor progression. An improved understanding of the molecular pathology and pathogenesis of gastric cancer may lead to a more rapid development of molecular diagnostic and patient tailored therapeutic targets.

Gene Transfer Techniques↗
PubMed2006

Identification of regulators of germ layer morphogenesis using proteomics in zebrafish.

During vertebrate gastrulation, a well-orchestrated series of morphogenetic changes leads to the formation of the three germ layers: the ectoderm, mesoderm and endoderm. The analysis of gene expression patterns during gastrulation has been central to the identification of genes involved in germ layer formation. However, many proteins are regulated on a translational or post-translational level and are thus undetectable by gene expression analysis. Therefore, we developed a 2D-gel-based comparative proteomic approach to target proteins involved in germ layer morphogenesis during zebrafish gastrulation. Proteomes of ectodermal and mesendodermal progenitor cells were compared and 35 significantly regulated proteins were identified by mass spectrometry, including several proteins with predicted functions in cytoskeletal organization. A comparison of our proteomic results with data obtained in an accompanying microarray-based gene expression analysis revealed no significant overlap, confirming the complementary nature of proteomics and transcriptomics. The regulation of ezrin2, which was identified based on a reduction in spot intensity in mesendodermal cells, was independently validated. Furthermore, we show that ezrin2 is activated by phosphorylation in mesendodermal cells and is required for proper germ layer morphogenesis. We demonstrate the feasibility of proteomics in zebrafish, concluding that proteomics is a valuable tool for analysis of early development.

Animals↗