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Results for “Integrated proteomics”

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At least 721 records · Page 40Linked to original sources

PubMed2003

ToxoDB: accessing the Toxoplasma gondii genome.

ToxoDB (http://ToxoDB.org) provides a genome resource for the protozoan parasite Toxoplasma gondii. Several sequencing projects devoted to T. gondii have been completed or are in progress: an EST project (http://genome.wustl.edu/est/index.php?toxoplasma=1), a BAC clone end-sequencing project (http://www.sanger.ac.uk/Projects/T_gondii/) and an 8X random shotgun genomic sequencing project (http://www.tigr.org/tdb/e2k1/tga1/). ToxoDB was designed to provide a central point of access for all available T. gondii data, and a variety of data mining tools useful for the analysis of unfinished, un-annotated draft sequence during the early phases of the genome project. In later stages, as more and different types of data become available (microarray, proteomic, SNP, QTL, etc.) the database will provide an integrated data analysis platform facilitating user-defined queries across the different data types.

Animals↗

PubMed2006

Protein complex compositions predicted by structural similarity.

Proteins function through interactions with other molecules. Thus, the network of physical interactions among proteins is of great interest to both experimental and computational biologists. Here we present structure-based predictions of 3387 binary and 1234 higher order protein complexes in Saccharomyces cerevisiae involving 924 and 195 proteins, respectively. To generate candidate complexes, comparative models of individual proteins were built and combined together using complexes of known structure as templates. These candidate complexes were then assessed using a statistical potential, derived from binary domain interfaces in PIBASE (http://salilab.org/pibase). The statistical potential discriminated a benchmark set of 100 interface structures from a set of sequence-randomized negative examples with a false positive rate of 3% and a true positive rate of 97%. Moreover, the predicted complexes were also filtered using functional annotation and sub-cellular localization data. The ability of the method to select the correct binding mode among alternates is demonstrated for three camelid VHH domain-porcine alpha-amylase interactions. We also highlight the prediction of co-complexed domain superfamilies that are not present in template complexes. Through integration with MODBASE, the application of the method to proteomes that are less well characterized than that of S.cerevisiae will contribute to expansion of the structural and functional coverage of protein interaction space. The predicted complexes are deposited in MODBASE (http://salilab.org/modbase).

Algorithms↗

PubMed2026

FANTASIA suite: a reproducible and configurable framework for embedding-based functional annotation of proteins.

Embedding-based annotation transfer is increasingly used for protein function inference due to protein language models capture sequence, structural, and functional signals that may extend beyond conventional pairwise similarity. However, systematic application of these approaches requires control over model choice, reference composition, lookup parameters, evidence traceability, and output formats. We developed the FANTASIA suite, a configurable framework for embedding-based functional annotation of proteins. The suite combines a database-backed implementation for reproducible and extensible analyses with a portable flat-file implementation for rapid local annotation and pipeline integration. Using non-model and model-organism proteomes, we show that larger neighbourhood sizes remain practical for proteome-scale analyses and that taxonomy and sequence-identity filtering support leakage-aware benchmarking. We also compare the supported models with baseline methods through external CAFA5 evaluation and provide practical guidance based on empirical evidence variables. FANTASIA provides a controlled, scalable, and reproducible framework for extending functional annotation across the rapidly expanding diversity of sequenced organisms.

Software↗

PubMed2004

Thematic review series: The pathogenesis of atherosclerosis. Toward a biological network for atherosclerosis.

The goal of systems biology is to define all of the elements present in a given system and to create an interaction network between these components so that the behavior of the system, as a whole and in parts, can be explained under specified conditions. The elements constituting the network that influences the development of atherosclerosis could be genes, pathways, transcript levels, proteins, or physiologic traits. In this review, we discuss how the integration of genetics and technologies such as transcriptomics and proteomics, combined with mathematical modeling, may lead to an understanding of such networks.

Animals↗

PubMed2002

[What is bioinformatics?].

Yet bioinformatics has neither clear definition nor proper Japanese equivalent for this word. Bioinformatics is mainly composed of two fields or disciplines; one is computer and information technology as a tool for analyzing massive data such as genome and proteome, and the other one is research for the integrated biology based on the knowledge of genome. Classical and narrow-sense bioinformatics corresponds to the former. The latter called new bioinformatics aims to understand living thing as a whole, where both genome information and mathematical model play complimentary roles. From now the postgenome area on, this new bioinformatics may give novel perspective about living thing, biology and human.

Computational Biology↗

PubMed2006

Enrichment of integral membrane proteins from small amounts of brain tissue.

Subcellular fractionation represents an essential technique for functional proteome analysis. Recently, we provided a subcellular fractionation protocol for minute amounts of tissue that yielded a nuclear fraction, a membrane and organelle fraction, and a cytosolic fraction. In the current study, we attempted to improve the protocol for the isolation of integral membrane proteins, as these are particularly important for brain function. In the membrane and organelle fraction, we increased the yield of membranes and organelles by about 50% by introducing a single re-extraction step. We then tested two protocols towards their capacity to enrich membrane proteins present in the membrane and organelle fraction. One protocol is based on sequential solubilization using subsequent increases of chaotropic conditions such as urea, thereby partitioning hydrophobic proteins from hydrophilic ones. The alternative protocol applies high-salt and high-pH washes to remove non-membrane proteins. The enrichment of membrane proteins by these procedures, as compared to the original membrane and organelle fraction, was evaluated by 16-BAC-SDS-PAGE followed by mass spectrometry of randomly selected spots. In the original membrane and organelle fraction, 7 of 50 (14%) identified proteins represented integral membrane proteins, and 15 (30%) were peripheral membrane proteins. In the urea-soluble fraction, 4 of 33 (12%) identified proteins represented integral membrane proteins, and 10 (30%) were peripheral membrane proteins. In the high-salt/high-pH resistant sediment, 12 of 45 (27%) identified proteins were integral membrane proteins and 13 (29%) represented peripheral membrane proteins. During the analysis, several proteins involved in neuroexocytosis were detected, including syntaxin, NSF, and Rab3-interaction protein 2. Taken together, differential centrifugation in combination with high-salt and high-pH washes resulted in the highest enrichment of integral membrane proteins and, therefore, represents an adequate technique for region-specific profiling of membrane proteins in the brain.

Animals↗

PubMed2005

Integrating cell-level kinetic modeling into the design of engineered protein therapeutics.

Functional genomics and proteomics are identifying many potential drug targets for novel therapeutic proteins, and both rational and combinatorial protein engineering methods are available for creating drug candidates. A central challenge is the definition of the most appropriate design criteria, which will benefit critically from computational kinetic models that incorporate integration from the molecular level to the whole systems level. Interpretation of these processes will require mathematical models that are refined in combination with relevant data derived from quantitative assays, to correctly set biophysical objectives for protein design.

Cell Physiological Phenomena↗

PubMedDate not supplied

[Methods for transcriptome and proteome research: applications for studying the biology of reproduction in cattle].

Improvements of animal health, welfare and product quality are major goals of modern animal breeding. Thus, in addition to the classical production traits, functional traits such as disease resistance, fertility and longevity moved into the center of animal breeder's interests. Due to their low heritability, the improvement of functional traits using conventional approaches of phenotypic testing and quantitative genetics is difficult. A number of studies have been conducted worldwide in various species to map quantitative trait loci (QTLs) and to identify genetic markers for health traits. This has revealed a plethora of chromosome regions which may harbor genes with relevance for animal health. Functional genome research integrates holistic investigations at the level of the genome, at the level of gene activity (transcriptome, proteome) and at various levels of phenotypic expression. The integration of all these levels of information provides the basis for the functional dissection of complex traits. This review provides an overview of the most important strategies for holistic transcriptome and proteome analyses. The successful application of these techniques is exemplified by our studies of bovine reproductive biology.

Animals↗

PubMed2003

The Protein Information Resource.

The Protein Information Resource (PIR) is an integrated public resource of protein informatics that supports genomic and proteomic research and scientific discovery. PIR maintains the Protein Sequence Database (PSD), an annotated protein database containing over 283 000 sequences covering the entire taxonomic range. Family classification is used for sensitive identification, consistent annotation, and detection of annotation errors. The superfamily curation defines signature domain architecture and categorizes memberships to improve automated classification. To increase the amount of experimental annotation, the PIR has developed a bibliography system for literature searching, mapping, and user submission, and has conducted retrospective attribution of citations for experimental features. PIR also maintains NREF, a non-redundant reference database, and iProClass, an integrated database of protein family, function, and structure information. PIR-NREF provides a timely and comprehensive collection of protein sequences, currently consisting of more than 1 000 000 entries from PIR-PSD, SWISS-PROT, TrEMBL, RefSeq, GenPept, and PDB. The PIR web site (http://pir.georgetown.edu) connects data analysis tools to underlying databases for information retrieval and knowledge discovery, with functionalities for interactive queries, combinations of sequence and text searches, and sorting and visual exploration of search results. The FTP site provides free download for PSD and NREF biweekly releases and auxiliary databases and files.

Amino Acid Sequence↗

PubMed2003

Expanding the organismal scope of proteomics: cross-species protein identification by mass spectrometry and its implications.

Due to the limited applicability of conventional protein identification methods to the proteomes of organisms with unsequenced genomes, researchers have developed approaches to identify proteins using mass spectrometry and sequence similarity database searches. Both the integration of mass spectrometry with bioinformatics and genomic sequencing drive the expanding organismal scope of proteomics.

Amino Acid Sequence↗

PubMed2006

Integration of genomic and metabonomic data in systems biology--are we 'there' yet?

The measurement of genes, proteins and metabolites has gained increasing acceptance as a means by which to study the response of an organism to stimuli, whether they are environmental, genetic, pharmacological, toxicological, etc. Typically referred to as genomics, proteomics, and metabonomics or metabolomics, respectively, these methods as independent entities have undoubtedly provided new biological insight that was not attainable a decade ago. Not surprisingly, scientists continue to push the boundaries to extract knowledge from data, and it is currently recognized that the full realization of these technologies is limited by a lack of tools to enable data integration. Integration of these 'omic datasets, or integromics, is desirable as it links the individual biological elements together to provide a more complete understanding of dynamic biological processes. Accordingly, in addition to developing new data analysis methods to extract further details from each of the high-content datasets individually, effort is also being expended to create or improve statistical methods, databases, annotations and pathway mapping to maximize our learning. There are several recent examples, in both mammalian and non-mammalian systems, in which genes, proteins and/or metabolites have been integrated using either biology- or data-driven strategies. Herein, key findings are reviewed, gaps in our current tools and technologies are identified and illustrated, and perspective is provided on the potential of integromics in biological research.

Acetaminophen↗

PubMed2004

Only a small subset of the horizontally transferred chromosomal genes in Escherichia coli are translated into proteins.

Horizontally transferred genes are believed to play a critical role in the divergence of bacterial strains from a common ancestor, but whether all of these genes express functional proteins in the cell remains unknown. Here, we used an integrated LC-based protein identification technology to analyze the proteome of Escherichia coli strain K12 (JM109) and identified 1,480 expressed proteins, which are equivalent to approximately 35% of the total open reading frames predicted in the genome. This subset contained proteins with cellular abundance of several dozens to hundreds of thousands of copies, and included nearly all types of proteins in terms of chemical characteristics, subcellular distribution, and function. Interestingly, the subset also contained 138 of 164 gene products that are currently known to be essential for bacterial viability (84% coverage). However, the subset contained only a very small population (10%) of protein products from genes mapped within K-loops, which are "hot spots" for the integration of foreign DNAs within the K12 genome. On the other hand, these genes in K-loops appeared to be transcribed to RNAs almost as efficiently as the native genes in the bacterial cell as monitored by DNA microarray analysis, raising the possibility that most of the recently acquired foreign genes are inadequate for the translational machinery for the native genes and do not generate functional proteins within the cell.

Bacterial Proteins↗

PubMed2006

BioNetBuilder: automatic integration of biological networks.

UNLABELLED: BioNetBuilder is an open-source client-server Cytoscape plugin that offers a user-friendly interface to create biological networks integrated from several databases. Users can create networks for approximately 1500 organisms, including common model organisms and human. Currently supported databases include: DIP, BIND, Prolinks, KEGG, HPRD, The BioGrid and GO, among others. The BioNetBuilder plugin client is available as a Java Webstart, providing a platform-independent network interface to these public databases. AVAILABILITY: http://err.bio.nyu.edu/cytoscape/bionetbuilder/

Algorithms↗

PubMed2026

Multi-omics profiling of cerebrospinal fluid in autoimmune encephalitis: insights into pathogenesis and therapeutic targets.

BACKGROUND: Autoimmune encephalitis (AIE) is a rare, severe inflammatory brain disease, with its pathogenesis not yet fully elucidated. This study aimed to characterize proteomic and metabolomic alterations in the cerebrospinal fluid (CSF) of AIE patients and identify potential therapeutic targets. METHODS: 65 consecutive AIE patients and age-matched concurrent controls were enrolled, respectively. Clinical characteristics, including blood and CSF laboratory findings, were compared between the two groups, and CSF samples were collected for multi-omics analysis. Differentially expressed proteins (DEPs) and metabolites (DEMs) between AIE patients and controls were identified using data-independent acquisition-based proteomics and targeted liquid chromatography-mass spectrometry-based metabolomics, followed by integrated multi-omics analysis. RESULTS: Compared with controls, AIE patients had lower levels of triglyceride and C1q, but higher HDL-CH levels, neutrophil counts, and eosinophil counts in blood. CSF leukocyte, erythrocyte, lymphocyte, and mononuclear cell counts were also elevated in AIE patients. Proteomic analysis identified 163 DEPs, with enrichment of 87 canonical pathways primarily associated with immune-inflammatory responses, neuronal-synaptic dysfunction, and cell signaling and metabolic pathways. Metabolomic analysis recognized 21 DEMs, predominantly amino acids, lipids, and carbohydrates, which were involved in lipid-carbohydrate metabolism and immune regulation. Integrated multi-omics analysis validated these findings and identified several potential therapeutic targets for AIE, including the IL6-STAT3 axis. CONCLUSIONS: Integrated multi-omics analysis systematically delineates cellular and molecular alterations underlying AIE. Immune-inflammatory response and lipid metabolism are pivotal in AIE progression and the IL6-STAT3 axis holds promise as a potential therapeutic target.

Humans↗

PubMed2001

Identification of putative exported/secreted proteins in prokaryotic proteomes.

The increasing number of bacterial genomes being sequenced fuels an equal demand for methods to rapidly analyze the proteomes of these organisms. One group of proteins of pressing importance is the exported/secreted proteins, given their dominant immunogenicity and role in pathogenesis. With this in mind, a weight matrix algorithm and two artificial neural networks, one based on amino acid position within the N-terminus and the other on amino acid frequency, were developed for identification of such proteins. The neural networks and a hybrid method, combining the weight matrix algorithm and the amino acid frequency neural network, were tested independently against a standard data set of secreted and cytoplasmic proteins to determine their accuracy in predicting secreted prokaryotic proteins. The results of these analyses demonstrated that the amino acid position neural network provided the highest accuracy (Mathews correlation coefficient of 0.93) in predicting secreted proteins of Gram-negative bacteria, whereas the hybrid method was best (Mathews correlation coefficient of 0.97) for prediction of Gram-positive secreted proteins. These two methods were integrated into a single program (ExProt) designed to analyze whole proteomes. In addition to protein localization, ExProt also contains a neural network trained to identify the most probable signal peptidase I cleavage site of secreted proteins. When tested against the standard protein data set ExProt correctly predicted 73.5 and 84.5% of the cleavage sites in Gram-positive and Gram-negative secreted proteins, respectively. Comparative analysis of Gram-negative, Gram-positive, Mycobacterium tuberculosis, and Archaea proteomes with ExProt revealed that the fraction of putative exported/secreted proteins encoded by bacterial genomes ranged from 8% for Methanococcus jannaschii to 37% for Mycoplasma pneumoniae.

Algorithms↗

PubMed2005

Genomic medicine: genetic variation and its impact on the future of health care.

Advances in genome technology and other fruits of the Human Genome Project are playing a growing role in the delivery of health care. With the development of new technologies and opportunities for large-scale analysis of the genome, transcriptome, proteome and metabolome, the genome sciences are poised to have a profound impact on clinical medicine. Cancer prognostics will be among the first major test cases for a genomic medicine paradigm, given that all cancer is caused by genomic instability, and microarrays allow assessment of patients' entire expressed genomes. Analysis of breast cancer patients' expression patterns can already be highly correlated with recurrence risks. By integrating clinical data with gene expression profiles, imaging, metabolomic profiles and proteomic data, the prospect for developing truly individualized care becomes ever more real. Notwithstanding these promises, daunting challenges remain for genomic medicine. Success will require planning robust prospective trials, analysing health care economic and outcome data, assuaging insurance and privacy concerns, developing health delivery models that are commercially viable and scaling up to meet the needs of the whole population.

Delivery of Health Care↗

PubMed2002

Advances in the development of genetic markers for the diagnosis of disease and drug response.

Genetic diversity, including single nucleotide polymorphisms, contributes to both disease susceptibility and variability in drug response. Since most genes contain multiple single nucleotide polymorphisms, identifying those that are most relevant with respect to disease or drug response is important and may uncover variants that are predictive of either disease susceptibility or therapeutic response to drugs, both with respect to efficacy and toxic side effects. The candidate gene approach has been widely used to search for the genetic basis of pharmacogenomic traits. Although a few successful examples have emerged from this approach, notably trastuzumab (Herceptin; Genentech), imatinib mesylate (Gleevec (USA), Glivec; Novartis) and certain drugs that demonstrate variable efficacy or adverse effects that are attributed to metabolizing enzymes, for most drugs, the genetic variations that determine their clinical response remain uncovered. Genome-wide linkage approach presents an alternative to the candidate gene approach. The powerful combination of linkage when coupled to ultra-high-throughput genotyping, gene array and proteomics technology, together with innovative bioinformatic resources, provides a focused integrative strategy for pinpointing disease-causing genes that may generate validated drug targets and genes that are responsible for differential drug response. Thus, it is anticipated that genetic research will soon generate new information that can be used to develop novel therapeutic strategies and diagnostic tests that will ultimately lead to safer and more efficacious drugs for all patients. This review addresses recent advances in the development of genetic markers that can be used to diagnose disease or drug response.

Cardiovascular Diseases↗

PubMed2004

Case study: data management strategies in an integrated pathway tool.

This paper describes the development strategies for an integrated tool to support scientists in the creative exploration of data relating to biochemical pathways. The multiple user groups, diverse functionalities, and many types and sources of data demanded a flexible yet coherent approach. This paper summarises the software requirements and the implied modules and functions, and focuses on the design decisions relevant to the representation, management and flow of data. Finally, several case studies in the use of the software are described and evaluated, and recommendations are made for future work.

Animals↗