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PubMedDate not supplied

On graphical and numerical characterization of proteomics maps.

We outlined a mathematical approach suitable for characterization of experimental data given by 2-D densitograms. In particular we consider numerical characterization of proteomics maps. The basis of our approach is to order "spots" of a 2-D map and assign them unique labels (that in general will depend on the criteria used for ordering). In this way a map is "translated" into a sequence. In the next step one associates with the generated sequence a geometrical path and views such a path as a mathematical object that needs characterization. We have ordered spots representing proteins in 2-D gel plates according to their relative intensities which results in a zigzag path that produces a complicated "fingerprint" pattern. Mathematical characterization of zigzag pattern follows similar mathematical characterizations of embedded patterns based on matrices, the elements of which are given as quotients of Euclidean distance between spots and the distance along the zigzag path. The leading eigenvalue of constructed matrices is taken to represent characterization of the original 2-D map. Comparison of different 2-D maps (simulated by using random generator) allows one to construct partial order, which although qualitative in nature gives some insight into perturbation induced by foreign agents to the proteome of the control cell.

Computer Graphics↗
PubMedDate not supplied

Rapid subpicogram protein detection on a microchip without denaturing.

Protein size separation based on sodium dodecyl sulfate-gel electrophoresis (SDS-GE) requires denaturing, but we propose that denaturing is unnecessary for analysis by microchip electrophoresis (micro-CE). By omitting the protein denaturing process, we achieved not only shortened total analysis time, but also dramatically improved sensitivity without compromising size determination. The detection limit was improved to 0.1 ng/microL under conditions without denaturing and 600 pg (9.0 femtomol) of bovine serum albumin was detectable, which equals levels detectable by Silver stain, although a routine method by microchips in the Coomassie Blue detection level.

Databases, Protein↗
PubMed2006

Rapid validation of protein identifications with the borderline statistical confidence via de novo sequencing and MS BLAST searches.

Protein identifications with the borderline statistical confidence are typically produced by matching a few marginal quality MS/MS spectra to database peptide sequences and represent a significant bottleneck in the reliable and reproducible characterization of proteomes. Here, we present a method for rapid validation of borderline hits that circumvents the need in, often biased, manual inspection of raw MS/MS spectra. The approach takes advantage of the independent interpretation of corresponding MS/MS spectra by PepNovo de novo sequencing software followed by mass spectrometry-driven BLAST (MS BLAST) sequence-similarity database searches that utilize all partially inaccurate, degenerate and redundant candidate peptide sequences. In a case study involving the identification of more than 180 Caenorhabditis elegans proteins by nanoLC-MS/MS analysis on a linear ion trap LTQ mass spectrometer, the approach enabled rapid assignment (confirmation or rejection) of more than 70% of Mascot hits of borderline statistical confidence.

Amino Acid Sequence↗
PubMed2006

Mass spectrometry reveals specific and global molecular transformations during viral infection.

Mass spectrometry analysis was used to target three different aspects of the viral infection process: the expression kinetics of viral proteins, changes in the expression levels of cellular proteins, and the changes in cellular metabolites in response to viral infection. The combination of these methods represents a new, more comprehensive approach to the study of viral infection revealing the complexity of these events within the infected cell. The proteins associated with measles virus (MV) infection of human HeLa cells were measured using a label-free approach. On the other hand, the regulation of cellular and Flock House Virus (FHV) proteins in response to FHV infection of Drosophila cells was monitored using stable isotope labeling. Three complementary techniques were used to monitor changes in viral protein expression in the cell and host protein expression. A total of 1500 host proteins was identified and quantified, of which over 200 proteins were either up- or down-regulated in response to viral infection, such as the up-regulation of the Drosophila apoptotic croquemort protein, and the down-regulation of proteins that inhibited cell death. These analyses also demonstrated the up-regulation of viral proteins functioning in replication, inhibition of RNA interference, viral assembly, and RNA encapsidation. Over 1000 unique metabolites were also observed with significant changes in over 30, such as the down-regulated cellular phospholipids possibly reflecting the initial events in cell death and viral release. Overall, the cellular transformation that occurs upon viral infection is a process involving hundreds of proteins and metabolites, many of which are structurally and functionally uncharacterized.

Animals↗
PubMed2006

The proteomic reactor: a microfluidic device for processing minute amounts of protein prior to mass spectrometry analysis.

Gel-free proteomics has emerged as a complement to conventional gel-based proteomics. Gel-free approaches focus on peptide or protein fractionation, but they do not address the efficiency of protein processing. We report the development of a microfluidic proteomic reactor that greatly simplifies the processing of complex proteomic samples by combining multiple proteomic steps. Rapid extraction and enrichment of proteins from complex proteomic samples or directly from cells are readily performed on the reactor. Furthermore, chemical and enzymatic treatments of proteins are performed in 50 nL effective volume, which results in an increased number of generated peptides. The products are compatible with mass spectrometry. We demonstrated that the proteomic reactor is at least 10 times more sensitive than current gel-free methodologies with one protein identified per 440 pg of protein lysate injected on the reactor. Furthermore, as little as 300 cells can be directly introduced on the proteomic reactor and analyzed by mass spectrometry.

Animals↗
PubMed2006

Large-scale predictions of gram-negative bacterial protein subcellular locations.

Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells. Therefore, developing an automated method for fast and reliable prediction of Gram-negative protein subcellular location will allow us to not only timely annotate gene products, but also screen candidates for drug discovery. However, protein subcellular location prediction is a very difficult problem, particularly when more location sites need to be involved and when unknown query proteins do not have significant homology to proteins of known subcellular locations. PSORT-B, a recently updated version of PSORT, widely used for predicting Gram-negative protein subcellular location, only covers five location sites. Also, the data set used to train PSORT-B contains many proteins with high degrees of sequence identity in a same location group and, hence, may bear a strong homology bias. To overcome these problems, a new predictor, called "Gneg-PLoc", is developed. Featured by fusing many basic classifiers each being trained with a stringent data set containing proteins with strictly less than 25% sequence identity to one another in a same location group, the new predictor can cover eight subcellular locations; that is, cytoplasm, extracellular space, fimbrium, flagellum, inner membrane, nucleoid, outer membrane, and periplasm. In comparison with PSORT-B, the new predictor not only covers more subcellular locations, but also yields remarkably higher success rates. Gneg-PLoc is available as a Web server at http://202.120.37.186/bioinf/Gneg. To support the demand of people working in the relevant areas, a downloadable file is provided at the same Web site to list the results identified by Gneg-PLoc for 49 907 Gram-negative protein entries in the Swiss-Prot database that have no subcellular location annotations or are annotated with uncertain terms. The large-scale results will be updated twice a year to cover the new entries of Gram-negative bacterial proteins and reflect the new development of Gneg-PLoc.

Bacterial Proteins↗
PubMed2006

Elevated expression of C10orf3 (chromosome 10 open reading frame 3) is involved in the growth of human colon tumor.

After analysing gene-expression profiles of colon cancers on a cDNA microarray containing cDNAs corresponding to 23 040 human genes, we focused on a gene annotated as C10orf3 (chromosome 10 open reading frame 3), whose expression was elevated in colorectal cancers (CRC) as well as in tumors arising in the stomach, lung, pancreas, and breast. The gene encodes a putative 464-amino-acid protein containing a domain known as AAA (ATPases associated with a variety of cellular activities). Western blot analysis using an antibody to the gene product confirmed that the protein was overexpressed in nine of the 15 clinical cancer tissues examined, compared to corresponding noncancerous epithelial cells. A subsequent proteomics analysis revealed that C10orf3 product associated with the product of tumor susceptibility gene 101 (TSG101), and that C10orf3 downregulated TSG101 in a post-transcriptional manner. Expression of short interfering RNA in cells derived from CRC caused significant decreases in C10orf3 expression and inhibited growth of the transfected cells, which was associated with increased apoptotic cells. These data suggest that elevated C10orf3 expression might play an essential role in the growth of cancer cells, and that suppression of C10orf3-mediated signal transduction may be a novel therapeutic strategy to a wide range of human tumors.

Cell Division↗
PubMed2005

Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: assessment in two blind tests.

Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energy-minimized with the ECEPP/3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of alpha and alpha+beta proteins [60-70 residues with C(alpha) rms deviation (rmsd) <6 A]. However, for alpha+beta proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two alpha and three alpha+beta, with sizes of 53-235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue alpha+beta protein from Thermotoga maritima), we predicted the complete, topologically correct structure with 7.3-A C(alpha) rmsd. So far this protein is the largest alpha+beta protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly alpha-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 A rmsd, except the 32 C-terminal residues, most of which form a beta-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction.

Amino Acid Sequence↗
PubMed2002

Increased frequency of cysteine, tyrosine, and phenylalanine residues since the last universal ancestor.

Analysis of extant proteomes has the potential of revealing how amino acid frequencies within proteins have evolved over biological time. Evidence is presented here that cysteine, tyrosine, and phenylalanine residues have substantially increased in frequency since the three primary lineages diverged more than three billion years ago. This inference was derived from a comparison of amino acid frequencies within conserved and non-conserved residues of a set of proteins dating to the last universal ancestor in the face of empirical knowledge of the relative mutability of these amino acids. The under-representation of these amino acids within last universal ancestor proteins relative to their modern descendants suggests their late introduction into the genetic code. Thus, it appears that extant ancient proteins contain evidence pertaining to early events in the formation of biological systems.

Amino Acid Sequence↗
PubMed2005

An immunoproteomic approach for identification of clinical biomarkers for monitoring disease: application to cystic fibrosis.

Circulating antibodies can be used to probe protein arrays of body fluids, prepared by two-dimensional gel electrophoresis, for antigenic biomarker detection. However, detected proteins, particularly low abundance antigens, often remain unidentifiable due to proteome complexity and limiting sample amounts. Using a novel enrichment approach exploiting patient antibodies for isolation of antigenic biomarkers, we demonstrate how immunoproteomic strategies can accelerate biomarker discovery. Application of this approach as a means of identifying biomarkers was demonstrated for cystic fibrosis (CF) lung disease by isolation and identification of inflammatory-associated autoantigens, including myeloperoxidase and calgranulin B from sputum of subjects with CF. The approach was also exploited for isolation of proteins expressed by the Pseudomonas aeruginosa strain PA01. Capture of PA01 antigens using circulating antibodies from CF subjects implicated in vivo expression of Pseudomonas proteins. All CF subjects screened, but not controls, were immunoreactive against immunocaptured Pseudomonas proteins, representing stress (GroES and ferric iron-binding protein HitA), immunosuppressive (thioredoxin), and alginate synthetase pathway (nucleoside-diphosphate kinase) proteins, implicating their clinical relevance as biomarkers of infection.

Adolescent↗
PubMed2005

Differential proteomic analysis of bronchoalveolar lavage fluid in asthmatics following segmental antigen challenge.

Allergic asthma is characterized by persistent airway inflammation and remodeling. Bronchoalveolar lavage conducted with fiberoptic bronchoscopy has been widely used for investigating the pathogenesis of asthma and other lung disorders. Identification of proteins in the bronchoalveolar lavage fluid (BALF) and their expression changes at different stages of asthma could provide further insights into the complex molecular mechanisms involved in this disease. In this report, we describe the first comprehensive differential proteomic analysis of BALF from both asthmatic patients and healthy subjects before and 24 h after segmental allergen challenge. Our proteomic analysis involves affinity depletion of six abundant BALF proteins, SDS-PAGE fractionation, protein in-gel digestion, and subsequent nano-LC-MS/MS analysis in conjunction with database searching for protein identification and semiquantitation. More than 1,500 distinct proteins were identified of which about 10% displayed significant up-regulation specific to the asthmatic patients after segmental allergen challenge. The differentially expressed proteins represent a wide spectrum of functional classes such as chemokines, cytokines, proteases, complement factors, acute phase proteins, monocyte-specific granule proteins, and local matrix proteins, etc. The majority of these protein expression changes are closely associated with many aspects of the pathophysiology of asthma, including inflammation, eosinophilia, airway remodeling, tissue damage and repair, mucus production, and plasma infiltration. Importantly a large portion of these proteins and their expression changes were identified for the first time from BALF, thus providing new insights for finding novel pathological mediators and biomarkers of asthma.

Adult↗
PubMed2005

Quantitative neuropeptidomics of microwave-irradiated mouse brain and pituitary.

In neuropeptidomics, the degradation of a small fraction of abundant proteins overwhelms the low signals from neuropeptides, and many neuropeptides cannot be detected by mass spectrometry without extensive purification. Protein degradation was prevented when mice were sacrificed with focused microwave irradiation, permitting the detection of hypothalamic neuropeptides by mass spectrometry. Here we report an alternative and very simple method utilizing an ordinary microwave oven to inhibit enzymatic degradation. We used this technique to identify brain and pituitary neuropeptides. Quantitative analysis using mass spectrometry in combination with stable isotopic labeling was performed to determine the effect of microwave irradiation on relative levels of neuropeptides and protein degradation fragments. Microwave irradiation greatly reduced the levels of degradation fragments of proteins. In contrast, neuropeptide levels were increased about 2-3 times in hypothalamus by the microwave irradiation but not increased in pituitary. In a second experiment, three brain regions (hypothalamus, hippocampus, and striatum) from microwave-irradiated mice were analyzed. Altogether 41 neuropeptides or fragments of secretory pathway proteins were identified after microwave treatment; some of these are novel. These peptides were derived from 15 proteins: proopiomelanocortin, proSAAS, proenkephalin, preprotachykinins A and B, provasopressin, prooxytocin, melanin-concentrating hormone, proneurotensin, chromogranins A and B, secretogranin II, prohormone convertases 1 and 2, and peptidyl amidating monooxygenase. Although some protein degradation fragments were still found after microwave irradiation, these appear to result from protein breakdown during the extraction and not to an enzymatic reaction during the postmortem period. Two of the protein fragments corresponded to novel protein forms: VAP-33 with a 7-residue N-terminal extension and beta tubulin with a glutathione on the Cys near the N terminus. In conclusion, microwave irradiation with an ordinary microwave oven effectively inhibits enzymatic postmortem protein degradation, increases the recovery of neuropeptides, and makes it possible to conduct neuropeptidomic studies with mouse brain tissues.

Animals↗
PubMed2006

Experimental standards for high-throughput proteomics.

Proteome analysis, utilizing high-throughput proteomics approaches, involves studying proteins that a whole organism (or specific tissue or cellular compartment) expresses under certain conditions. Intrinsic difficulties of these studies, as well as the enormous volumes of data they typically produce, make the proteome analysis and interpretation very difficult. As with any high-throughput approach, proteomics experiments should be carefully designed, analyzed, and verified. In addition to computational standards,experimental standards--simple and complex mixtures of known proteins--for high-throughput proteomics have to be developed and utilized. This article discusses such experimental standards and their implementations.

Animals↗
PubMed2006

Enhanced sequence coverage of proteins in human cerebrospinal fluid using multiple enzymatic digestion and linear ion trap LC-MS/MS.

The cerebrospinal fluid (CSF) provides a ready access into the health state of the central nervous system, and alterations in some CSF proteins have been documented in brain disease. However, the complete variety of proteins is not known and methods to identify protein components are still being developed. The goal of this study was to examine the sequence coverage obtained from human CSF digests produced with different proteases. Enzymatic digests of CSF proteins were obtained with arginine-C endopeptidase (ArgC), glutamic acid endopeptidase (GluC), chymotrypsin, trypsin and their combinations, and then examined using reverse phase chromatography and a Finnigan LTQ linear ion trap mass spectrometer. Peptide sequences were identified with BioWorks 3.1 and sequence coverage calculated for the 38 most confidently identified proteins. Trypsin and GluC yielded greater coverage than chymotrypsin, while ArgC had the least sequence coverage. Protein sequence coverage was affected only slightly over four orders of magnitude dynamic range of abundance. Combining the peptides derived from different proteases further increased the coverage. Maximal sequence coverage was achieved by combining digest results from both GluC and trypsin. These results have implications for future studies to identify CSF proteins and their post-translational modifications.

Amino Acid Sequence↗
PubMed2004

Exploring protein fold space by secondary structure prediction using data distribution method on Grid platform.

MOTIVATION: Since the newly developed Grid platform has been considered as a powerful tool to share resources in the Internet environment, it is of interest to demonstrate an efficient methodology to process massive biological data on the Grid environments at a low cost. This paper presents an efficient and economical method based on a Grid platform to predict secondary structures of all proteins in a given organism, which normally requires a long computation time through sequential execution, by means of processing a large amount of protein sequence data simultaneously. From the prediction results, a genome scale protein fold space can be pursued. RESULTS: Using the improved Grid platform, the secondary structure prediction on genomic scale and protein topology derived from the new scoring scheme for four different model proteomes was presented. This protein fold space was compared with structures from the Protein Data Bank, database and it showed similarly aligned distribution. Therefore, the fold space approach based on this new scoring scheme could be a guideline for predicting a folding family in a given organism.

Computing Methodologies↗
PubMed2004

A proteomic approach to identification of transmembrane proteins and membrane-anchored proteins of Arabidopsis thaliana by peptide sequencing.

A proteomic approach was developed for the identification of membrane-bound proteins of Arabidopsis thaliana. A subcellular fraction enriched in vacuolar membranes was prepared from 4-week-old plants and was washed with various agents to remove peripheral membrane proteins and contaminating soluble proteins. The remaining membrane-bound proteins were then subjected to proteomic analysis. Given that these proteins were resolved poorly by standard two-dimensional gel electrophoresis, we subjected them instead to SDS-polyacrylamide gel electrophoresis and to protein digestion within gel slices with lysylendopeptidase. The resulting peptides were separated by reverse-phase high-performance liquid chromatography and subjected to Edman sequencing. From the 163 peptide peaks analyzed, 69 peptide sequences were obtained, 64 of which were informative. The proteins corresponding to these peptide sequences were identified as belonging to 42 families, including two subfamilies, by comparison with the protein sequences predicted from annotation of the A. thaliana genome. A total of 34 proteins was identified definitively with protein-specific peptide sequences. Transmembrane proteins detected in the membrane fraction included transporters, channels, receptors, and unknown molecules, whereas the remaining proteins, categorized as membrane-anchored proteins, included small GTPases, GTPase binding proteins, heat shock protein 70-like proteins, ribosomal proteins, and unknown proteins. These membrane-anchored proteins are likely attached to membranes by hydrophobic anchor molecules or through tight association with other membrane-bound proteins. This proteomic approach has thus proved effective for the identification of membrane-bound proteins.

Amino Acid Sequence↗