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PubMed2006

MannDB - a microbial database of automated protein sequence analyses and evidence integration for protein characterization.

BACKGROUND: MannDB was created to meet a need for rapid, comprehensive automated protein sequence analyses to support selection of proteins suitable as targets for driving the development of reagents for pathogen or protein toxin detection. Because a large number of open-source tools were needed, it was necessary to produce a software system to scale the computations for whole-proteome analysis. Thus, we built a fully automated system for executing software tools and for storage, integration, and display of automated protein sequence analysis and annotation data. DESCRIPTION: MannDB is a relational database that organizes data resulting from fully automated, high-throughput protein-sequence analyses using open-source tools. Types of analyses provided include predictions of cleavage, chemical properties, classification, features, functional assignment, post-translational modifications, motifs, antigenicity, and secondary structure. Proteomes (lists of hypothetical and known proteins) are downloaded and parsed from Genbank and then inserted into MannDB, and annotations from SwissProt are downloaded when identifiers are found in the Genbank entry or when identical sequences are identified. Currently 36 open-source tools are run against MannDB protein sequences either on local systems or by means of batch submission to external servers. In addition, BLAST against protein entries in MvirDB, our database of microbial virulence factors, is performed. A web client browser enables viewing of computational results and downloaded annotations, and a query tool enables structured and free-text search capabilities. When available, links to external databases, including MvirDB, are provided. MannDB contains whole-proteome analyses for at least one representative organism from each category of biological threat organism listed by APHIS, CDC, HHS, NIAID, USDA, USFDA, and WHO. CONCLUSION: MannDB comprises a large number of genomes and comprehensive protein sequence analyses representing organisms listed as high-priority agents on the websites of several governmental organizations concerned with bio-terrorism. MannDB provides the user with a BLAST interface for comparison of native and non-native sequences and a query tool for conveniently selecting proteins of interest. In addition, the user has access to a web-based browser that compiles comprehensive and extensive reports. Access to MannDB is freely available at http://manndb.llnl.gov/.

Algorithms↗
PubMed2025

Core passive and facultative mTOR-mediated mechanisms coordinate mammalian protein synthesis and decay.

The maintenance of cellular homeostasis requires tight regulation of proteome concentration and composition. To achieve this, protein production and elimination must be robustly coordinated. However, the mechanistic basis of this coordination remains unclear. Here, we address this question using quantitative live-cell imaging, computational modeling, transcriptomics, and proteomics approaches. We found that protein decay rates systematically adapt to global alterations of protein synthesis rates. This adaptation is driven by a core passive mechanism supplemented by facultative changes in mechanistic/mammalian target of rapamycin (mTOR) signaling. Passive adaptation hinges on changes in the production rate of the machinery governing protein decay and allows for partial maintenance of the cellular proteome. Sustained changes in mTOR signaling provide an additional layer of adaptation unique to naive pluripotent stem cells, allowing for near-perfect maintenance of proteome composition. Our work unravels the mechanisms protecting the integrity of mammalian proteomes upon variations in protein synthesis rates. A record of this paper's transparent peer review process is included in the supplemental information.

TOR Serine-Threonine Kinases↗
PubMed2006

Enhanced detectability in proteome studies.

The discovery of candidate biomarkers from biological materials coupled with the development of detection methods holds both incredible clinical potential as well as significant challenges. However, the proteomic techniques still provide the low dynamic range of protein detection at lower abundances. This review describes the current development of potential methods to enhance the detection and quantification in proteome studies. It also includes the bioinformatics tools that are helpfully used for data mining of protein ontology. Therefore, we believe that this review provided many proteomic approaches, which would be very potent and useful for proteome studies and for further diagnostic and therapeutic applications.

Biomarkers↗
PubMed2006

Looking for Thom's biomarkers with proteomics.

In recent years, large numbers of putative disease biomarkers have been identified. Combinations of protein biomarkers have been proposed to overcome the lack of single, magic-bullet identifiers of disease conditions. The number of biomarkers in a panel must be kept small to avoid the combinatorial explosion that requires very large, uneconomical sample cohorts for validation. Recent results on high sensitivity blood-based diagnostic proteomics (Godovac-Zimmermann, J et al., J. Proteome Res. 2006) suggest that the keys to identifying useful panels include judicious application of physiological knowledge to choose appropriate combinations of local, tissue/disease markers and global, systemic markers and to use very high sensitivity protein detection. Biomarkers that show non-Gaussian landscapes reminiscent of Rene Thom's multiple, stable-state landscapes seem to have the greatest predictive value for breast cancer (Godovac-Zimmermann, J. et al., J. Proteome Res. 2006).

Biomarkers↗
PubMed2004

Feature-based prediction of non-classical and leaderless protein secretion.

We present a sequence-based method, SecretomeP, for the prediction of mammalian secretory proteins targeted to the non-classical secretory pathway, i.e. proteins without an N-terminal signal peptide. So far only a limited number of proteins have been shown experimentally to enter the non-classical secretory pathway. These are mainly fibroblast growth factors, interleukins and galectins found in the extracellular matrix. We have discovered that certain pathway-independent features are shared among secreted proteins. The method presented here is also capable of predicting (signal peptide-containing) secretory proteins where only the mature part of the protein has been annotated or cases where the signal peptide remains uncleaved. By scanning the entire human proteome we identified new proteins potentially undergoing non-classical secretion. Predictions can be made at http://www.cbs.dtu.dk/services/SecretomeP.

Amino Acid Motifs↗
PubMed2004

Architecture of a grid-enabled research platform with location-transparency for bioinformatics.

The recent advance in information technologies has bought about the borderlessness in every field of both science and business. The borderlessness has increasingly made activities in interdisciplinary field more important. This current situation produces a strong demand that people want to establish a virtual group, organization and society for their business and scientific purposes irrespective of the actual structure formed by organizations. Remarkably, bio sciences require a research platform that satisfies such demand for further development. In this paper, we present a research platform for bioinformatics in detail. The prominent feature of the research platform is the use of Grid and its location transparency, which means that bio scientists and researchers are able to utilize a large amount of computational power for their analysis and to access data of their interest without being aware of where data and computational resources are located. The usefulness and feasibility of the architecture of the research platform is shown as well as future issues to achieve toward the final goal of our research in this paper.

Biomedical Research↗
PubMed2006

Profiling human brain proteome by multi-dimensional separations coupled with MS.

In our initial attempt to analyze the human brain proteome, we applied multi-dimensional protein separation and identification techniques using a combination of sample fractionation, 1-D SDS-PAGE, and MS analysis. The complexity of human brain proteome requires multiple fractionation strategies to extend the range and total number of proteins identified. According to the method of Klose (Methods Mol. Biol. 1999, 112, 67), proteins of the temporal lobe of human brain were fractionated into (i) cytoplasmic and nucleoplasmic, (ii) membrane and other structural, and (iii) DNA-binding proteins. Each fraction was then separated by SDS-PAGE, and the resulting gel line was cut into approximately 50 bands. After trypsin digestion, the resulting peptides from each band were analyzed by RP-LC/ESI-MS/MS using an LTQ spectrometer. The SEQUEST search program, which searched against the IPI database, was used for peptide sequence identification, and peptide sequences were validated by reversed sequence database search and filtered by the Protein Hit Score. Ultimately, 1533 proteins could be detected from the human brain. We classified the identified proteins according to their distribution on cellular components. Among these proteins, 24% were membrane proteins. Our results show that the multiple separation strategy is effective for high-throughput characterization of proteins from complex proteomic mixtures.

Brain↗
PubMed2006

A Fourier transformation based method to mine peptide space for antimicrobial activity.

BACKGROUND: Naturally occurring antimicrobial peptides are currently being explored as potential candidate peptide drugs. Since antimicrobial peptides are part of the innate immune system of every living organism, it is possible to discover new candidate peptides using the available genomic and proteomic data. High throughput computational techniques could also be used to virtually scan the entire peptide space for discovering out new candidate antimicrobial peptides. RESULT: We have identified a unique indexing method based on biologically distinct characteristic features of known antimicrobial peptides. Analysis of the entries in the antimicrobial peptide databases, based on our indexing method, using Fourier transformation technique revealed a distinct peak in their power spectrum. We have developed a method to mine the genomic and proteomic data, for the presence of peptides with potential antimicrobial activity, by looking for this distinct peak. We also used the Euclidean metric to rank the potential antimicrobial peptides activity. We have parallelized our method so that virtually any given protein space could be data mined, in search of antimicrobial peptides. CONCLUSION: The results show that the Fourier transform based method with the property based coding strategy could be used to scan the peptide space for discovering new potential antimicrobial peptides.

Amino Acid Sequence↗
PubMed2006

Experimental design of time series data for learning from dynamic Bayesian networks.

Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) are becoming more widely used as a way to learn various types of networks, including cellular signaling networks, from high-throughput data. Due to the high cost of performing experiments, we are interested in developing an experimental design for time series data generation. Specifically, we are interested in determining properties of time series data that make them more efficient for DBN modeling. We present a theoretical analysis on the ability of DBNs without hidden variables to learn from proteomic time series data. The analysis reveals, among other lessons, that under a reasonable set of assumptions a fixed budget is better spent on collecting many short time series data than on a few long time series data.

Algorithms↗
PubMed2003

Proteomic analysis of wild-type Sinorhizobium meliloti responses to N-acyl homoserine lactone quorum-sensing signals and the transition to stationary phase.

Proteome analysis revealed that two long-chain N-acyl homoserine lactones (AHLs) produced by Sinorhizobium meliloti 1021 induced significant differences in the accumulation of more than 100 polypeptides in early-log-phase cultures of the wild type. Fifty-six of the corresponding proteins have been identified by peptide mass fingerprinting. The proteins affected by addition of these two AHLs had diverse functions in carbon and nitrogen metabolism, energy cycles, metabolite transport, DNA synthesis, and protein turnover. Two hours of exposure to 3-oxo-C(16:1)-homoserine lactone (3-oxo-C(16:1)-HL) affected the accumulation of 40 of the 56 identified proteins, whereas comparable exposure to C(14)-HL affected 13 of the 56 proteins. Levels of four proteins were affected by both AHLs. Exposure to 3-oxo-C(16:1)-HL for 8 h affected the accumulation of 17 proteins, 12 of which had reduced accumulation. Of the 80 proteins identified as differing in accumulation between early-log- and early-stationary-phase cultures, only 13 were affected by exposure to 3-oxo-C(16:1)-HL or C(14)-HL. These results provide a foundation for future studies of the functions regulated by AHL quorum sensing in S. meliloti and help to establish proteomic analysis as a powerful global approach to the identification of quorum-sensing regulatory patterns in wild-type bacteria.

4-Butyrolactone↗
PubMed2002

Development of improved cell lysis, solubilization and imaging approaches for proteomic analyses.

Analysis of complex biochemical processes at the level of the proteome requires methods that quantitatively solubilize cytosolic and membrane bound proteins yet are compatible with isoelectric focusing and sodium dodecyl sulfate-polyacrylamide gel electrophoresis. In addition, it is often necessary to employ several highly sensitive detection methods to identify key proteins that are modified or exhibit a change in expression levels in response to a given experimental stimulus or condition. Methods were developed that efficiently extract tissues or lyse cultured cells and quantitatively solubilize proteins in a single step without the need to shear nucleic acids. These approaches utilize urea, thiourea, a mixture of detergents, low levels of an ampholyte blend, reductant and a combination of alcohols. To aid in the detection of low abundance proteins and the accurate identification of specific proteins of interest in these samples, two approaches were pursued. In one, proteins are transferred from two-dimensional (2-D) gels to blot membranes. Proteins are then detected by staining with SYPRO Ruby and the resulting 2-D protein pattern is captured using a charge-coupled device (CCD) camera. The blots are then probed with antibodies directed against the protein(s) or functionalities of interest. The resulting chemiluminescent blot image is also generated with the CCD camera and the fluorescent SYPRO Ruby image is recaptured again without moving the membrane. It is thereby possible to generate a direct image overlay of the blot pattern on that of the stained protein pattern. This approach significantly aids in the accurate identification of the dye-stained protein that is detected by the specific antibody. In addition to detecting protein post-gel transfer, a second approach utilizes protein samples labeled with fluorescent dyes prior to 2-D electrophoresis in an effort to increase the sensitivity of protein detection and to facilitate protein quantitation. It is also possible to stain the blots with different dyes and overlay these images as well. Using these approaches, it is possible to perform more rapid and accurate comparative analyses and proteomic, post-gel characterization of proteins of interest than using comparative image analysis of multiple gels.

Blotting, Western↗
PubMedDate not supplied

[Numerical euristic approach to the proteome map analysis].

The possibility of numerical-heuristic approach has been examined for proteome map analysis that realized by neural network processing of 2D protein electrophoregramms. The fetal alcohol syndrome was used to illustrate that this approach islatively effective for diagnostics of diseases.

Biomarkers↗
PubMed2004

Use of performic acid oxidation to expand the mass distribution of tryptic peptides.

Significant identification of proteins by mass fingerprinting and partial sequencing of tryptic peptides is central to proteomics. However, peptide masses cluster with distances of approximately 1 Da. Expanding these clusters will give more peptides of unique masses, thereby identifying proteins with a higher significance. The mass clusters can be expanded downward by including more oxygen atoms in the peptides. Classic performic acid oxidation modifies three residues, Cys to CysO(3), Met to MetO(2), and Trp to TrpO(2). In this study, we compare the mass distributions of tryptic peptides computed from the predicted proteomes of Bacillus subtilis, Drosophila melanogaster, Arabidopsis thaliana, and Homo sapiens modified by oxidation, reduction, and reduction followed by carboxymethylation, carboxamidomethylation, or pyridylethylation. Forty to 46% of the eukaryotic tryptic peptides contain Cys, Met, or Trp. Additionally, the importance of mass accuracy of differentially modified tryptic peptides for significant protein identification by database searches was analyzed. The results show that performic acid oxidation gives markedly extended mass distributions at mass accuracies from +/-0.002 to +/-0.25 Da for the eukaryotes. The effect of the expanded mass distribution on significant protein identification was illustrated by searching simulated mass peak lists against the databases containing oxidized and reduced tryptic peptides. The specificity of formic acid oxidation was tested experimentally, and no general adverse effects were detected. Tryptic peptides provided a 100% sequence coverage of oxidized barley grain peroxidase by LC-MS, and the sequence coverages of oxidized and carboxymethylated bovine serum albumin were similar by MALDI-TOF MS analyses.

Amino Acids↗
PubMedDate not supplied

Matching 2D gel electrophoresis images.

Automatic alignment (matching) of two-dimensional gel electrophoresis images is of primary interest in the field of proteomics. The proposed method of 2D gel image matching is based on fuzzy alignment of features, extracted from gels' images, and it allows both global and local interpolation of image grid, followed by brightness interpolation. Method performance is tested on simulated images and gel images available via the Internet databases.

Algorithms↗
PubMed2004

Fast automatic registration of images using the phase of a complex wavelet transform: application to proteome gels.

Image registration describes the process of manipulating a distorted version of an image such that its pixels overlay the equivalent pixels in a clean, master or reference image. The need for it has assumed particular prominence in the analysis of images of electrophoretic gels used in the analysis of protein expression levels in living cells, but also has fundamental applications in most other areas of image analysis. Much of the positional information of a data feature is carried in the phase of a complex transform, so a complex transform allows explicit specification of the phase, and hence of the position of features in the image. Registration of a test gel to a reference gel is achieved by using a multiresolution movement map derived from the phase of a complex wavelet transform (the Q-shift wavelet transform) to dictate the warping directly via movement of the nodes of a Delaunay-triangulated mesh of points. This warping map is then applied to the original untransformed image such that the absolute magnitude of the spots remains unchanged. The technique is general to any type of image. Results are presented for a simple computer simulated gel, a simple real gel registration between similar "clean" gels with local warping vectors distributed about one main direction, a hard problem between a reference gel and a "dirty" test gel with multi-directional warping vectors and many artifacts, and some typical gels of present interest in post-genomic biology. The method compares favourably with others, since it is computationally rapid, effective and entirely automatic.

Animals↗
PubMed2007

Quantitative analysis of 2D gels.

The use of 2D electrophoresis for comparative proteomics allows the revelation of variations of protein relative amounts according to various physiological or genetic criteria. The statistical significance of these results is related to different factors, from the experimental design to the statistical tests. In this chapter we describe different parameters that should be taken into account in the experimental design and during image acquisition, and we present different programs for the normalization of quantitative data, the selection of reproducible spots according to user-defined criteria, and the selection of spots showing qualitative and significant quantitative variations.

Algorithms↗