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PubMed2005

Numerical approaches for quantitative analysis of two-dimensional maps: a review of commercial software and home-made systems.

The present review attempts to cover a number of methods that have appeared in the last few years for performing quantitative proteome analysis. However, due to the large number of methods described for both electrophoretic and chromatographic approaches, we have limited this review to conventional two-dimensional (2-D) map analysis which couples orthogonally a charge-based step (isoelectric focusing) to a size-based separation step (sodium dodecyl sulfate-electrophoresis). The first and oldest method applied to 2-D map data reduction is based on statistical analysis performed on sets of gels via powerful software packages, such as Melanie, PDQuest, Z3 and Z4000, Phoretix and Progenesis. This method calls for separately running a number of replicas for control and treated samples. The two sets of data are then merged and compared via a number of software packages which we describe. In addition to commercially-available systems, a number of home made approaches for 2-D map comparison have been recently described and are also reviewed. They are based on fuzzyfication of the digitized 2-D gel image coupled to linear discriminant analysis, three-way principal component analysis or a combination of principal component analysis and soft-independent modeling of class analogy. These statistical tools appear to perform well in differential proteomic studies.

Cluster Analysis↗
PubMed2006

Label-free semiquantitative peptide feature profiling of human breast cancer and breast disease sera via two-dimensional liquid chromatography-mass spectrometry.

A label-free semiquantitative peptide feature profiling method was developed in response to challenges associated with analysis of two-dimensional liquid chromatography-tandem mass spectrometry data. One hundred twenty human sera (49 from invasive breast carcinoma patients, 26 from non-invasive breast carcinoma patients, 35 from benign breast disease patients, and 10 from normal controls) were repeatedly analyzed using a standardized two-dimensional liquid chromatography-mass spectrometry method. Data were extracted using the novel semiquantitative peptide feature profiling method, which is based on comparisons of normalized relative ion intensities. Hierarchical cluster analyses and principle component analyses were used to evaluate the predicative capability of the extracted data, and results were promising. Extracted data were also randomly assigned to either a training group (65%) or to a test group (35%) for artificial neural network modeling. Models best identified invasive breast carcinomas (212 predictions, 94% accurate) and benign non-neoplastic breast disease (96 predictions, 81.3% accurate). These results suggest that, after further development, the novel method may be useful for large scale clinical proteomic profiling.

Breast Diseases↗
PubMedDate not supplied

Proteomic analysis of the outer membrane of Anabaena sp. strain PCC 7120.

Anabaena is a model to analyze the evolutionary development of plastids, cell differentiation, and the regulation of nitrogen fixation. Thereby, the outer membrane proteome is the place of sensing environmental differences and during plastid development, systems for intracellular communication had to be added to the proteome of this membrane. We present a protocol for the isolation of the outer membrane from Anabaena and the analysis of the proteome using different tools. 55 proteins were identified.

Anabaena↗
PubMed2003

From genomics to proteomics.

Proteomics is the study of the function of all expressed proteins. Tremendous progress has been made in the past few years in generating large-scale data sets for protein-protein interactions, organelle composition, protein activity patterns and protein profiles in cancer patients. But further technological improvements, organization of international proteomics projects and open access to results are needed for proteomics to fulfil its potential.

Animals↗
PubMed2002

Proteome analysis based on motif statistics.

MOTIVATION: Even for the amino acid motifs collected in the Prosite database there may be chance occurences as opposed to those occurences where the motif is involved in fold or function of a protein. With recent mathematical advances in assessing the significance of observing such a motif a particular number of times, we can now study the over- or under-representation of particular motifs in a complete genome and attempt to make functional deductions. RESULTS: We demonstrate that statistical over- or under-representation of motifs in complete proteomes may be an indicator of whether, in that organism, we are looking at chance occurrences of the motif or whether the occurrences are sufficiently numerous to suggest a systematic, and thus functionally important occurrence. This has important implications on databank annotations. AVAILABILITY: The complete dataset comprising the plotted statistics of 266 Prosite motifs on 42 proteomes is available at http://algo.inria.fr/nicodeme/proteomes/proteocomp.html. The software used to compute this data has been described by Nicodème (2000, 2001). They are available either by web access as mentioned in these articles or by direct request from Pierre Nicodème.

Amino Acid Motifs↗
PubMed2002

Interactive InterPro-based comparisons of proteins in whole genomes.

MOTIVATION: The SWISS-PROT group at the EBI has developed the Proteome Analysis Database utilizing existing resources and providing comprehensive and integrated comparative analysis of the predicted protein coding sequences of the complete genomes of bacteria, archaea and eukaryotes. The Proteome Analysis Database is accompanied by a program that has been designed to carry out interactive InterPro proteome comparisons for any one proteome against any other one or more of the proteomes in the database.

Computational Biology↗
PubMed2005

BioLayout(Java): versatile network visualisation of structural and functional relationships.

Visualisation of biological networks is becoming a common task for the analysis of high-throughput data. These networks correspond to a wide variety of biological relationships, such as sequence similarity, metabolic pathways, gene regulatory cascades and protein interactions. We present a general approach for the representation and analysis of networks of variable type, size and complexity. The application is based on the original BioLayout program (C-language implementation of the Fruchterman-Rheingold layout algorithm), entirely re-written in Java to guarantee portability across platforms. BioLayout(Java) provides broader functionality, various analysis techniques, extensions for better visualisation and a new user interface. Examples of analysis of biological networks using BioLayout(Java) are presented.

Computer Graphics↗
PubMed2001

EUDOC: a computer program for identification of drug interaction sites in macromolecules and drug leads from chemical databases.

The completion of the Human Genome Project, the growing effort on proteomics, and the Structural Genomics Initiative have recently intensified the attention being paid to reliable computer docking programs able to identify molecules that can affect the function of a macromolecule through molecular complexation. We report herein an automated computer docking program, EUDOC, for prediction of ligand-receptor complexes from 3D receptor structures, including metalloproteins, and for identification of a subset enriched in drug leads from chemical databases. This program was evaluated from the standpoints of force field and sampling issues using 154 experimentally determined ligand-receptor complexes and four "real-life" applications of the EUDOC program. The results provide evidence for the reliability and accuracy of the EUDOC program. In addition, key principles underlying molecular recognition, and the effects of structural water molecules in the active site and different atomic charge models on docking results are discussed. Copyright 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1750-1771, 2001

Journal Article↗
PubMed2001

Bioinformatic assessment of mass spectrometric chemical derivatisation techniques for proteome database searching.

Identification of proteins from the mass spectra of peptide fragments generated by proteolytic cleavage using database searching has become one of the most powerful techniques in proteome science, capable of rapid and efficient protein identification. Using computer simulation, we have studied how the application of chemical derivatisation techniques may improve the efficiency of protein identification from mass spectrometric data. These approaches enhance ion yield and lead to the promotion of specific ions and fragments, yielding additional database search information. The impact of three alternative techniques has been assessed by searching representative proteome databases for both single proteins and simple protein mixtures. For example, by reliably promoting fragmentation of singly-charged peptide ions at aspartic acid residues after homoarginine derivatisation, 82% of yeast proteins can be unambiguously identified from a single typical peptide-mass datum, with a measured mass accuracy of 50 ppm, by using the associated secondary ion data. The extra search information also provides a means to confidently identify proteins in protein mixtures where only limited data are available. Furthermore, the inclusion of limited sequence information for the peptides can compensate and exceed the search efficiency available via high accuracy searches of around 5 ppm, suggesting that this is a potentially useful approach for simple protein mixtures routinely obtained from two-dimensional gels.

Animals↗
PubMed2025

Associations of High Attenuation Area-Related Proteomic Biomarkers with Fibrotic or Subpleural Interstitial Lung Abnormalities.

Rationale: High-attenuation area (HAA) is a computed tomography (CT) tool that correlates with lung inflammation and fibrosis. Systemic molecular correlates of HAA (e.g., plasma proteins) may inform biological processes involved in interstitial lung disease. Objectives: To identify plasma proteins that associate with HAA and correlate with a higher probability of developing new-onset fibrotic or subpleural interstitial lung abnormalities (ILAs). Methods: Plasma protein levels were measured using a semiquantitative aptamer-based platform in MESA (the Multi-Ethnic Study of Atherosclerosis; N = 5,486) and SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study; N = 1,781). Linear regression models identified HAA-associated proteins after adjustment for demographic and socioeconomic factors, CT scanner parameters, study center, and batch. Associations of HAA-related proteins with new-onset fibrotic or subpleural ILAs were examined in MESA participants with ILA assessments on full-lung CT 10 years later. Immunohistochemical staining of select proteins was performed in lung tissue from pulmonary fibrosis cases. Measurements and Main Results: There were 75 proteins detected that were significantly associated with HAA in MESA and SPIROMICS. Gene Ontology analysis of these proteins identified processes involved in immune cell chemotaxis and cellular growth and apoptosis. Seven proteins were associated with a higher probability of new-onset fibrotic or subpleural ILAs in MESA, and two of these, junctional adhesion molecule-like protein and GTP cyclohydrolase 1 feedback regulatory protein, stained in areas of fibrosis in lung tissue from patients with interstitial lung disease. Conclusions: Plasma proteins associated with more HAA are involved in immune and cellular processes and associate with new-onset fibrotic-subpleural ILA.

Humans↗
PubMed2006

The HUPO Plasma Proteome Project: a report from the Munich congress.

The Human Proteome Organization has several major collaborative research initiatives, including the Plasma Proteome Project. A major feature of the HUPO World Congress in Munich in August 2005 was the release of the special issue of PROTEOMICS with 28 articles from the pilot phase of the Plasma Proteome Project. An open Workshop and a presentation in the closing plenary session of the congress focused on next phases for the Plasma Proteome Project.

Antibodies↗
PubMed2006

Machine learning in bioinformatics.

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

Artificial Intelligence↗
PubMed2004

A general computational model of mitochondrial metabolism in a whole organelle scale.

UNLABELLED: A computational tool for mitochondrial systems biology has been developed as a simulation model of E-Cell2, a publicly available simulation system. The general model consists of 58 enzymatic reactions and 117 metabolites, representing the respiratory chain, the TCA cycle, the fatty acid beta-oxidation and the inner-membrane transport system. It is based on previously published enzyme kinetics studies in the literature; we have successfully integrated and packaged them into a single large model. The model can be easily extended and modified so that mitochondrial biologists/physiologists can integrate their own models and evaluate them in the context of the whole organelle metabolism. AVAILABILITY: The mitochondrial model is bundled up with E-Cell2 simulation system, which can be downloaded from http://www.e-cell.org. CD-ROMs are also available and are distributed at major conferences. SUPPLEMENTARY INFORMATION: All the kinetic data are available via http://www.e-cell.org

Animals↗