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PubMed2005

Comparative analysis of DNA-binding proteins between thermophilic and mesophilic bacteria.

Thermophilic bacteria are one of the most attractive forms of life, and their adaptation mechanisms to elevated temperatures have been extensively studied over the years. Thermal adaptations of cell components such as proteins and RNA are well studied, but adaptations of interactions between these components must be also vital for the thermophiles. Protein-DNA interactions play crucial roles in the cell, but little is known about their thermal adaptations. In this study, we analyzed DNA-binding proteins from thermophilic bacteria. Comparison of amino acid compositions at the DNA-binding interfaces between thermophiles and their mesophilic close relatives revealed several commonalities between phylogenetically unrelated organisms. Advantages and limitations of our methods will be also discussed.

Adaptation, Physiological↗
PubMed2006

Rank information: a structure-independent measure of evolutionary trace quality that improves identification of protein functional sites.

Protein functional sites are key targets for drug design and protein engineering, but their large-scale experimental characterization remains difficult. The evolutionary trace (ET) is a computational approach to this problem that has been useful in a variety of case studies, but its proteomic scale application is partially hindered because automated retrieval of input sequences from databases often includes some with errors that degrade functional site identification. To recognize and purge these sequences, this study introduces a novel and structure-free measure of ET quality called rank information (RI). It is shown that RI decreases in response to errors in sequences, alignments, or functional classifications. Conversely, an automated procedure to increase RI by selectively removing sequences improves functional site identification so as to nearly match manually curated traces in kinases and in a test set of 79 diverse proteins. Thus we conclude that RI partially reflects the evolutionary consistency of sequence, structure, and function. In practice, as the size of the proteome continues to grow exponentially, it provides a novel and structure-free measure of ET quality that increases its accuracy for large-scale automated annotation of protein functional sites.

Algorithms↗
PubMedDate not supplied

[Computer-assisted vaccine design].

With the modern molecular biology techniques, it has been possible to detect, isolate and clone biological macromolecules, which could be used as immunogenes in artificial vaccine constructs. In the post-genomic era, the prospective immunogenic components are searched using bionformatic tools and proteomic technologies. Today it is quite realistic to combine the artificial vaccine constructs from the preselected molecular components. Existing computational methods are able to detect the potential immunogenes in genomic sequences, predict their characteristics and subcellular location. The set of methods is designed to predict the T- and B-epitopes that can be used as components of minimal vaccine constructs. The variety of systems for production and delivery of vaccines are developed and tested. These include transgenic plants, bacterial and viral vectors, DNA molecules etc. Several informational resources provide free access to molecular immunology data and deliver services on prediction of antigenic features. Several artificial vaccines have already been launched, but much more preparations are under preclinical and clinical trials. Computer-aided design of vaccines may significantly decrease time and costs required for their development. Modern bioinformatic technologies are now employed for discovery of more effective and potent vaccine.

Animals↗
PubMed2005

What is mzXML good for?

mzXML (extensible markup language) is one of the pioneering data formats for mass spectrometry-based proteomics data collection. It is an open data format that has benefited and evolved as a result of the input of many groups, and it continues to evolve. Due to its dynamic history, its structure, purpose and applicability have all changed with time, meaning that groups that have looked at the standard at different points during its evolution have differing impressions of the usefulness of mzXML. In discussing mzXML, it is important to understand what mzXML is not. First, mzXML does not capture the raw data. Second, mzXML is not sufficient for regulatory submission. Third, mzXML is not optimized for computation and, finally, mzXML does not capture the experiment design. In general, it is the authors' opinion that XML is not a panacea for bioinformatics or a substitute for good data representation, and groups that want to use mzXML (or other XML-based representations) directly for data storage or computation will encounter performance and scalability problems. With these limitations in mind, the authors conclude that mzXML is, nonetheless, an indispensable data exchange format for proteomics.

Computational Biology↗
PubMed2005

Great mood in proteomics: Beijing and the HUPO Human Brain Proteome Project.

More than 1200 attendees came together at the 3(rd) HUPO World Congess in Beijing, October 25-27, 2004. In numerous different sessions the wide range of proteomic areas became visible. The HUPO Brain Proteome Project (HUPO BPP) organized an evening session on October 23, presenting the first results of two pilot studies as well as the newest, very positive international development in this field. The rising importance became even more apparent in the plenary presentation of all HUPO initiatives and the following congress activities.

Animals↗
PubMed2005

Developing liquid chromatography ion mobility mass spectometry techniques.

When a packet of ions in a buffer gas is exposed to a weak electric field, the ions will separate according to differences in their mobilities through the gas. This separation forms the basis of the analytical method known as ion mobility spectroscopy and is highly efficient, in that it can be carried out in a very short time frame (micro- to milliseconds). Recently, efforts have been made to couple the approach with liquid-phase separations and mass spectrometry in order to create a high-throughput and high-coverage approach for analyzing complex mixtures. This article reviews recent work to develop this approach for proteomics analyses. The instrumentation is described briefly. Several multidimensional data sets obtained upon analyzing complex mixtures are shown in order to illustrate the approach as well as provide a view of the limitations and required future work.

Animals↗
PubMed2003

Proteomic analysis of immunostained, laser-capture microdissected brain samples.

Proteomic analysis is often performed on homogenized preparations of whole tissues, which does not provide any information about relevant biochemical changes in specific cell types. Laser-capture microdissection (LCM) is a technique that is precise enough to dissect single cells within a tissue section. Phenotypically defined cells of interest may be visualized by immunostaining prior to microdissection. Previously published immunostaining protocols adapted to LCM require the use of very high antibody titers and very short incubation times. This raises the concern that low-abundance antigens would not be detected and that antisera would be rapidly depleted. In addition, protein recovery from samples was not evaluated in most of these studies. Here, we describe an optimized immunostaining method based on immunofluorescence. By comparing two-dimensional electrophoresis (2-DE) results obtained from immunostained LCM brain tissue samples to those obtained from unstained, manually dissected samples, we demonstrated that immunofluorescent staining gave comparable protein recovery and similar resolution of protein spots on 2-DE gels. Moreover, matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry analysis of selected spots from gels derived from control and immunostained LCM samples revealed that the immunostaining process had minimal effect on protein identification. LCM of immunofluorescently labeled tissue sections is a practical and powerful method to perform proteomic studies on specifically defined cell groups.

Animals↗
PubMed2006

Piecing together a ciliome.

Cilia are slender microtubule-based appendages that emanate from the surfaces of a large proportion of eukaryotic cells. The motile and non-motile forms of cilia represent bona fide organelles comprising distinct repertoires of proteins that serve specific roles in locomotion or fluid movement, and sense chemical or physical extracellular cues. Owing in part to the growing number of genes associated with ciliary disorders, such as polycystic kidney disease and Bardet-Biedl syndrome, there has been a recent profusion of studies aimed at unveiling the protein makeup of cilia. The approaches used are complementary, involving several different organisms and spanning the fields of bioinformatics, genomics and proteomics. Here we review these studies and assess the various data sets to help define a comprehensive ciliary proteome, or 'ciliome'. We have compiled a cilia protein database that includes known cilia-associated proteins and numerous putative ciliary proteins including RAB-like small GTPases, which might be implicated in vesicular trafficking, and the microtubule-binding protein MIP-T3, some of which might be associated with ciliopathies.

Animals↗
PubMed2007

Useful mass spectrometry programs freely available on the internet.

The intention with this chapter is to give an overview of a broad range of freely available programs on the internet which are useful for analyses of mass spectrometry data. The list is by no means a full list of free proteomics tools on the net and I apologize if there are other good tools on the internet that have been missed. The presented programs should cover the needs for the most general tasks in data analysis of proteomics data and have to a limited extend been tested. The links provided in this chapter will over time become invalid. In such cases it is worth while to try a World Wide Web search using the program packages names. This will often reveal the updated links. Some of the links presented in this chapter will also be maintained at http://yass.sdu.dk.

Computational Biology↗
PubMed2004

Acute leukemia subclassification: a marker protein expression perspective.

Improved leukemia classification and tailoring of therapy have greatly improved patient outcome particularly for children with acute leukemia (AL). Using immunophenotyping, molecular genetics and cytogenetics the low hanging fruits of biomedical research have been successfully incorporated in routine diagnosis of leukemia subclasses. Future improvements in the classification and understanding of leukemia biology will very likely be more slow and laborious. Recently, gene expression profiling has provided a framework for the global molecular analysis of hematological cancers, and high throughput proteomic analysis of leukemia samples is on the way. Here we consider classification of acute leukemia samples by flow cytometry using the marker proteins of immunophenotyping as a component of the proteome. Marker protein expressions are converted into quantitative expression values and subjected to computational analysis. Quantitative multivariate analysis from panels of marker proteins has demonstrated that marker protein expression profiles can distinguish MLLre from non-MLLre ALL cases and also allow to specifically distinguish MLL/AF4 cases. Potentially, these quantitative expression analyses can be used in clinical diagnosis. Immunophenotypic data collection using flow cytometry is a fast and relatively easily accessible technology that has already been implemented in most centers for leukemia diagnosis and the translation into quantitative expression data sets is a matter of flow cytometer settings and output calibration. However, before application in clinical diagnostics can occur it is crucial that quantitative immunophenotypic data set analysis is validated in independent experiments and in large data sets.

Acute Disease↗
PubMed2004

Fine-tuning the prediction of sequences cleaved by signal peptidase II: a curated set of proven and predicted lipoproteins of Escherichia coli K-12.

A curated set of 81 proven and 44 predicted lipoproteins of Escherichia coli K-12 was defined with the combined use of a literature survey, a variety of predictive tools and human expertise. The well-documented Gram-negative proteome of E. coli K-12 was chosen to assess how the different approaches complement each other and to ensure a stable definition of a consistent set of lipoproteins. The results of detailed analysis of such proteins at the level of a single proteome are presented, corroborated and rationalized.

Amino Acid Motifs↗
PubMed2001

Individuation of monoclonal anti-HPV16 E7 antibody linear peptide epitope by computational biology.

We applied computational biology to identify the linear amino acid sequence recognized by a mouse monoclonal antibody raised against the full length HPV16 E7 oncoprotein. Computer-assisted search for the epitopic peptide used two parameters: the capability of E7 peptides to bind to MHC class II molecules, and the similarity level of the oncoprotein sequence to the mouse proteome. We report that anti-E7 mAb recognized the peptide having both high binding potential to MHC II molecules and low level of molecular mimicry to mouse proteome. Peptide ability to bind to MHC II molecules appears a necessary but not sufficient condition to determine peptide immunodominance, by needing to be supported by a low degree of peptide similarity to the host's proteome.

Amino Acid Sequence↗
PubMed2005

The use of common ontologies and controlled vocabularies to enable data exchange and deposition for complex proteomic experiments.

Controlled vocabularies provide a roadmap through complex biological data. Proteomic data is increasing in volume and is currently poorly served by public repositories due to the large number of different formats in which the data is generated and stored. The Human Proteome Organization Proteome Standards Initiative is establishing standards for data transfer and deposition. These standards utilize ontologies and controlled vocabularies to describe experimental procedures and common processes such as sample preparation This paper will discuss the development of such ontologies by the user community and their current utilization in the fields of protein:proein interactions and mass spectrometry.

Computational Biology↗
PubMed2026

Diet modulates cardiac metabolic stress during anthracycline treatment.

Diet is a modifiable determinant of cardiovascular risk and may influence tolerance to cancer therapies. The mechanisms by which specific dietary components affect cardiac metabolism during anthracycline treatment remain poorly defined, limiting the incorporation of dietary recommendations into treatment guidelines. Here, we integrated heart proteomics data from patients treated with or without anthracyclines with a genome-scale reconstruction of human cardiac metabolism (CardioNet). Using constraint-based flux analysis, we conducted >30,000 in silico simulations of diet scenarios generated from chemical profiles of ∼500 foods curated in the Periodic Table of Food Initiative. These simulations revealed that diets enriched in rapidly absorbable sugars and depleted of essential fatty acids impair cardiac metabolic efficiency, increasing reactive oxygen species production and the demand for purine salvage fluxes. These predicted metabolic patterns were consistent with plasma metabolomics from patients treated with anthracyclines, validating our findings. Computational modeling of 39 recipes across six cuisines revealed cardiometabolic effects of omnivorous versus vegan diets in patients. Modeling of a healthy vegan diet increased cardiometabolic efficiency compared with a healthy omnivorous diet in patients treated with anthracyclines, independent of the culinary background. Our approach demonstrates that integrating the molecular composition of food with genome-scale metabolic models enables systematic analysis of diet patterns for translational testing. Ultimately, these in silico studies provide a framework for trials and may inform dietary recommendations for improving cardiometabolic health.NEW & NOTEWORTHY We developed a systems biology framework to predict how diet influences cardiac metabolism during cancer therapy. Across >30,000 in silico diet simulations, we identified nutrient patterns that either exacerbate or mitigate anthracycline-induced metabolic stress. These findings demonstrate how computational modeling can uncover diet-metabolism interactions driving cardiotoxicity and guide dietary interventions.

Humans↗
PubMed2003

An analysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms.

We sought to identify all genes in the Candida albicans genome database whose deduced proteins would likely be soluble secreted proteins (the secretome). While certain C. albicans secretory proteins have been studied in detail, more data on the entire secretome is needed. One approach to rapidly predict the functions of an entire proteome is to utilize genomic database information and prediction algorithms. Thus, we used a set of prediction algorithms to computationally define a potential C. albicans secretome. We first assembled a validation set of 47 C. albicans proteins that are known to be secreted and 47 that are known not to be secreted. The presence or absence of an N-terminal signal peptide was correctly predicted by SignalP version 2.0 in 47 of 47 known secreted proteins and in 47 of 47 known non-secreted proteins. When all 6165 C. albicans ORFs from CandidaDB were analysed with SignalP, 495 ORFs were predicted to encode proteins with N-terminal signal peptides. In the set of 495 deduced proteins with N-terminal signal peptides, 350 were predicted to have no transmembrane domains (or a single transmembrane domain at the extreme N-terminus) and 300 of these were predicted not to be GPI-anchored. TargetP was used to eliminate proteins with mitochondrial targeting signals, and the final computationally-predicted C. albicans secretome was estimated to consist of up to 283 ORFs. The C. albicans secretome database is available at http://info.med.yale.edu/intmed/infdis/candida/

Algorithms↗
PubMed2025

Integrated proteomic network analysis reveals PTPRC as a central hub protein orchestrating co-expression modules and metabolic dysregulation in renal carcinoma: PTPRC protein molecular action.

The occurrence of renal carcinoma is closely related to a variety of molecular mechanisms and metabolic disorders. PTPRC (protein tyrosine phosphatase receptor C), as an important regulatory protein, was studied to reveal the role of PTPRC in renal carcinoma through comprehensive proteomic network analysis, especially its core position in the coordination of co-expression modules and metabolic disorders. This study was the first to download and process multiple publicly available renal cancer transcriptome data to conduct differential gene expression analysis across datasets. Functional enrichment and disease ontology analysis were performed on the transcriptome of renal cancer, and weighted gene co-expression network (WGCNA) was constructed. The results showed that comprehensive principal component analysis revealed significant differences in the transcriptome of renal cancer, and functional annotation revealed specific pathways associated with renal cancer. WGCNA analysis identified tumor-associated co-expression modules, while multi-omics analysis further identified core regulatory networks including PTPRC. As a central hub protein, PTPRC plays an important coordinating role in the co-expression module and metabolic dysregulation of renal carcinoma. This discovery provides a new perspective for understanding the molecular mechanism of kidney cancer.

Humans↗
PubMed2006

Automated interpretation of protein subcellular location patterns.

Proteomics is a major current focus of biomedical research, and location proteomics is the important branch of proteomics that systematically studies the subcellular distributions for all proteins expressed in a given cell type. Fluorescence microscopy of labeled proteins is currently the main methodology to obtain location information. Traditionally, microscope images are analyzed by visual inspection, which suffers from inefficiency and inconsistency. Automated and objective interpretation approaches are therefore needed for location proteomics. In this article, we briefly review recent advances in automated imaging interpretation tools, including supervised classification (which assigns location pattern labels to previously unseen images), unsupervised clustering (which groups proteins based on the similarity among their subcellular distributions), and additional statistical tools that can aid cell and molecular biologists who use microscopy in their work.

Automation↗
PubMed2007

Assessment of hierarchical clustering methodologies for proteomic data mining.

Hierarchical clustering methodology is a powerful data mining approach for a first exploration of proteomic data. It enables samples or proteins to be grouped blindly according to their expression profiles. Nevertheless, the clustering results depend on parameters such as data preprocessing, between-profile similarity measurement, and the dendrogram construction procedure. We assessed several clustering strategies by calculating the F-measure, a widely used quality metric. The combination, on logged matrix, of Pearson correlation and Ward's methods for data aggregation is among the best clustering strategies, at least with the data sets we studied. This study was carried out using PermutMatrix, a freely available software derived from transcriptomics.

Algorithms↗