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PubMed2003

Phosphospecific proteolysis for mapping sites of protein phosphorylation.

Protein phosphorylation is a dominant mechanism of information transfer in cells, and a major goal of current proteomic efforts is to generate a system-level map describing all the sites of protein phosphorylation. Recent efforts have focused on developing technologies for enriching and quantifying phosphopeptides. Identification of the sites of phosphorylation typically relies on tandem mass spectrometry to sequence individual peptides. Here we describe an approach for phosphopeptide mapping that makes it possible to interrogate a protein sequence directly with a protease that recognizes sites of phosphorylation. The key to this approach is the selective chemical transformation of phosphoserine and phosphothreonine residues into lysine analogs (aminoethylcysteine and beta-methylaminoethylcysteine, respectively). Aminoethylcysteine-modified peptides are then cleaved with a lysine-specific protease to map sites of phosphorylation. A blocking step enables single-site cleavage, and adaptation of this reaction to the solid phase facilitates phosphopeptide enrichment and modification in one step.

Cyclic AMP-Dependent Protein Kinases↗
PubMed2003

Proteomics in Drosophila melanogaster: first 2D database of larval hemolymph proteins.

A proteomic approach was used for the identification of larval hemolymph proteins of Drosophila melanogaster. We report the initial establishment of a two-dimensional gel electrophoresis reference map for hemolymph proteins of third instar larvae of D. melanogaster. We used immobilized pH gradients of pH 4-7 (linear) and a 12-14% linear gradient polyacrylamide gel. The protein spots were silver-stained and analyzed by nanoLC-Q-Tof MS/MS (on-line nanoscale liquid chromatography quadrupole time of flight tandem mass spectrometry) or by Matrix assisted laser desorption time of flight MS (MALDI-TOF MS). Querying the SWISSPROT database with the mass spectrometric data yielded the identity of the proteins in the spots. The presented proteome map lists those protein spots identified to date. This map will be updated continuously and will serve as a reference database for investigators, studying changes at the protein level in different physiological conditions.

Animals↗
PubMed2006

Rejuvenating rice proteomics: facts, challenges, and visions.

Proteomics is progressing at an unprecedented pace, as can be exemplified by the progress in model organisms such as yeast, bacteria, and mammals. Proteomics research in plants, however, has not progressed at the same pace. Unscrambling of the genome sequences of the dicotyledoneous Arabidopsis thaliana (L.) and monocotyledoneous rice (Oryza sativa L.) plant species, respectively, has made them accessible reference organisms to study plant proteomics. Study of these two reference plants is expected to unravel the mystery of plant biology. Rice, a critically important food crop on the earth, has been termed a "cornerstone" and the "Rosetta stone" for functional genomics of cereal crops. Here, we look at the progress in unraveling rice proteomes and present the facts, challenges, and vision. The text is divided into two major parts: the first part presents the facts and the second part discusses the challenges and vision. The facts include the technology and its use in developing proteomes, which have been critically and constructively reviewed. The challenges and vision deal with the establishment of technologies to exhaustively investigate the protein components of a proteome, to generate high-resolution gel-based reference maps, and to give rice proteomics a functional dimension by studying PTMs and isolation of multiprotein complexes. Finally, we direct a vision on rice proteomics. This is our third review in series on rice proteomics, which aims to stimulate an objective discussion among rice researchers and to understand the necessity and impact of unraveling rice proteomes to their full potential.

Arabidopsis↗
PubMed2006

Proteome analysis of responses to ascochlorin in a human osteosarcoma cell line by 2-D gel electrophoresis and MALDI-TOF MS.

Ascochlorin is a prenyl-phenol compound that was isolated from the fungus Ascochyta viciae. Ascochlorin reduces serum cholesterol and triglyceride levels, suppresses hypertension and tumor development, and ameliorates type I and II diabetes. Here, to better understand the mechanisms by which ascochlorin regulates physiological or pathological events and induces responses in the pharmacological treatment of cancer, we performed differential analysis of the proteome of the human osteosarcoma cells U2OS in response to ascochlorin. In addition, we established the first two-dimensional map of the U2OS proteome. The U2OS cell proteomes with and without treatment with ascochlorin were compared using two-dimensional electrophoresis, matrix-assisted laser desorption/ionization mass spectrometry and bioinformatics. The largest differences in expression were observed for the epidermal growth factor receptor (4-fold decrease), ribulose-5-phosphate-epimerase (13-fold decrease), ATP-dependent RNA helicase (8-fold decrease), and kelch-like ECH-associated protein 1 (6-fold decrease). The abundance of heterogeneous nuclear ribonucleoprotein L and minichromosome maintenance protein 7 increased 12- and 8.2-fold, respectively. In addition, Erk 2 was increased 3-fold in U2OS cells treated with ascochlorin. The expression of some selected proteins was confirmed by western blotting, zymography and RT-PCR analysis.

Alkenes↗
PubMedDate not supplied

Global proteome discovery using an online three-dimensional LC-MS/MS.

We have developed a proteomics technology featuring on-line three-dimensional liquid chromatography coupled to tandem mass spectrometry (3D LC-MS/MS). Using 3D LC-MS/MS, the yeast-soluble, urea-solubilized peripheral membrane and SDS-solubilized membrane protein samples collectively yielded 3019 unique yeast protein identifications with an average of 5.5 peptides per protein from the 6300-gene Saccharomyces Genome Database searched with SEQUEST. A single run of the urea-solubilized sample yielded 2255 unique protein identifications, suggesting high peak capacity and resolving power of 3D LC-MS/MS. After precipitation of SDS from the digested membrane protein sample, 3D LC-MS/MS allowed the analysis of membrane proteins. Among 1221 proteins containing two or more predicted transmembrane domains, 495 such proteins were identified. The improved yeast proteome data allowed the mapping of many metabolic pathways and functional categories. The 3D LC-MS/MS technology provides a suitable tool for global proteome discovery.

Amino Acid Sequence↗
PubMedDate not supplied

Proteomics and leukocytes: an approach to understanding potential molecular mechanisms of inflammatory responses.

Leukocytes play an important role in the progression of disease and leukocyte-derived proteins are associated with the pathogenesis of the disease. Leukocyte activation causes production of inflammatory mediators, over-expression of cell surface adhesion molecules, and an increase in migration and infiltration, phagocytosis, and degranulation, as well as receptor phosphorylation and signal transduction. An increasing number of studies on the application of leukocyte proteomics have appeared in mapping protein profiles of inflammatory cells, contributing to the understanding of potential mechanisms involved in leukocyte function. Together with improvements in proteomic technology in leukocyte research, leukocyte proteomic analysis becomes more simple, rapid, flexible, sensitive, and specific. This enables proteomic investigation of activated or non-activated leukocytes to be highly focused on defined suborgans or specific signaling pathways. Research in leukocyte proteomics is progressing from fingerprinting to functioning, human cell lines to primary leukocytes, non-activated cells to inflammatory mediator-stimulated cells, in vitro culture to in vivo challenge, and animal models to human disease. A number of newly identified proteins from leukocyte proteomics may offer new mechanism-orientated targets for drug discovery and development.

Dendritic Cells↗
PubMed2003

Review of proteomics with applications to genetic epidemiology.

Mapping of the human genome has the potential to transform the traditional methods of genetic epidemiology. The complete draft sequence of the 3.3 billion nucleotides comprising the genome is now available over the Internet, including the location and nearly complete sequence of the 26,000 to 31,000 protein-encoding genes. However, aside from water, almost everything in the human body is either made of, or by, proteins. Although the DNA code provides the instructions for their amino acid sequence, there are an estimated 1.5 million proteins. Thus, the correlation between DNA sequence and protein is low, reflecting alternate splicing as well as post-translational modification. The purpose of this article is to explore ways in which the emerging field of proteomics, the study of proteins in a cell, may inform our approach to gene mapping. This article reviews the various technical approaches currently available for proteomics. Technologies are available to quantify protein expression (and compare normal versus disease states), identify proteins through comparison with sequence information in databases or direct sequencing (which can then be mapped to chromosomal locations to ensure appropriate markers), elucidate protein-protein interactions (which may underlie disease), determine localization of proteins within the cell (abnormal trafficking of proteins could have an inherited basis), and characterize modifications of proteins (which is relevant to modifier gene candidates). Several examples are presented to illustrate the potential application of proteomics to the field of genetic epidemiology, and we conclude with various considerations regarding design and analysis.

Breast Neoplasms↗
PubMed2006

Effects of body weight and nutrition on mammary protein expression profiles in Holstein heifers.

A proteomics approach was used to characterize biochemical and cellular mechanisms governing effects of peripubertal feeding on heifer mammary development. Mammary parenchymal tissue from 24 Holstein heifers randomly assigned to treatments arranged in a 2 x 2 factorial design was used to generate 2-dimensional protein maps of mammary tissue extracts. Heifers were reared on 1 of 2 dietary treatments, restricted (650 g/ d of daily gain) or elevated (950 g/d of daily gain) and killed at 1 of 2 body weights (BW, 200 or 350 kg). Cytosolic mammary gland extracts were prepared from frozen mammary parenchyma. Proteome maps of extracts were constructed using PDQuest software. Densities of 820 protein spots were analyzed using the MIXED procedure of SAS. Protein spots were characterized by changes in profiles of expression in response to increased BW, dietary treatment, or both. Dietary treatment influenced the expression of 131 protein spots, whereas heifer BW influenced the expression of 108 spots. The 22 most highly influenced (statistically) spots were excised and submitted for mass spectrometric analyses. Returned protein names and accession numbers were used in National Center for Biotechnology Information database searches to obtain information on the identified proteins. For example, one of the proteins that differed by dietary treatment, transferrin, a binding protein of insulin-like growth factor binding protein-3, was identified via these methods. Possible roles of this and other proteins in mammary development are described. We concluded that a proteomic approach is an effective tool for identifying the proteins involved in bovine mammary development.

Animal Nutritional Physiological Phenomena↗
PubMed1997

Toward a functional analysis of the yeast genome through exhaustive two-hybrid screens.

The genome of the yeast Saccharomyces cerevisiae is now completely sequenced. Despite successful genetic work in recent years, 60% of yeast genes have no assigned function and half of those encode putative proteins without any homology with known proteins. Genetic analyses, such as suppressor or synthetic lethal screens, have suggested many functional links between gene products, some of which have been confirmed by biochemical means. Altogether, these approaches have led to a fairly extensive knowledge of defined biochemical pathways. However, the integration of these pathways against the background of complexity in a living cell remains to be accomplished. The two-hybrid method applied to the yeast genome might allow the characterization to the network of interactions between yeast proteins, leading to a better understanding of cellular functions. Such an analysis has been performed for the bacteriophage T7 genome that encodes 55 proteins and for Drosophila cell cycle regulators. However, the currently available two-hybrid methodology is not suitable for a large-scale project without specific methodological improvements In particular, the exhaustivity and selectivity of the screens must first be greatly improved. We constructed a new yeast genomic library and developed a highly selective two-hybrid procedure adapted for exhaustive screens of the yeast genome. For each bait we selected a limited set of interacting preys that we classified in categories of distinct heuristic values. Taking into account this classification, new baits were chosen among preys and, in turn, used for second-round screens. Repeating this procedure several times led to the characterization of the network of interactions. Using known pre-mRNA splicing factors as initial baits, we were able to characterize new interactions between known splicing factors, identify new yeast splicing factors, including homologues of human SF1 and SAP49, and reveal novel potential functional links between cellular pathways. Using different cellular pathways as anchor points, this novel strategy allows us to envision the building of an interaction map of the yeast proteome. In addition, this two-hybrid strategy could be applied to other genomes and might help to resolve the human protein linkage map.

Amino Acid Sequence↗
PubMed2005

Informatics for protein identification by mass spectrometry.

High throughput protein analysis (i.e., proteomics) first became possible when sensitive peptide mass mapping techniques were developed, thereby allowing for the possibility of identifying and cataloging most 2D gel electrophoresis spots. Shortly thereafter a few groups pioneered the idea of identifying proteins by using peptide tandem mass spectra to search protein sequence databases. Hence, it became possible to identify proteins from very complex mixtures. One drawback to these latter techniques is that it is not entirely straightforward to make matches using tandem mass spectra of peptides that are modified or have sequences that differ slightly from what is present in the sequence database that is being searched. This has been part of the motivation behind automated de novo sequencing programs that attempt to derive a peptide sequence regardless of its presence in a sequence database. The sequence candidates thus generated are then subjected to homology-based database search programs (e.g., BLAST or FASTA). These homology search programs, however, were not developed with mass spectrometry in mind, and it became necessary to make minor modifications such that mass spectrometric ambiguities can be taken into account when comparing query and database sequences. Finally, this review will discuss the important issue of validating protein identifications. All of the search programs will produce a top ranked answer; however, only the credulous are willing to accept them carte blanche.

Amino Acid Sequence↗
PubMed2003

The proteome: anno Domini 2002.

We present some current definitions related to functional and structural proteomics and the human proteome, and we review the following aspects of proteome analysis: Classical 2-D map analysis (isoelectric focusing (IEF) followed by SDS-PAGE); Quantitative proteomics (isotope-coded affinity tag (ICAT), fluorescent stains) and their use in e.g., tumor analysis and identification of new target proteins for drug development; Electrophoretic pre-fractionation (how to see the hidden proteome!); Multidimensional separations, such as: (a) coupled size-exclusion and reverse-phase (RP)-HPLC; (b) coupled ion-exchange and RP-HPLC; (c) coupled RP-HPLC and RP-HPLC at 25/60 degrees C; (d) coupled RP-HPLC and capillary electrophoresis (CE); (e) metal affinity chromatography coupled with CE; Protein chips. Some general conclusions are drawn on proteome analysis and we end this review by trying to decode the glass ball of the aruspex and answer the question: "Quo vadis, proteome"?

Animals↗
PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗
PubMed2003

Proteome analysis on an early transformed human bronchial epithelial cell line, BEP2D, after alpha-particle irradiation.

To probe the mechanism of carcinogenesis of lung cancer at the molecular level and to find potential protein markers involved in the early phase of tumorgenesis, differential proteome analysis on primary passage cell line R15H, and early transformed cell line R15H20 derived from (238)Pu alpha-particle irradiation of human papillomavirus (HPV) 18-immortalized human bronchial epithelial cell line (BEP2D), was carried out using two-dimensional electrophoresis (2-DE) and peptide mass fingerprinting (PMF) with matrix-assisted laser desorption/ionisation-time of flight mass spectrometry. Image analysis and Student's t-test (p < 0.05) showed that three protein spots were only expressed in R15H, intensities of 43 protein spots on the gels were altered between R15H and R15H20. Two of the three spots that were only expressed in R15H were identified as high mobility group protein 1. Two proteins decreased in abundance in R15H20 were identified as maspin precursor, a tumor suppressor and aminoacylase-1. Ornithine aminotransferase and peptidyl-prolyl cis-trans isomerase A that were increased in R15H20, were also identified. Relationships between these differentially expressed proteins and the carcinogenesis mechanism of lung cancer are discussed. The protein expression profile of the R15H cell line was also constructed during the study as a reference map for further comparative proteome analysis of the irradiation induced BEP2D cell line. Of the 90 spots analyzed with PMF in the 2-DE gel of R15H cell line, 50 proteins were identified by searching the nonredundant protein database SWISS-PROT/TrEMBL.

Alpha Particles↗
PubMedDate not supplied

Proteome of Haemophilus ducreyi by 2-D SDS-PAGE and mass spectrometry: strain variation, virulence, and carbohydrate expression.

We have analyzed the proteome of several strains of Haemophilus ducreyi by two-dimensional gel electrophoresis (2-DE) and mass spectrometry. Over 100 spots were analyzed from the soluble and insoluble protein fractions from the prototype strain 35000HP and 122 distinct proteins were identified. Functions of approximately 80% of the 122 proteins were deduced by identification with close homologues of Haemophilus influenzae. Four additional wild type and three mutant strains were also analyzed that vary in their virulence and/or outer-membrane lipooligosaccharide structures. Overall, the 2-DE gel maps of the wild type and mutant strains were similar to strain 35000HP, suggesting little proteome diversity in relation to carbohydrate expression and/or virulence. An exception was the Kenyan strain 33921 which contained significant differences in its proteome 2-DE map and also synthesizes an unusual LOS with a trisaccharide branch structure. This African strain may represent a prototype of a second clonal group of H. ducreyi.

Carbohydrate Metabolism↗
PubMed2026

Proteomics-Driven Strategies for Proximity-Inducing Drug Discovery.

In recent years, proximity-inducing drugs have emerged as a novel therapeutic modality that induces or stabilizes protein-protein interactions, especially by recruiting effector proteins to specific target proteins, thereby achieving functions beyond traditional inhibitors. The potential of proximity-inducing drugs extends beyond targeted protein degradation (TPD), as studies have demonstrated their ability to regulate biological processes such as signal transduction, gene transcription, chromatin regulation, and protein trafficking by modulating protein interaction networks. Rational discovery of proximity-inducing drugs requires clarifying their effects on protein-protein interactions, determining drug selectivity, and developing suitable ligands for drug construction. Proteomics has become a central technology in drug discovery, enabling global identification of the direct drug targets and systematic characterization of proteome-wide downstream responses. This provides a more refined map of drug mechanisms. In parallel, advances in machine learning applied to proteomic data, together with the expansion of proteome-wide ligandability maps, are further accelerating the discovery and optimization of proximity-inducing drugs. This review summarizes recent advances of proximity-inducing drugs, with a particular emphasis on how proteomics facilitates target space expansion, drug efficacy optimization, and ligandability discovery, alongside the emerging contributions of machine learning. Collectively, these insights aim to support the rational development of next-generation proximity-inducing drugs.

Drug Discovery↗