GO Explorer: A gene-ontology tool to aid in the interpretation of shotgun proteomics data

<p>Abstract</p> <p>Background</p> <p>Spectral counting is a shotgun proteomics approach comprising the identification and relative quantitation of thousands of proteins in complex mixtures. However, this strategy generates bewildering amounts of data whose biological interpretation is a challenge.</p> <p>Results</p> <p>Here we present a new algorithm, termed GO Explorer (GOEx), that leverages the gene ontology (GO) to aid in the interpretation of proteomic data. GOEx stands out because it combines data from protein fold changes with GO over-representation statistics to help draw conclusions. Moreover, it is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. Its usefulness is demonstrated by applying it to help interpret the effects of perillyl alcohol, a natural chemotherapeutic agent, on glioblastoma multiform cell lines (A172). We used a new multi-surfactant shotgun proteomic strategy and identified more than 2600 proteins; GOEx pinpointed key sets of differentially expressed proteins related to cell cycle, alcohol catabolism, the Ras pathway, apoptosis, and stress response, to name a few.</p> <p>Conclusion</p> <p>GOEx facilitates organism-specific studies by leveraging GO and providing a rich graphical user interface. It is a simple to use tool, specialized for biologists who wish to analyze spectral counting data from shotgun proteomics. GOEx is available at <url>http://pcarvalho.com/patternlab</url>.</p

The Human Melanoma Proteome Atlas—Complementing the melanoma transcriptome

The MM500 meta‐study aims to establish a knowledge basis of the tumor proteome to serve as a complement to genome and transcriptome studies. Somatic mutations and their effect on the transcriptome have been extensively characterized in melanoma. However, the effects of these genetic changes on the proteomic landscape and the impact on cellular processes in melanoma remain poorly understood. In this study, the quantitative mass‐spectrometry‐based proteomic analysis is interfaced with pathological tumor characterization, and associated with clinical data. The melanoma proteome landscape, obtained by the analysis of 505 well‐annotated melanoma tumor samples, is defined based on almost 16 000 proteins, including mutated proteoforms of driver genes. More than 50 million MS/MS spectra were analyzed, resulting in approximately 13,6 million peptide spectrum matches (PSMs). Altogether 13 176 protein‐coding genes, represented by 366 172 peptides, in addition to 52 000 phosphorylation sites, and 4 400 acetylation sites were successfully annotated. This data covers 65% and 74% of the predicted and identified human proteome, respectively. A high degree of correlation (Pearson, up to 0.54) with the melanoma transcriptome of the TCGA repository, with an overlap of 12 751 gene products, was found. Mapping of the expressed proteins with quantitation, spatiotemporal localization, mutations, splice isoforms, and PTM variants was proven not to be predicted by genome sequencing alone. The melanoma tumor molecular map was complemented by analysis of blood protein expression, including data on proteins regulated after immunotherapy. By adding these key proteomic pillars, the MM500 study expands the knowledge on melanoma disease

It is time for top-down venomics

Abstract The protein composition of animal venoms is usually determined by peptide-centric proteomics approaches (bottom-up proteomics). However, this technique cannot, in most cases, distinguish among toxin proteoforms, herein called toxiforms, because of the protein inference problem. Top-down proteomics (TDP) analyzes intact proteins without digestion and provides high quality data to identify and characterize toxiforms. Denaturing top-down proteomics is the most disseminated subarea of TDP, which performs qualitative and quantitative analyzes of proteoforms up to ~30 kDa in high-throughput and automated fashion. On the other hand, native top-down proteomics provides access to information on large proteins (> 50 kDA) and protein interactions preserving non-covalent bonds and physiological complex stoichiometry. The use of native and denaturing top-down venomics introduced novel and useful techniques to toxinology, allowing an unprecedented characterization of venom proteins and protein complexes at the toxiform level. The collected data contribute to a deep understanding of venom natural history, open new possibilities to study the toxin evolution, and help in the development of better biotherapeutics

Effects of pressure on the structure of metmyoglobin: Molecular dynamics predictions for pressure unfolding through a molten globule intermediate

We investigated the pathway for pressure unfolding of metmyoglobin using molecular dynamics (MD) for a range of pressures (0. 1 MPa to 1. 2 GPa) and a temperature of 300 K. We find that the unfolding of metmyoglobin proceeds via a two‐step mechanism native → molten globule intermediate → unfolded, where the molten globule forms at 700 MPa. The simulation describes qualitatively the experimental behavior of metmyoglobin under pressure. We find that unfolding of the alpha‐helices follows the sequence of migrating hydrogen bonds (i, i + 4) → (i, i + 2)

Proteômica e sepse: novas perspectivas para o diagnóstico Proteomics and sepsis: new perspectives for diagnosis

JUSTIFICATIVA E OBJETIVOS: O diagnóstico e o tratamento da sepse continuam a desafiar a todos; e desenvolver formas mais precisas de abordagem são absolutamente necessárias. O objetivo deste estudo foi empregar técnicas proteômicas, eletroforese bidimensional e espectrometria de massa, para verificar a expressão diferencial de proteínas, em soro de pacientes com sepse comparado com controles saudáveis. MÉTODO: Amostras de soro de 30 pacientes com sepse, causada por vários tipos de microorganismos e de 30 controles saudáveis foram obtidas para análise. A seguir, foram submetidas a 2D-SDS-PAGE, comparação entre géis, seleção de spots para excisão e digestão com tripsina, sendo os peptídeos analisados por MALDI TOF-TOF. Os espectros obtidos foram processados (Mascot-matrixscience) para identificação de proteínas no NCBInr Data Bank. RESULTADOS: A análise das imagens mostrou vários spots com expressão diferencial nos géis dos pacientes com sepse em relação aos controles. A identificação de proteínas em alguns destes spots encontrou: precursor Orosomucoide 1, Apolipoproteína A-IV, precursor Apolipoproteína A-IV, precursor Haptoglobina, Haptoglobina, proteína Zinc finger, Amilóide sérico A-1, Transtiretina, Nebulin, Complemento C4, Alfa1-Antitripsina, produto protéico não nominado e outros. CONCLUSÕES: Soros de pacientes com diferentes tipos de sepse expressam padrão protéico característico por 2D-SDS-PAGE comparado com controles. A maior expressão foi de proteínas de fase aguda e lipoproteínas. É possível que no futuro, com a proteômica, criar painel diagnóstico de proteínas, encontrar novos biomarcadores e alvos para intervenção terapêutica na sepse. Esta é a primeira descrição, com a proteômica, das alterações na expressão protéica, no soro de pacientes com sepse.<br>BACKGROUND AND OBJECTIVES: The diagnostic and treatment of sepsis continue to challenger all, and, more specific forms to approach are absolutely necessary. The objective of this study was to use proteomics techniques, two-dimensional electrophoresis and mass spectrometry, to verify the differential protein expression between serum of patients with sepsis and health controls. METHODS: Samples of serum the 30 patients with sepsis, caused for different types of microorganisms and serum of 30 health controls were obtained for analysis. Next, were submitted to 2D-SDS-PAGE, gels compared, selection of spots for excision and digestion with trypsin, being the peptides analyzed for MALDI TOF-TOF. The obtained spectrums were processed (Mascot-matrix science) for protein identification in NCBInr Data Bank. RESULTS: Image analyses showed several spots with differential expressions in the gels of the patients with sepsis in relation to the controls. The protein identification of some of these spots founded: Orosomucoid 1 precursor, Apolipoprotein A-IV, Apolipoprotein A-IV precursor, Haptoglobin protein precursor, Haptoglobin, Zinc finger protein, Serum amyloid A-1, Transthyretin, Nebulin, Complement C4, Alpha1-Antitrypsin, Unnamed protein product and others. CONCLUSIONS: Serum of the patients with different types of sepsis express characteristic protein profiles by 2D-SDS-PAGE compared with controls. The most expressed were from acute phase proteins and lipoproteins. It is possible in the future, with proteomics, create diagnostic panel of proteins, finding news biomarkers and targets for therapeutic interventions in sepsis. This is a first description, with proteomics, of the alterations in protein expression, in serum of the patients with sepsis

Quantitative proteomic analysis identifies proteins and pathways related to neuronal development in differentiated SH-SY5Y neuroblastoma cells

SH-SY5Y neuroblastoma cells are susceptible to differentiation using retinoic acid (RA) and brain-derived neurotrophic factor (BDNF), providing a model of neuronal differentiation. We compared SH-SY5Y cells proteome before and after RA/BDNF treatment using iTRAQ and phosphopeptide enrichment strategies. We identified 5587 proteins, 366 of them with differential abundance. Differentiated cells expressed proteins related to neuronal development, and, undifferentiated cells expressed proteins involved in cell proliferation. Interactive network covered focal adhesion, cytoskeleton dynamics and neurodegenerative diseases processes and regulation of mitogen-activated protein kinase-related signaling pathways; key proteins involved in those processes might be explored as markers for neuronal differentiation