Comparative phosphoproteomics reveals evolutionary and functional conservation of phosphorylation across eukaryotes

A comparison of phosphoproteomics datasets of six eukaryotes shows significant overlap between phosphoproteomes

Інформаційно-аналітична підтримка прийняття управлінських рішень

Розглянуто питання інформаційно-аналітичної підтримки прийняття управлінських рішень, застосування сучасних інформаційних технологій і організації інформаційного забезпечення аналітичної обробки інформації у корпоративних організаціях.Рассмотрены вопросы информационно-аналитической поддержки принятия управленческих решений, применения современных информационных технологий и организации информационного обеспечения аналитической обработки информации в корпоративных организациях.The questions of information-analytical support of administrative decision-making, application of modern information technologies and organization of information maintenance of analytical processing of the information in corporate organizations are considered

Proteogenomic Features of the Highly Polymorphic Histidine-rich Glycoprotein Arose Late in Evolution

Histidine-rich glycoprotein (HRG) is a liver-produced protein circulating in human serum at high concentrations of around 125 μg/ml. HRG belongs to the family of type-3 cystatins and has been implicated in a plethora of biological processes, albeit that its precise function is still not well understood. Human HRG is a highly polymorphic protein, with at least five variants with minor allele frequencies of more than 10%, variable in populations from different parts of the world. Considering these five mutations we can theoretically expect 35 = 243 possible genetic HRG variants in the population. Here, we purified HRG from serum of 44 individual donors and investigated by proteomics the occurrence of different allotypes, each being either homozygote or heterozygote for each of the five mutation sites. We observed that some mutational combinations in HRG were highly favored, while others were apparently missing, although they ought to be present based on the independent assembly of these five mutation sites. To further explore this behavior, we extracted data from the 1000 genome project (n ∼ 2500 genomes) and assessed the frequency of different HRG mutants in this larger dataset, observing a prevailing agreement with our proteomics data. From all the proteogenomic data we conclude that the five different mutation sites in HRG are not occurring independently, but several mutations at different sites are fully mutually exclusive, whereas others are highly intwined. Specific mutations do also affect HRG glycosylation. As the levels of HRG have been suggested as a protein biomarker in a variety of biological processes (e.g., aging, COVID-19 severity, severity of bacterial infections), we here conclude that the highly polymorphic nature of the protein needs to be considered in such proteomics evaluations, as these mutations may affect HRG's abundance, structure, posttranslational modifications, and function

The PRoteomics IDEntification (PRIDE) Converter 2 Framework: An Improved Suite of Tools to Facilitate Data Submission to the PRIDE Database and the ProteomeXchange Consortium

The original PRIDE Converter tool greatly simplified the process of submitting mass spectrometry (MS)-based proteomics data to the PRIDE database. However, after much user feedback, it was noted that the tool had some limitations and could not handle several user requirements that were now becoming commonplace. This prompted us to design and implement a whole new suite of tools that would build on the successes of the original PRIDE Converter and allow users to generate submission-ready, well-annotated PRIDE XML files. The PRIDE Converter 2 tool suite allows users to convert search result files into PRIDE XML (the format needed for performing submissions to the PRIDE database), generate mzTab skeleton files that can be used as a basis to submit quantitative and gel-based MS data, and post-process PRIDE XML files by filtering out contaminants and empty spectra, or by merging several PRIDE XML files together. All the tools have both a graphical user interface that provides a dialog-based, user-friendly way to convert and prepare files for submission, as well as a command-line interface that can be used to integrate the tools into existing or novel pipelines, for batch processing and power users. The PRIDE Converter 2 tool suite will thus become a cornerstone in the submission process to PRIDE and, by extension, to the ProteomeXchange consortium of MS-proteomics data repositories.publishedVersio

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Perturbation of the yeast N-acetyltransferase NatB induces elevation of protein phosphorylation levels

<p>Abstract</p> <p>Background</p> <p>The addition of an acetyl group to protein N-termini is a widespread co-translational modification. NatB is one of the main N-acetyltransferases that targets a subset of proteins possessing an N-terminal methionine, but so far only a handful of substrates have been reported. Using a yeast <it>nat3Δ </it>strain, deficient for the catalytic subunit of NatB, we employed a quantitative proteomics strategy to identify NatB substrates and to characterize downstream effects in <it>nat3Δ</it>.</p> <p>Results</p> <p>Comparing by proteomics WT and <it>nat3Δ </it>strains, using metabolic ¹⁵N isotope labeling, we confidently identified 59 NatB substrates, out of a total of 756 detected acetylated protein N-termini. We acquired in-depth proteome wide measurements of expression levels of about 2580 proteins. Most remarkably, NatB deletion led to a very significant change in protein phosphorylation.</p> <p>Conclusions</p> <p>Protein expression levels change only marginally in between WT and <it>nat3Δ</it>. A comparison of the detected NatB substrates with their orthologous revealed remarkably little conservation throughout the phylogenetic tree. We further present evidence of post-translational N-acetylation on protein variants at non-annotated N-termini. Moreover, analysis of downstream effects in <it>nat3Δ </it>revealed elevated protein phosphorylation levels whereby the kinase Snf1p is likely a key element in this process.</p

qcML: an exchange format for quality control metrics from mass spectrometry experiments.

Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml

Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy

Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein–protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein–protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein–protein interactions and a unique perspective on possible novel protein complexes

Facilitating protein disulfide mapping by a combination of pepsin digestion, electron transfer higher energy dissociation (EThcD), and a dedicated search algorithm SlinkS

Disulfide bond identification is important for a detailed understanding of protein structures, which directly affect their biological functions. Here we describe an integrated workflow for the fast and accurate identification of authentic protein disulfide bridges. This novel workflow incorporates acidic proteolytic digestion using pepsin to eliminate undesirable disulfide reshuffling during sample preparation and a novel search engine, SlinkS, to directly identify disulfide-bridged peptides isolated via electron transfer higher energy dissociation (EThcD). In EThcD fragmentation of disulfide-bridged peptides, electron transfer dissociation preferentially leads to the cleavage of the S-S bonds, generating two intense disulfide-cleaved peptides as primary fragment ions. Subsequently, higher energy collision dissociation primarily targets unreacted and charge-reduced precursor ions, inducing peptide backbone fragmentation. SlinkS is able to provide the accurate monoisotopic precursor masses of the two disulfide-cleaved peptides and the sequence of each linked peptide by matching the remaining EThcD product ions against a linear peptide database. The workflow was validated using a protein mixture containing six proteins rich in natural disulfide bridges. Using this pepsin-based workflow, we were able to efficiently and confidently identify a total of 31 unique Cys-Cys bonds (out of 43 disulfide bridges present), with no disulfide reshuffling products detected. Pepsin digestion not only outperformed trypsin digestion in terms of the number of detected authentic Cys-Cys bonds, but, more important, prevented the formation of artificially reshuffled disulfide bridges due to protein digestion under neutral pH. Our new workflow therefore provides a precise and generic approach for disulfide bridge mapping, which can be used to study protein folding, structure, and stability