MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform.

Summary: Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments. Availability and Implementation: MAPPI-DAT is developed in Python, using R for data analysis and MySQL as data management system. MAPPI-DAT is cross-platform and can be ran on Microsoft Windows, Linux and OS X/macOS. The source code and a Microsoft Windows executable are freely available under the permissive Apache2 open source license at https://github.com/compomics/MAPPI-DAT. Contact: jan.tavernier@vib-ugent.be or lennart.martens@vib-ugent.be. Supplementary information: Supplementary data are available at Bioinformatics online.

MS2PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation.

We present an MS(2) peak intensity prediction server that computes MS(2) charge 2+ and 3+ spectra from peptide sequences for the most common fragment ions. The server integrates the Unimod public domain post-translational modification database for modified peptides. The prediction model is an improvement of the previously published MS(2)PIP model for Orbitrap-LTQ CID spectra. Predicted MS(2) spectra can be downloaded as a spectrum file and can be visualized in the browser for comparisons with observations. In addition, we added prediction models for HCD fragmentation (Q-Exactive Orbitrap) and show that these models compute accurate intensity predictions on par with CID performance. We also show that training prediction models for CID and HCD separately improves the accuracy for each fragmentation method. The MS(2)PIP prediction server is accessible from http://iomics.ugent.be/ms2pip.

The iceLogo web server and SOAP service for determining protein consensus sequences.

The iceLogo web server and SOAP service implement the previously published iceLogo algorithm. iceLogo builds on probability theory to visualize protein consensus sequences in a format resembling sequence logos. Peptide sequences are compared against a reference sequence set that can be tailored to the studied system and the used protocol. As such, not only over- but also underrepresented residues can be visualized in a statistically sound manner, which further allows the user to easily analyse and interpret conserved sequence patterns in proteins. The web application and SOAP service can be found free and open to all users without the need for a login on http://iomics.ugent.be/icelogoserver/main.html.

Pladipus Enables Universal Distributed Computing in Proteomics Bioinformatics.

The use of proteomics bioinformatics substantially contributes to an improved understanding of proteomes, but this novel and in-depth knowledge comes at the cost of increased computational complexity. Parallelization across multiple computers, a strategy termed distributed computing, can be used to handle this increased complexity; however, setting up and maintaining a distributed computing infrastructure requires resources and skills that are not readily available to most research groups. Here we propose a free and open-source framework named Pladipus that greatly facilitates the establishment of distributed computing networks for proteomics bioinformatics tools. Pladipus is straightforward to install and operate thanks to its user-friendly graphical interface, allowing complex bioinformatics tasks to be run easily on a network instead of a single computer. As a result, any researcher can benefit from the increased computational efficiency provided by distributed computing, hence empowering them to tackle more complex bioinformatics challenges. Notably, it enables any research group to perform large-scale reprocessing of publicly available proteomics data, thus supporting the scientific community in mining these data for novel discoveries.

Up-to-Date Workflow for Plant (Phospho)proteomics Identifies Differential Drought-Responsive Phosphorylation Events in Maize Leaves.

Protein phosphorylation is one of the most common post-translational modifications (PTMs), which can regulate protein activity and localization as well as protein-protein interactions in numerous cellular processes. Phosphopeptide enrichment techniques enable plant researchers to acquire insight into phosphorylation-controlled signaling networks in various plant species. Most phosphoproteome analyses of plant samples still involve stable isotope labeling, peptide fractionation, and demand a lot of mass spectrometry (MS) time. Here, we present a simple workflow to probe, map, and catalogue plant phosphoproteomes, requiring relatively low amounts of starting material, no labeling, no fractionation, and no excessive analysis time. Following optimization of the different experimental steps on Arabidopsis thaliana samples, we transferred our workflow to maize, a major monocot crop, to study signaling upon drought stress. In addition, we included normalization to protein abundance to identify true phosphorylation changes. Overall, we identified a set of new phosphosites in both Arabidopsis thaliana and maize, some of which are differentially phosphorylated upon drought. All data are available via ProteomeXchange with identifier PXD003634, but to provide easy access to our model plant and crop data sets, we created an online database, Plant PTM Viewer ( bioinformatics.psb.ugent.be/webtools/ptm_viewer/ ), where all phosphosites identified in our study can be consulted.

A decoy-free approach to the identification of peptides.

A growing number of proteogenomics and metaproteomics studies indicate potential limitations of the application of the "decoy" database paradigm used to separate correct peptide identifications from incorrect ones in traditional shotgun proteomics. We therefore propose a binary classifier called Nokoi that allows fast yet reliable decoy-free separation of correct from incorrect peptide-to-spectrum matches (PSMs). Nokoi was trained on a very large collection of heterogeneous data using ranks supplied by the Mascot search engine to label correct and incorrect PSMs. We show that Nokoi outperforms Mascot and achieves a performance very close to that of Percolator at substantially higher processing speeds.

PepShell: visualization of conformational proteomics data.

Proteins are dynamic molecules; they undergo crucial conformational changes induced by post-translational modifications and by binding of cofactors or other molecules. The characterization of these conformational changes and their relation to protein function is a central goal of structural biology. Unfortunately, most conventional methods to obtain structural information do not provide information on protein dynamics. Therefore, mass spectrometry-based approaches, such as limited proteolysis, hydrogen-deuterium exchange, and stable-isotope labeling, are frequently used to characterize protein conformation and dynamics, yet the interpretation of these data can be cumbersome and time consuming. Here, we present PepShell, a tool that allows interactive data analysis of mass spectrometry-based conformational proteomics studies by visualization of the identified peptides both at the sequence and structure levels. Moreover, PepShell allows the comparison of experiments under different conditions, including different proteolysis times or binding of the protein to different substrates or inhibitors.

The Online Protein Processing Resource (TOPPR): a database and analysis platform for protein processing events.

We here present The Online Protein Processing Resource (TOPPR; http://iomics.ugent.be/toppr/), an online database that contains thousands of published proteolytically processed sites in human and mouse proteins. These cleavage events were identified with COmbinded FRActional DIagonal Chromatography proteomics technologies, and the resulting database is provided with full data provenance. Indeed, TOPPR provides an interactive visual display of the actual fragmentation mass spectrum that led to each identification of a reported processed site, complete with fragment ion annotations and search engine scores. Apart from warehousing and disseminating these data in an intuitive manner, TOPPR also provides an online analysis platform, including methods to analyze protease specificity and substrate-centric analyses. Concretely, TOPPR supports three ways to retrieve data: (i) the retrieval of all substrates for one or more cellular stimuli or assays; (ii) a substrate search by UniProtKB/Swiss-Prot accession number, entry name or description; and (iii) a motif search that retrieves substrates matching a user-defined protease specificity profile. The analysis of the substrates is supported through the presence of a variety of annotations, including predicted secondary structure, known domains and experimentally obtained 3D structure where available. Across substrates, substrate orthologs and conserved sequence stretches can also be shown, with iceLogo visualization provided for the latter.

Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit.

While transcriptome- and proteome-wide technologies to assess processes in protein biogenesis are now widely available, we still lack global approaches to assay post-ribosomal biogenesis events, in particular those occurring in the eukaryotic secretory system. We here develop a method, SECRiFY, to simultaneously assess the secretability of >105 protein fragments by two yeast species, S. cerevisiae and P. pastoris, using custom fragment libraries, surface display and a sequencing-based readout. Screening human proteome fragments with a median size of 50-100 amino acids, we generate datasets that enable datamining into protein features underlying secretability, revealing a striking role for intrinsic disorder and chain flexibility. The SECRiFY methodology generates sufficient amounts of annotated data for advanced machine learning methods to deduce secretability patterns. The finding that secretability is indeed a learnable feature of protein sequences provides a solid base for application-focused studies.