The MobiDB database (URL: https://mobidb.org/) provides predictions and annotations for intrinsically disordered proteins. Here, we report recent developments implemented in MobiDB version 4, regarding the database format, with novel types of annotations and an improved update process. The new website includes a re-designed user interface, a more effective search engine and advanced API for programmatic access. The new database schema gives more flexibility for the users, as well as simplifying the maintenance and updates. In addition, the new entry page provides more visualisation tools including customizable feature viewer and graphs of the residue contact maps. MobiDB v4 annotates the binding modes of disordered proteins, whether they undergo disorder-to-order transitions or remain disordered in the bound state. In addition, disordered regions undergoing liquid-liquid phase separation or post-translational modifications are defined. The integrated information is presented in a simplified interface, which enables faster searches and allows large customized datasets to be downloaded in TSV, Fasta or JSON formats. An alternative advanced interface allows users to drill deeper into features of interest. A new statistics page provides information at database and proteome levels. The new MobiDB version presents state-of-the-art knowledge on disordered proteins and improves data accessibility for both computational and experimental users.

Rising population density and global mobility are among the reasons why pathogens such as SARS-CoV-2, the virus that causes COVID-19, spread so rapidly across the globe. The policy response to such pandemics will always have to include accurate monitoring of the spread, as this provides one of the few alternatives to total lockdown. However, COVID-19 diagnosis is currently performed almost exclusively by reverse transcription polymerase chain reaction (RT-PCR). Although this is efficient, automatable, and acceptably cheap, reliance on one type of technology comes with serious caveats, as illustrated by recurring reagent and test shortages. We therefore developed an alternative diagnostic test that detects proteolytically digested SARS-CoV-2 proteins using mass spectrometry (MS). We established the Cov-MS consortium, consisting of 15 academic laboratories and several industrial partners to increase applicability, accessibility, sensitivity, and robustness of this kind of SARS-CoV-2 detection. This, in turn, gave rise to the Cov-MS Digital Incubator that allows other laboratories to join the effort, navigate, and share their optimizations and translate the assay into their clinic. As this test relies on viral proteins instead of RNA, it provides an orthogonal and complementary approach to RT-PCR using other reagents that are relatively inexpensive and widely available, as well as orthogonally skilled personnel and different instruments. Data are available via ProteomeXchange with identifier PXD022550.

The Protein Data Bank in Europe – Knowledge Base (PDBe-KB, https://pdbe-kb.org) is an open collaboration between world-leading specialist data resources contributing functional and biophysical annotations derived from or relevant to the Protein Data Bank (PDB). The goal of PDBe-KB is to place macromolecular structure data in their biological context by developing standardised data exchange formats and integrating functional annotations from the contributing partner resources into a knowledge graph that can provide valuable biological insights. Since we described PDBe-KB in 2019, there have been significant improvements in the variety of available annotation data sets and user functionality. Here, we provide an overview of the consortium, highlighting the addition of annotations such as predicted covalent binders, phosphorylation sites, effects of mutations on the protein structure and energetic local frustration. In addition, we describe a library of reusable web-based visualisation components and introduce new features such as a bulk download data service and a novel superposition service that generates clusters of superposed protein chains weekly for the whole PDB archive.

Protein phosphorylation is a key post-translational modification (PTM) in many biological processes and is associated to human diseases such as cancer and metabolic disorders. The accurate identification, annotation and functional analysis of phosphosites is therefore crucial to understand their various roles. Phosphosites (P-sites) are mainly analysed through phosphoproteomics, which has led to increasing amounts of publicly available phosphoproteomics data. Several resources have been built around the resulting phosphosite information, but these are usually restricted to protein sequence and basic site metadata. What is often missing from these resources, however, is context, including protein structure mapping, experimental provenance information, and biophysical predictions. We therefore developed Scop3P: a comprehensive database of human phosphosites within their full context. Scop3P integrates sequences (UniProtKB/Swiss-Prot), structures (PDB), and uniformly reprocessed phosphoproteomics data (PRIDE) to annotate all known human phosphosites. Furthermore, these sites are put into biophysical context by annotating each phosphoprotein with perresidue structural propensity, solvent accessibility, disordered probability, and early folding information.Scop3P, available at https://iomics.ugent.be/scop3p, presents a unique resource for visualization and analysis of phosphosites, and for understanding of phosphosite structure-function relationships.

Endosymbiosis is a widespread phenomenon and hosts of bacterial endosymbionts can be found all-over the eukaryotic tree of life. Likely, this evolutionary success is connected to the altered phenotype arising from a symbiotic association. The potential variety of symbiont’s contributions to new characteristics or abilities of host organisms are largely unstudied. Addressing this aspect, we focused on an obligate bacterial endosymbiont that confers an intraspecific killer phenotype to its host. The symbiosis between Paramecium tetraurelia and Caedibacter taeniospiralis, living in the host’s cytoplasm, enables the infected paramecia to release Caedibacter symbionts, which can simultaneously produce a peculiar protein structure and a toxin. The ingestion of bacteria that harbor both components leads to the death of symbiont-free congeners. Thus, the symbiosis provides Caedibacter-infected cells a competitive advantage, the “killer trait.” We characterized the adaptive gene expression patterns in symbiont-harboring Paramecium as a second symbiosis-derived aspect next to the killer phenotype. Comparative transcriptomics of infected P. tetraurelia and genetically identical symbiont-free cells confirmed altered gene expression in the symbiont-bearing line. Our results show up-regulation of specific metabolic and heat shock genes whereas down-regulated genes were involved in signaling pathways and cell cycle regulation. Functional analyses to validate the transcriptomics results demonstrated that the symbiont increases host density hence providing a fitness advantage. Comparative transcriptomics shows gene expression modulation of a ciliate caused by its bacterial endosymbiont thus revealing new adaptive advantages of the symbiosis. Caedibacter taeniospiralis apparently increases its host fitness via manipulation of metabolic pathways and cell cycle control.

Rising population density and global mobility are among the reasons why pathogens such as SARS-CoV-2, the virus that causes COVID-19, spread so rapidly across the globe. The policy response to such pandemics will always have to include accurate monitoring of the spread, as this provides one of the few alternatives to total lockdown. However, COVID-19 diagnosis is currently performed almost exclusively by Reverse Transcription Polymerase Chain Reaction (RT-PCR). Although this is efficient, automatable and acceptably cheap, reliance on one type of technology comes with serious caveats, as illustrated by recurring reagent and test shortages. We therefore developed an alternative diagnostic test that detects proteolytically digested SARS-CoV-2 proteins using Mass Spectrometry (MS). We established the Cov-MS consortium, consisting of fifteen academic labs and several industrial partners to increase applicability, accessibility, sensitivity and robustness of this kind of SARS-CoV-2 detection. This in turn gave rise to the Cov-MS Digital Incubator that allows other labs to join the effort, navigate and share their optimizations, and translate the assay into their clinic. As this test relies on viral proteins instead of RNA, it provides an orthogonal and complementary approach to RT-PCR, using other reagents that are relatively inexpensive and widely available, as well as orthogonally skilled personnel and different instruments. Data are available via ProteomeXchange with identifier PXD022550.

Motivation: With a regulatory impact on numerous biological processes, protein phosphorylation is one of the most studied post-translational modifications. Effective computational methods that provide a sequence-based prediction of probable phosphorylation sites are desirable to guide functional experiments or constrain search spaces for proteomics-based experimental pipelines. Currently, the most successful methods train deep learning models on amino acid composition representations. However, recently proposed protein language models provide enriched sequence representations that may contain higher-level pattern information on which more performant phosphorylation site predictions may be based. Results: We explored the applicability of protein language models to general phosphorylation site prediction. We found that training prediction models on top of protein language models yield a relative improvement of between 13.4% and 63.3% in terms of area under the precision-recall curve over the state-of-the-art predictors. Advanced model interpretation and model transferability experiments reveal that across models, protease-specific cleavage patterns give rise to a protease-specific training bias that results in an overly optimistic estimation of phosphorylation site prediction performance, an important caveat in the application of advanced machine learning approaches to protein modification prediction based on proteomics data.

Phospholipase C (PLC) is an important enzyme of signal transduction pathways by generation of second messengers from membrane lipids. PLCs are also indicated to cleave glycosylphosphatidylinositol (GPI)-anchors of surface proteins thus releasing these into the environment. However, it remains unknown whether this enzymatic activity on the surface is due to distinct PLC isoforms in higher eukaryotes. Ciliates have, in contrast to other unicellular eukaryotes, multiple PLC isoforms as mammals do. Thus, Paramecium represents a perfect model to study subcellular distribution and potential surface activity of PLC isoforms. We have identified distinct subcellular localizations of four PLC isoforms indicating functional specialization. The association with different calcium release channels (CRCs) argues for distinct subcellular functions. They may serve as PI-PLCs in microdomains for local second messenger responses rather than free floating IP. In addition, all isoforms can be found on the cell surface and they are found together with GPI-cleaved surface proteins in salt/ethanol washes of cells. We can moreover show them in medium supernatants of living cells where they have access to GPI-anchored surface proteins. Among the isoforms we cannot assign GPI-PLC activity to specific PLC isoforms; rather each PLC is potentially responsible for the release of GPI-anchored proteins from the surface.