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To evaluate the time for recovery of the sense of smell in patients with CRSwNP who underwent Reboot surgery compared to patients undergoing ESS in a long-term follow-up study.Data were collected retrospectively from 168 patients with severe uncontrolled CRSwNP, who underwent revision surgery, either as Extended Endoscopic Sinus Surgery (Reboot, 140 patients) or as regular Endoscopic Sinus Surgery (ESS, 28 patients) between January 1, 2014, and December 31, 2015, aiming to compare the outcome of surgeries after 2 years of follow-up. Sense of smell was scored as judged by the patient using scores 0 to 3 reflecting a percentage estimate of remaining smell.Smell improved similarly in the Reboot and ESS groups over the first 9 months, which was maintained over 24 months in the Reboot, but not the ESS group (p = 0.007 after 18 months, p = 0.001 after 24 months). Furthermore, polyp recurrence rates were significantly lower in the Reboot group.Reboot surgery significantly improved olfactory function and significantly reduced nasal polyp recurrence rates over 2 years post-operatively. Therefore, Reboot should be considered for patients with uncontrolled severe CRSwNP, specifically when ESS failed, to offer long-term smell and a polyp-free status.3b.

MotivationWith 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 phosphorylation sites are desirable to guide functional experiments. Currently, the most successful methods train neural networks on amino acid composition representations. However, recently proposed protein language models provide enriched sequence representations that contain higher-level pattern information on which more performant phosphorylation site predictions may be based.ResultsWe 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 up to 68.4% in terms of area under the precision-recall curve over the state-of-the-art predictors. Model interpretation and model transferability experiments reveal that protease-specific cleavage patterns give rise to a protease-specific training bias. This can result 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. We show that improving data quality by negative sample filtering using experimental metadata can mitigate this problem.Availability and implementationThe PhosphoLingo tool, with trained models, code, models, datasets, and predictions are available athttps://github.com/jasperzuallaert/PhosphoLingo.Contactsven.degroeve@vib-ugent.beSupplementary informationSupplementary materials are available atbioRxiv.

Adult expression of IMP2 is often associated with several types of disease and cancer. The RNA binding protein IMP2 binds and stabilizes the IGF2 mRNA as well as hundreds of other transcripts during development. To gain insight into the molecular action of IMP2 and its contribution to disease in context of adult cellular metabolism, we analyze transgenic overexpression of IMP2 in mouse livers, which has been shown to induce a steatosis-like phenotype and enhanced risk to develop hepatocellular carcinoma (HCC). Our data show up-regulation of several HCC marker genes and miRNAs (miR438-3p and miR151-5p). To characterize the impact of miRNAs to their targets, integrative analysis of transcriptome-and miRNAome-dynamics in combination with IMP2 target prediction was carried out. Our analyses show that targets of expressed miRNAs become accumulated in the case that these transcripts have positive IMP2 binding prediction. Therefore, our data indicates that overexpression of IMP2 alters the regulatory capacity of many miRNAs and we conclude that IMP2 competes with miRNAs for binding sites on thousands of transcripts. As a result, our data implicates that overexpression of IMP2 has distinct effects to the regulatory capacity of miRNAs with yet unknown consequences for translational efficiency.

Protein phosphorylation is the most common post-translational reversible modification of proteins and is key in the regulation of many cellular processes. Due to this importance, phosphorylation is extensively studied, resulting in the availability of a large amount of mass spectrometry based phospho-proteomics data. Here, we leverage the information in these large-scale phospho-proteomics datasets, as contained in Scop3P, to analyze and characterize proteome-wide protein phosphorylation sites (P-sites). First, we set out to differentiate correctly observed P-sites from false positive sites using five complementary site properties. We then describe the context of these P-sites in terms of protein structure, solvent accessibility, structural transitions and disorder, and biophysical properties. We also investigate the relative prevalence of disease-linked mutations on and around P-sites. Moreover, we also assess structural dynamics of P-sites in their phosphorylated and unphosphorylated state. Our study shows that the residues that gets phosphorylated are more flexible than their equivalent non-phosphorylated residues. Our structural and biophysical analyses of P-sites in solvent inaccessible (buried) regions of proteins show that these sites are primarily found in multi-site phospho-proteins, where highly dynamic structural transitions can occur upon binding with another protein. Finally, our analysis of the biophysical properties of P-site mutations shows that P-site mutations that occur in structurally rigid regions are more often involved in disease.

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., ELIXIR [IDP implementation study]; Biotechnology and Biological Sciences Research Council via the 3D-Gateway [BB/T01959X/1]; FunPDBe [BB/P024351/1]; European Molecular Biology Laboratory-European Bioinformatics Institute who supported this work; J.D. acknowledges support from the Ministry of Education, Youth and Sport of the Czech Republic [INBIO CZ.02.1.01/0.0/0.0/16_026/0008451]; R.S., K.B. and J.D. also acknowledge support from the Ministry of Education, Youth and Sport of the Czech Republic [ELIXIR-CZ LM2018131]; L.M. acknowledges support from the European Union's Horizon 2020 Programme (H2020-INFRAIA-2018-1) [823839]; Research Foundation Flanders (FWO) [G032816N, G042518N, G028821N]; W.V. acknowledges support from the Research Foundation Flanders (FWO) [G032816N, G028821N]; A.R. acknowledges support from the Fondazione Cassa Di Risparmio di Firenze [24316]; European Commission [101017567]; M.H.C. acknowledges the AIRC project to MHC [IG 23539]; J.F.-R. acknowledges support from the Spanish Ministry of Science and Innovation [PID2019-110167RB-I00]; N.R. acknowledges support from the Norwegian Research Council (Norges Forskningsråd) [288008]; E.D.L. acknowledges support from the European Union's Horizon 2020 research and innovation programme [819318]; M.J.E.S. acknowledges support from the Wellcome Trust [104955/Z/14/Z, 218242/Z/19/Z]. Funding for open access charge: Biotechnology and Biological Sciences Research Council grant [BB/T01959X/1]; Wellcome Trust [104955/Z/14/Z and 218242/Z/19/Z].

We provide integrated protein sequence-based predictions via https://bio2byte.be/b2btools/. The aim of our predictions is to identify the biophysical behaviour or features of proteins that are not readily captured by structural biology and/or molecular dynamics approaches. Upload of a FASTA file or text input of a sequence provides integrated predictions from DynaMine backbone and side-chain dynamics, conformational propensities, and derived EFoldMine early folding, DisoMine disorder, and Agmata β-sheet aggregation. These predictions, several of which were previously not available online, capture 'emergent' properties of proteins, i.e. the inherent biophysical propensities encoded in their sequence, rather than context-dependent behaviour (e.g. final folded state). In addition, upload of a multiple sequence alignment (MSA) in a variety of formats enables exploration of the biophysical variation observed in homologous proteins. The associated plots indicate the biophysical limits of functionally relevant protein behaviour, with unusual residues flagged by a Gaussian mixture model analysis. The prediction results are available as JSON or CSV files and directly accessible via an API. Online visualisation is available as interactive plots, with brief explanations and tutorial pages included. The server and API employ an email-free token-based system that can be used to anonymously access previously generated results., SCOPUS: ar.j, info:eu-repo/semantics/published

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. ispartof: JACS AU vol:1 issue:6 pages:750-765 ispartof: location:United States status: published

Background: The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. Main: We present a website (https://bio2byte.be/sars2/) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, β-sheet aggregation, protein-protein interaction and epitope propensities. These predictions attempt to capture the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide the biophysical variation that is observed in homologous proteins, which gives an indication of the limits of their functionally relevant biophysical behaviour. Conclusion: The https://bio2byte.be/sars2/ website provides a range of protein sequence-based predictions for 27 SARS-CoV-2 proteins, enabling researchers to form hypotheses about their possible functional modes of action., SCOPUS: ar.j, info:eu-repo/semantics/published

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., SCOPUS: ar.j, info:eu-repo/semantics/published

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 IP3. 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.

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.

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., The exact protein features that control passage through the eukaryotic secretory system remain largely unknown. Here the authors report SECRiFY which they use to evaluate the secretory potential of polypeptides on a proteome-wide scale in yeast, revealing a role for flexibility and intrinsic disorder.

Additional file 1: Supplementary data. Contains two additional Figs. (S1, S2) showing the spread of biophysical predictions based on the multiple sequence alignment (MSA) for the P0DTC9 SARS-CoV-2.

Additional file 1: Supplementary data. Contains two additional Figs. (S1, S2) showing the spread of biophysical predictions based on the multiple sequence alignment (MSA) for the P0DTC9 SARS-CoV-2.