DisCanVis: Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in a wide range of biological processes and have been associated with various diseases, including cancer. In the last few years, cancer genome projects have systematically collected genetic variations underlying multiple cancer types. In parallel, the number and different types of disordered proteins characterized by experimental methods have also significantly increased. Nevertheless, the role of IDPs in various types of cancer is still not well understood. In this work, we present DisCanVis, a novel visualization tool for cancer mutations with a special focus on IDPs. In order to aid the interpretation of observed mutations, genome level information is combined with information about the structural and functional properties of proteins. The web server enables users to inspect individual proteins, collect examples with existing annotations of protein disorder and associated function or to discover currently uncharacterized examples with likely disease relevance. Through a REST API interface and precompiled tables the analysis can be extended to a group of proteins

The interaction between LC8 and LCA5 reveals a novel oligomerization function of LC8 in the ciliary-centrosome system

Dynein light chain LC8 is a small dimeric hub protein that recognizes its partners through short linear motifs and is commonly assumed to drive their dimerization. It has more than 100 known binding partners involved in a wide range of cellular processes. Recent large-scale interaction studies suggested that LC8 could also play a role in the ciliary/centrosome system. However, the cellular function of LC8 in this system remains elusive. In this work, we characterized the interaction of LC8 with the centrosomal protein lebercilin (LCA5), which is associated with a specific form of ciliopathy. We showed that LCA5 binds LC8 through two linear motifs. In contrast to the commonly accepted model, LCA5 forms dimers through extensive coiled coil formation in a LC8-independent manner. However, LC8 enhances the oligomerization ability of LCA5 that requires a finely balanced interplay of coiled coil segments and both binding motifs. Based on our results, we propose that LC8 acts as an oligomerization engine that is responsible for the higher order oligomer formation of LCA5. As LCA5 shares several common features with other centrosomal proteins, the presented LC8 driven oligomerization could be widespread among centrosomal proteins, highlighting an important novel cellular function of LC8

DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.Fil: Quaglia, Federica. Università di Padova; Italia. Consiglio Nazionale delle Ricerche; ItaliaFil: Mészáros, Bálint. European Molecular Biology Laboratory; AlemaniaFil: Salladini, Edoardo. Università di Padova; ItaliaFil: Hatos, András. Università di Padova; ItaliaFil: Pancsa, Rita. Research Centre for Natural Sciences; HungríaFil: Chemes, Lucia Beatriz. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Pajkos, Mátyás. Eötvös Loránd University; HungríaFil: Lazar, Tamas. Vlaams Instituut voor Biotechnology; Hungría. Vrije Unviversiteit Brussel; BélgicaFil: Peña Díaz, Samuel. Universitat Autònoma de Barcelona; EspañaFil: Santos, Jaime. Universitat Autònoma de Barcelona; EspañaFil: Ács, Veronika. Research Centre for Natural Sciences; HungríaFil: Farahi, Nazanin. Vlaams Instituut voor Biotechnology; Bélgica. Vrije Unviversiteit Brussel; BélgicaFil: Fichó, Erzsébet. Research Centre for Natural Sciences; HungríaFil: Aspromonte, Maria Cristina. Università di Padova; Italia. Città della Speranza Pediatric Research Institute; ItaliaFil: Bassot, Claudio. Stockholms Universitet; SueciaFil: Chasapi, Anastasia. Centre for Research & Technology Hellas; GreciaFil: Davey, Norman E.. Chester Beatty Laboratories; Reino UnidoFil: Davidović, Radoslav. University of Belgrade; SerbiaFil: Laszlo Holland, Alicia Verónica. European Molecular Biology Laboratory; Alemania. Research Centre for Natural Sciences; HungríaFil: Elofsson, Arne. Stockholms Universitet; SueciaFil: Erdős, Gábor. Eötvös Loránd University; HungríaFil: Gaudet, Pascale. Swiss Institute of Bioinformatics; SuizaFil: Giglio, Michelle. University of Maryland School of Medicine; Estados UnidosFil: Glavina, Juliana. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Iserte, Javier Alonso. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Iglesias, Valentín. Universitat Autònoma de Barcelona; EspañaFil: Kálmán, Zsófia. Pázmány Péter Catholic University; HungríaFil: Lambrughi, Matteo. Danish Cancer Society Research Center; DinamarcaFil: Leonardi, Emanuela. Università di Padova; Italia. Pediatric Research Institute Città della Speranza; ItaliaFil: Rodriguez Sawicki, Luciana. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

DisProt: intrinsic protein disorder annotation in 2020

The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the ‘dark’ proteome

Critical assessment of protein intrinsic disorder prediction

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Pipeline for transferring annotations between proteins beyond globular domains

Background DisProt is the primary repository of Intrinsically Disordered Proteins (IDPs). This database is manually curated and the annotations there have strong experimental support. Currently, DisProt contains a relatively small number of proteins highlighting the importance of transferring annotations regarding verified disorder state and corresponding functions to homologous proteins in other species. In such a way, providing them with highly valuable information to better understand their biological roles. While the principles and practicalities of homology transfer are well-established for globular proteins, these are largely lacking for disordered proteins. Methods We used DisProt to evaluate the transferability of the annotation terms to orthologous proteins. For each protein, we looked for their orthologs, with the assumption that they will have a similar function. Then, for each protein and their orthologs we made multiple sequence alignments (MSAs). Disordered sequences are fast evolving and can be hard to align: Therefore we implemented alignment quality control steps ensuring robust alignments before mapping the annotations. Results We have designed a pipeline to obtain good quality MSAs and to transfer annotations from any protein to their orthologs. Applying the pipeline to DisProt proteins, from the 1,731 entries with 5,623 annotations we can reach 97,555 orthologs and transfer a total of 301,190 terms by homology. We also provide a web server for consulting the results of DisProt proteins and execute the pipeline for any other protein. The server Homology Transfer IDP (HoTIDP) is accessible at http://hotidp.leloir.org.ar.Fil: Martinez Perez, Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina. Fundación Instituto Leloir; ArgentinaFil: Pajkos, Mátyás. Eötvös University; ArgentinaFil: Tosatto, Silvio C. E.. Università di Padova; ItaliaFil: Gibson, Toby James. European Molecular Biology Laboratory Heidelberg; AlemaniaFil: Dosztanyi, Zsuzsanna. Eötvös University; ArgentinaFil: Marino, Cristina Ester. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina. Fundación Instituto Leloir; Argentin

GO enrichment in the high confidence set of LC8 binding partners.

<p>Molecular Function (blue), Biological Process (green) and Cellular Component (red) categories that are enriched in the high-confident LC8 binding partners compared to human background. The x-axis represents the log-odds ratio of each enriched GO category. Process names related to the Hippo pathway are colored in red.</p

Novel linear motif filtering protocol reveals the role of the LC8 dynein light chain in the Hippo pathway

<div><p>Protein-protein interactions (PPIs) formed between short linear motifs and globular domains play important roles in many regulatory and signaling processes but are highly underrepresented in current protein-protein interaction databases. These types of interactions are usually characterized by a specific binding motif that captures the key amino acids shared among the interaction partners. However, the computational proteome-level identification of interaction partners based on the known motif is hindered by the huge number of randomly occurring matches from which biologically relevant motif hits need to be extracted. In this work, we established a novel bioinformatic filtering protocol to efficiently explore interaction network of a hub protein. We introduced a novel measure that enabled the optimization of the elements and parameter settings of the pipeline which was built from multiple sequence-based prediction methods. In addition, data collected from PPI databases and evolutionary analyses were also incorporated to further increase the biological relevance of the identified motif hits. The approach was applied to the dynein light chain LC8, a ubiquitous eukaryotic hub protein that has been suggested to be involved in motor-related functions as well as promoting the dimerization of various proteins by recognizing linear motifs in its partners. From the list of putative binding motifs collected by our protocol, several novel peptides were experimentally verified to bind LC8. Altogether 71 potential new motif instances were identified. The expanded list of LC8 binding partners revealed the evolutionary plasticity of binding partners despite the highly conserved binding interface. In addition, it also highlighted a novel, conserved function of LC8 in the upstream regulation of the Hippo signaling pathway. Beyond the LC8 system, our work also provides general guidelines that can be applied to explore the interaction network of other linear motif binding proteins or protein domains.</p></div

Filtering protocol to find true binding partners.

<p>(A) Schematic diagram of the binary filtering protocol we created utilizing information gain. A given attribute provides a binary split of the Parent group into the <i>C1</i> and <i>C2</i> child groups. The information gain (<i>I</i>) is then calculated as the difference of the Shannon entropy (<i>H</i>) of the Parent group minus the Shannon entropy of the Children groups weighted by their relative probabilities (<i>p</i>). These values were calculated over the dataset containing 40 known human binding partners and 10,000 random human segments from the proteome with a higher than zero PSSM score. (B) The information gain of the PSSM score (left panel) and four disorder prediction methods as a function of different cut-off values (right panel). The disorder prediction method used here were: IUPred (blue), Espritz Disprot (green) and VSL2 (red line), DISOPRED3 (cyan). Optimal cut-off values were obtained from the cut-off value corresponding to the maximum of the information gain, yielding 3.3 for the PSSM score, and 0.42 for IUPred disorder prediction score. (C) The outline of the final filtering protocol indicating the number of elements and percentage of cases in each Child group with the applied binary split.</p