7 research outputs found

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

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    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. © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research

    3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

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    While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modeling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank

    Caractérisation des périodes de sécheresse sur le domaine de l'Afrique simulée par le Modèle Régional Canadien du Climat (MRCC5)

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    Les conséquences des changements climatiques sur la fréquence ainsi que sur l'intensité des précipitations auront un impact direct sur les périodes de sécheresse et par conséquent sur différents secteurs économiques tels que le secteur de l'agriculture. Ainsi, dans cette étude, l'habilité du Modèle Régional Canadien du Climat (MRCC5) à simuler les différentes caractéristiques des périodes de sécheresse est évaluée pour 4 seuils de précipitation soit 0.5 mm, 1 mm, 2 mm et 3 mm. Ces caractéristiques incluent le nombre de jours secs, le nombre de périodes de sécheresse ainsi que le maximum de jours consécutifs sans précipitation associé à une récurrence de 5 ans. Les résultats sont présentés pour des moyennes annuelles et saisonnières. L'erreur de performance est évaluée en comparant le MRCC5 piloté par ERA-Interim aux données d'analyses du GPCP pour le climat présent (1997-2008). L'erreur due aux conditions aux frontières c'est-à-dire les erreurs de pilotage du MRCC5, soit par CanESM2 et par ERA-Interim ainsi que l'évaluation de la valeur ajoutée du MRCC5 face au CanESM2 sont également analysées. L'analyse de ces caractéristiques est également faite dans un contexte de climat changeant pour deux périodes futures, soit 2041-2070 et 2071-2100 à l'aide du MRCC5 piloté par le modèle de circulation générale CanESM2 de même que par le modèle CanESM2 sous le scénario RCP 4.5. Les résultats suggèrent que le MRCC5 piloté par ERA-Interim a tendance à surestimer la moyenne annuelle du nombre de jours secs ainsi que le maximum de jours consécutifs sans précipitation associé à une récurrence de 5 ans dans la plupart des régions de l'Afrique et une tendance à sous-estimer le nombre de périodes de sécheresse. En général, l'erreur de performance est plus importante que l'erreur due aux conditions aux frontières pour les différentes caractéristiques de périodes de sécheresse. Pour les régions équatoriales, les changements appréhendés par le MRCC5 piloté par CanESM2 pour les différentes caractéristiques de périodes de sécheresse et pour deux périodes futures (2041-2070 et 2071-2100), suggèrent une augmentation significatives du nombre de jours secs ainsi que du maximum de jours consécutifs sans précipitation associé à une récurrence de 5 ans. Une diminution significative du nombre de périodes de sécheresse est aussi prévue.\ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Modèle Régional du Climat, Changement climatique, Jours secs, Nombre de périodes de sécheresse, Événement de faible récurrence, Afriqu

    FuzDrop on AlphaFold: visualizing the sequence-dependent propensity of liquid-liquid phase separation and aggregation of proteins.

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    Many proteins perform their functions within membraneless organelles, where they form a liquid-like condensed state, also known as droplet state. The FuzDrop method predicts the probability of spontaneous liquid-liquid phase separation of proteins and provides a sequence-based score to identify the regions that promote this process. Furthermore, the FuzDrop method estimates the propensity of conversion of proteins to the amyloid state, and identifies aggregation hot-spots, which can drive the irreversible maturation of the liquid-like droplet state. These predictions can also identify mutations that can induce formation of amyloid aggregates, including those implicated in human diseases. To facilitate the interpretation of the predictions, the droplet-promoting and aggregation-promoting regions can be visualized on protein structures generated by AlphaFold. The FuzDrop server (https://fuzdrop.bio.unipd.it) thus offers insights into the complex behavior of proteins in their condensed states and facilitates the understanding of the functional relationships of proteins

    FuzDB: a new phase in understanding fuzzy interactions

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    : Fuzzy interactions are specific, variable contacts between proteins and other biomolecules (proteins, DNA, RNA, small molecules) formed in accord to the cellular context. Fuzzy interactions have recently been demonstrated to regulate biomolecular condensates generated by liquid-liquid phase separation. The FuzDB v4.0 database (https://fuzdb.org) assembles experimentally identified examples of fuzzy interactions, where disordered regions mediate functionally important, context-dependent contacts between the partners in stoichiometric and higher-order assemblies. The new version of FuzDB establishes cross-links with databases on structure (PDB, BMRB, PED), function (ELM, UniProt) and biomolecular condensates (PhaSepDB, PhaSePro, LLPSDB). FuzDB v4.0 is a source to decipher molecular basis of complex cellular interaction behaviors, including those in protein droplets

    FuzPred: a web server for the sequence-based prediction of the context-dependent binding modes of proteins

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    Proteins form complex interactions in the cellular environment to carry out their functions. They exhibit a wide range of binding modes depending on the cellular conditions, which result in a variety of ordered or disordered assemblies. To help rationalise the binding behavior of proteins, the FuzPred server predicts their sequence-based binding modes without specifying their binding partners. The binding mode defines whether the bound state is formed through a disorder-to-order transition resulting in a well-defined conformation, or through a disorder-to disorder transition where the binding partners remain conformationally heterogeneous. To account for the context-dependent nature of the binding modes, the FuzPred method also estimates the multiplicity of binding modes, the likelihood of sampling multiple binding modes. Protein regions with a high multiplicity of binding modes may serve as regulatory sites or hot-spots for structural transitions in the assembly. To facilitate the interpretation of the predictions, protein regions with different interaction behaviors can be visualised on protein structures generated by AlphaFold. The FuzPred web server (https://fuzpred.bio.unipd.it) thus offers insights into the structural and dynamical changes of proteins upon interactions and contributes to development of structure-function relationships under a variety of cellular conditions.[GRAPHICS

    DisProt : intrinsic protein disorder annotation in 2020

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    Altres ajuts: European Regional Development Fund [POCI-01-0145-FEDER-031173, POCI-01-0145-FEDER-029221].- ICREA-Academia 2015The 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
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