Evidence map›Paper›PMID 40347452›Full record

ArticleBioinformatics (Oxford, England)2025

MobiDB-lite 4.0: faster prediction of intrinsic protein disorder and structural compactness.

Mahta Mehdiabadi, Matthias Blum, Giulio Tesei, Sören von Bülow, Kresten Lindorff-Larsen, Silvio C E Tosatto, Damiano Piovesan

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Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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4 citing papers in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Mahta MehdiabadiDepartment of Biomedical Sciences, University of Padova, 35131 Padova, Italy.ORCID 0009-0007-2044-541X
Matthias BlumEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom.ORCID 0000-0001-5773-4724
Giulio TeseiStructural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.
Sören von BülowStructural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.
Kresten Lindorff-LarsenStructural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.
Silvio C E TosattoDepartment of Biomedical Sciences, University of Padova, 35131 Padova, Italy.ORCID 0000-0003-4525-7793
Damiano PiovesanDepartment of Biomedical Sciences, University of Padova, 35131 Padova, Italy.ORCID 0000-0001-8210-2390

Funding

European Union through NextGenerationEU, PNRR project ELIXIRxNextGenIT IR0000010National Center for Gene Therapy and Drugs based on RNA Technology CN00000041
6 · The paper itself

Abstract

motivationIn recent years, many disorder predictors have been developed to identify intrinsically disordered regions (IDRs) in proteins, achieving high accuracy. However, it may be difficult to interpret differences in predictions across methods. Consensus methods offer a simple solution, highlighting reliable predictions while filtering out uncertain positions. Here, we present a new version of MobiDB-lite, a consensus method designed to predict long IDRs and classify them based on compositional biases and conformational properties.

resultsMobiDB-lite 4.0 pipeline was optimized to be ten times faster than the previous version. It now provides compactness annotations based on predicted apparent scaling exponent. The newly added features and disorder subclassifications allow the users to get a comprehensive insight into the protein's function and characteristics. MobiDB-lite 4.0 is integrated into the MobiDB and DisProt databases. A version without the compactness predictor is integrated into InterProScan, propagating MobiDB-lite annotations to UniProtKB. AVAILABILITY AND IMPLEMENTATION: The MobiDB-lite 4.0 source code and a Docker container are available from the GitHub repository: https://github.com/BioComputingUP/MobiDB-lite.

Indexed as

Computational BiologyIntrinsically Disordered ProteinsSoftwareDatabases, ProteinProtein ConformationIntrinsically Disordered Proteins

Identifiers

PMID40347452
PMCPMC12122076

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.