Literature DB >> 22661649

MobiDB: a comprehensive database of intrinsic protein disorder annotations.

Tomás Di Domenico1, Ian Walsh, Alberto J M Martin, Silvio C E Tosatto.   

Abstract

MOTIVATION: Disordered protein regions are key to the function of numerous processes within an organism and to the determination of a protein's biological role. The most common source for protein disorder annotations, DisProt, covers only a fraction of the available sequences. Alternatively, the Protein Data Bank (PDB) has been mined for missing residues in X-ray crystallographic structures. Herein, we provide a centralized source for data on different flavours of disorder in protein structures, MobiDB, building on and expanding the content provided by already existing sources. In addition to the DisProt and PDB X-ray structures, we have added experimental information from NMR structures and five different flavours of two disorder predictors (ESpritz and IUpred). These are combined into a weighted consensus disorder used to classify disordered regions into flexible and constrained disorder. Users are encouraged to submit manual annotations through a submission form. MobiDB features experimental annotations for 17 285 proteins, covering the entire PDB and predictions for the SwissProt database, with 565 200 annotated sequences. Depending on the disorder flavour, 6-20% of the residues are predicted as disordered. AVAILABILITY: The database is freely available at http://mobidb.bio.unipd.it/. CONTACT: silvio.tosatto@unipd.it.

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Year:  2012        PMID: 22661649     DOI: 10.1093/bioinformatics/bts327

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  59 in total

1.  Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe.

Authors:  Marco Necci; Damiano Piovesan; Silvio C E Tosatto
Journal:  Protein Sci       Date:  2016-10-25       Impact factor: 6.725

2.  Codon selection reduces GC content bias in nucleic acids encoding for intrinsically disordered proteins.

Authors:  Christopher J Oldfield; Zhenling Peng; Vladimir N Uversky; Lukasz Kurgan
Journal:  Cell Mol Life Sci       Date:  2019-06-07       Impact factor: 9.261

3.  Paradoxes and wonders of intrinsic disorder: Prevalence of exceptionality.

Authors:  Vladimir N Uversky
Journal:  Intrinsically Disord Proteins       Date:  2015-06-25

4.  Intrinsic disorder in spondins and some of their interacting partners.

Authors:  Oluwole Alowolodu; Gbemisola Johnson; Lamis Alashwal; Iqbal Addou; Irina V Zhdanova; Vladimir N Uversky
Journal:  Intrinsically Disord Proteins       Date:  2016-12-15

5.  An optimized Npro-based method for the expression and purification of intrinsically disordered proteins for an NMR study.

Authors:  Natsuko Goda; Naoki Matsuo; Takeshi Tenno; Sonoko Ishino; Yoshizumi Ishino; Satoshi Fukuchi; Motonori Ota; Hidekazu Hiroaki
Journal:  Intrinsically Disord Proteins       Date:  2015-02-23

Review 6.  Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions.

Authors:  Fanchi Meng; Vladimir N Uversky; Lukasz Kurgan
Journal:  Cell Mol Life Sci       Date:  2017-06-06       Impact factor: 9.261

7.  Dissecting physical structure of calreticulin, an intrinsically disordered Ca2+-buffering chaperone from endoplasmic reticulum.

Authors:  Anna Rita Migliaccio; Vladimir N Uversky
Journal:  J Biomol Struct Dyn       Date:  2017-05-26

8.  Intrinsically disordered proteins and conformational noise: implications in cancer.

Authors:  Gita Mahmoudabadi; Krithika Rajagopalan; Robert H Getzenberg; Sridhar Hannenhalli; Govindan Rangarajan; Prakash Kulkarni
Journal:  Cell Cycle       Date:  2012-12-19       Impact factor: 4.534

9.  AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields.

Authors:  Sheng Wang; Jianzhu Ma; Jinbo Xu
Journal:  Bioinformatics       Date:  2016-09-01       Impact factor: 6.937

10.  Automatic detection and classification of leukocytes using convolutional neural networks.

Authors:  Jianwei Zhao; Minshu Zhang; Zhenghua Zhou; Jianjun Chu; Feilong Cao
Journal:  Med Biol Eng Comput       Date:  2016-11-07       Impact factor: 2.602

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