MobiDB-lite

Fast and highly specific consensus prediction of intrinsic disorder in proteins

Marco Necci, Damiano Piovesan, Z. Dosztányi, Silvio C.E. Tosatto

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Motivation: Intrinsic disorder (ID) is established as an important feature of protein sequences. Its use in proteome annotation is however hampered by the availability of many methods with similar performance at the single residue level, which have mostly not been optimized to predict long ID regions of size comparable to domains. Results: Here, we have focused on providing a single consensus-based prediction, MobiDB-lite, optimized for highly specific (i.e. few false positive) predictions of long disorder. The method uses eight different predictors to derive a consensus which is then filtered for spurious short predictions. Consensus prediction is shown to outperform the single methods when annotating long ID regions. MobiDB-lite can be useful in large-scale annotation scenarios and has indeed already been integrated in the MobiDB, DisProt and InterPro databases.

Original languageEnglish
Pages (from-to)1402-1404
Number of pages3
JournalBioinformatics
Volume33
Issue number9
DOIs
Publication statusPublished - May 1 2017

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Disorder
Proteins
Protein
Prediction
Annotation
Proteome
Protein Sequence
False Positive
Databases
Predictors
Availability
Predict
Scenarios

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

MobiDB-lite : Fast and highly specific consensus prediction of intrinsic disorder in proteins. / Necci, Marco; Piovesan, Damiano; Dosztányi, Z.; Tosatto, Silvio C.E.

In: Bioinformatics, Vol. 33, No. 9, 01.05.2017, p. 1402-1404.

Research output: Contribution to journalArticle

Necci, Marco ; Piovesan, Damiano ; Dosztányi, Z. ; Tosatto, Silvio C.E. / MobiDB-lite : Fast and highly specific consensus prediction of intrinsic disorder in proteins. In: Bioinformatics. 2017 ; Vol. 33, No. 9. pp. 1402-1404.
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