POODLE: Tools Predicting Intrinsically Disordered Regions of Amino Acid Sequence

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Protein intrinsic disorder , a widespread phenomenon characterized by a lack of stable three-dimensional structure, is thought to play an important role in protein function. In the last decade, dozens of computational methods for predicting intrinsic disorder from amino acid sequences have been developed. They are widely used by structural biologists not only for analyzing the biological function of intrinsic disorder but also for finding flexible regions that possibly hinder successful crystallization of the full-length protein. In this chapter, I introduce Prediction Of Order and Disorder by machine LEarning (POODLE), which is a series of programs accurately predicting intrinsic disorder. After giving the theoretical background for predicting intrinsic disorder, I give a detailed guide to using POODLE. I then also briefly introduce a case study where using POODLE for functional analyses of protein disorder led to a novel biological findings.

Original languageEnglish
Pages (from-to)131-145
Number of pages15
JournalMethods in Molecular Biology
Volume1137
DOIs
Publication statusPublished - 2014
Externally publishedYes

Fingerprint

Amino Acid Sequence
Proteins
Crystallization
Machine Learning

Keywords

  • Amino acid composition
  • Amino acid sequence
  • Intrinsically disordered protein
  • Machine learning
  • Position-specific scoring matrix
  • Prediction

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Medicine(all)

Cite this

POODLE : Tools Predicting Intrinsically Disordered Regions of Amino Acid Sequence. / Shimizu, Kana.

In: Methods in Molecular Biology, Vol. 1137, 2014, p. 131-145.

Research output: Contribution to journalArticle

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