LocNES: A computational tool for locating classical NESs in CRM1 cargo proteins

Darui Xu, Kara Marquis, Jimin Pei, Szu Chin Fu, Tolga Caʇatay, Nick V. Grishin, Yuh Min Chook

Research output: Contribution to journalArticle

55 Scopus citations

Abstract

Motivation: Classical nuclear export signals (NESs) are short cognate peptides that direct proteins out of the nucleus via the CRM1-mediated export pathway. CRM1 regulates the localization of hundreds of macromolecules involved in various cellular functions and diseases. Due to the diverse and complex nature of NESs, reliable prediction of the signal remains a challenge despite several attempts made in the last decade. Results: We present a new NES predictor, LocNES. LocNES scans query proteins for NES consensus-fitting peptides and assigns these peptides probability scores using Support Vector Machine model, whose feature set includes amino acid sequence, disorder propensity, and the rank of position-specific scoring matrix score. LocNES demonstrates both higher sensitivity and precision over existing NES prediction tools upon comparative analysis using experimentally identified NESs.

Original languageEnglish (US)
Pages (from-to)1357-1365
Number of pages9
JournalBioinformatics
Volume31
Issue number9
DOIs
StatePublished - 2015

ASJC Scopus subject areas

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

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