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

Bioinformatics. 2015 May 1;31(9):1357-65. doi: 10.1093/bioinformatics/btu826. Epub 2014 Dec 15.

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.

Availability and implementation: LocNES is freely available at http://prodata.swmed.edu/LocNES CONTACT: yuhmin.chook@utsouthwestern.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Amino Acid Sequence
  • Consensus Sequence
  • Humans
  • Karyopherins / chemistry*
  • Nuclear Export Signals*
  • Position-Specific Scoring Matrices
  • Receptors, Cytoplasmic and Nuclear / chemistry*
  • Sequence Analysis, Protein
  • Software*
  • Support Vector Machine

Substances

  • Karyopherins
  • Nuclear Export Signals
  • Receptors, Cytoplasmic and Nuclear
  • exportin 1 protein