Darui Xu1, Kara Marquis1, Jimin Pei1, Szu-Chin Fu1, Tolga Cağatay1, Nick V Grishin2, Yuh Min Chook3. 1. Department of Pharmacology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9041, USA, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA, Department of Biophysics and Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA. 2. Department of Pharmacology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9041, USA, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA, Department of Biophysics and Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA Department of Pharmacology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9041, USA, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA, Department of Biophysics and Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA Department of Pharmacology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9041, USA, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA, Department of Biophysics and Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA. 3. Department of Pharmacology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9041, USA, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA, Department of Biophysics and Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA Department of Pharmacology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9041, USA, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA, Department of Biophysics and Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9050, USA.
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.
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.
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