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Updated: Apr 19, 2026

Analysis of mRNA Nuclear Export Kinetics in Mammalian Cells by Microinjection
Published on: December 4, 2010
LocNES: a computational tool for locating classical NESs in CRM1 cargo proteins
Darui Xu1, Kara Marquis1, Jimin Pei1
1Department 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.
LocNES accurately predicts nuclear export signals (NESs), which are crucial for protein transport. This new tool improves upon existing methods for identifying these vital peptide sequences.
Area of Science:
- Molecular Biology
- Bioinformatics
Background:
- Nuclear export signals (NESs) are short peptides mediating protein export from the nucleus via the CRM1 pathway.
- CRM1-mediated export regulates numerous cellular functions and is implicated in various diseases.
- Predicting NESs is challenging due to their diverse and complex nature.
Purpose of the Study:
- To develop a novel and accurate predictor for nuclear export signals (NESs).
Main Methods:
- Developed LocNES, a Support Vector Machine-based predictor.
- Utilized amino acid sequence, disorder propensity, and position-specific scoring matrix scores as features.
- Scanned query proteins for NES consensus-fitting peptides and assigned probability scores.
Main Results:
- LocNES demonstrates superior sensitivity and precision compared to existing NES prediction tools.
- Comparative analysis used experimentally identified NESs to validate performance.
Conclusions:
- LocNES offers a more reliable method for predicting nuclear export signals.
- Improved NES prediction can advance understanding of CRM1-mediated transport and related diseases.
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