期刊
BIOINFORMATICS
卷 31, 期 9, 页码 1357-1365出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btu826
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资金
- Cancer Prevention Research Institute of Texas (CPRIT) [RP120352]
- National Institutes of Health [F32GM093493, GM094575, R01 GM069909]
- University of Texas Southwestern Endowed Scholars Program
- Welch Foundation Grant [I-1505, I-1532]
- Leukemia and Lymphoma Society Scholar Award
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 consensusfitting 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.
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