NESmapper: accurate prediction of leucine-rich nuclear export signals using activity-based profiles

• Published in: PLoS computational biology Vol. 10; no. 9; p. e1003841
• Main Authors: Kosugi, Shunichi, Yanagawa, Hiroshi, Terauchi, Ryohei, Tabata, Satoshi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.09.2014; Public Library of Science (PLoS)
• Subjects: Active Transport, Cell Nucleus - genetics; Active Transport, Cell Nucleus - physiology; Amino Acid Sequence; Amino acids; Analysis; Animals; Biological transport; Biology and Life Sciences; Cell cycle; Computational Biology - methods; Datasets; DNA Mutational Analysis; Exports; Genetic aspects; Leucine - chemistry; Leucine - genetics; Mice; Molecular Sequence Data; NIH 3T3 Cells; Nuclear Export Signals - genetics; Nuclear Export Signals - physiology; Physiological aspects; Probability distribution; Proteins; ROC Curve; Sequence Analysis, Protein - methods; Software; Yeast; Japan
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1003841

The nuclear export of proteins is regulated largely through the exportin/CRM1 pathway, which involves the specific recognition of leucine-rich nuclear export signals (NESs) in the cargo proteins, and modulates nuclear-cytoplasmic protein shuttling by antagonizing the nuclear import activity mediated by importins and the nuclear import signal (NLS). Although the prediction of NESs can help to define proteins that undergo regulated nuclear export, current methods of predicting NESs, including computational tools and consensus-sequence-based searches, have limited accuracy, especially in terms of their specificity. We found that each residue within an NES largely contributes independently and additively to the entire nuclear export activity. We created activity-based profiles of all classes of NESs with a comprehensive mutational analysis in mammalian cells. The profiles highlight a number of specific activity-affecting residues not only at the conserved hydrophobic positions but also in the linker and flanking regions. We then developed a computational tool, NESmapper, to predict NESs by using profiles that had been further optimized by training and combining the amino acid properties of the NES-flanking regions. This tool successfully reduced the considerable number of false positives, and the overall prediction accuracy was higher than that of other methods, including NESsential and Wregex. This profile-based prediction strategy is a reliable way to identify functional protein motifs. NESmapper is available at http://sourceforge.net/projects/nesmapper.