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

Abstract

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.

Figure 1. Nuclear export activity of class 1a and class 1c NES mutants.

(A) Class 1a NESs carrying mutations at two hydrophobic positions and three spacer positions between Φ2 and Φ4. (B) Class 1c NESs carrying mutations at three positions within the spacer region between Φ2 and Φ3. These NES mutants were assayed for their nuclear export activity in NIH3T3 cells, and the activities were classified as scores from 1 to 10, as in Figure S1. The scores are indicated at the right columns of the corresponding sequences. Altered bases are highlighted in blue.

Figure 2. Positive and negative NES datasets obtained from four different data resources.

(A) Artificial NES datasets. (B) DUB NES datasets. (C) Valid NES datasets. (D) Sp-protein datasets. The positive and negative datasets (B-P2 and B-N2) of the DUB datasets and the negative training dataset (D-N2) of the Sp-protein datasets were always included in the training data for the profile optimization, whereas the other training datasets were used only when they were not contained in a test dataset to be used. For example, when we conducted the prediction test with the test datasets, A-P1 and A-N1, we used the optimized profiles for NESmapper, that were trained with C-N2, in addition to B-P2, B-N2, and D-N2.

Figure 3. Independent and additive contributions of amino acids at the conserved hydrophobic positions to the entire NES activity.

One or two leucine residues of a class 1a NES at the Φ1, Φ3, or Φ4 conserved hydrophobic positions, indicated on the top line, were replaced with cysteine, phenylalanine, threonine or tryptophan, as highlighted in blue, and the nuclear export activity was assayed in NIH3T3 cells. The indicated activity scores were determined as in Figure S1. Note that the effects of the substituted residues on the NES activity scores were roughly independent and additive.

Figure 4. Activity-based profiles of CRM1-dependent NES.

(A) Activity-based profile of class 1a/3 NES. Class 1a/3 NES is an extension of class 1a NES, in which the N-terminal region of class 1a and the C-terminal region of class 3 overlap. A single amino acid residue of a class 1a/3 NES template sequence, indicated at the top of the matrix, was replaced with the various other residues indicated in the left column. The nuclear export activity of the NES mutant was assayed in NIH3T3 cells. The indicated activity scores were determined as in Figure S1. This template NES has an activity score of 8. Scores with higher, slightly higher, and lower activities than the average value for each position are shown in red, orange, and blue, respectively. At several mutational positions, modified templates with a different level of basal activity were used to obtain more dispersed scores. The conserved hydrophobic positions (Φ0–Φ4) are marked on the template sequence. The scores at the Φ0 position (P^a) were estimated based on the data of Güttler et al . Blanks represent undetermined scores. (B) NES profile of the spacer region between the Φ1 and Φ2 positions of a class 1b NES. The template sequence has a standard activity score of 4. (PSSELAKLAGLDLN) (C) NES profile for the spacer regions between Φ1 and Φ3 positions of the class 1c NES. The template sequence (SELAEKLQAGLDLN) has an activity score of 8. (D) Activity-based profile of class 2 NESs. The template NES sequence, indicated at the top of the matrix, has a standard activity score of 3.

Figure 5. ROC analyses for five NES prediction methods.

(A) ROC curve generated with artificial NES datasets. For the artificial NES sets, 163 positive and 60 negative experimentally verified NESs were used to plot the ROC curves for the traditional consensus-based prediction, NetNES, NESmapper, Wregex, and NESsential. The true positive rates (TPRs) and false positive rates (FPRs) for each tool were measured by changing the threshold scores for Wregex and NESmapper or the threshold probability values for NESsential. The curves for the NESmapper predictions with the optimized and unoptimized profiles are shown with solid lines with red circles and with dotted lines with orange triangles, respectively, those for Wregex with solid lines with green squares, and those for NESsential with solid lines with blue diamonds. The results for the traditional consensus-based prediction and NetNES are shown with green and blue asterisks, respectively. (B) ROC curve generated with ValidNES/Sp-test datasets. We measured the false positives by counting NESs called from regions other than the ranges corresponding to true NESs. To calculate the FPRs for the ValidNES and Sp-test datasets, only called NESs that matched the traditional consensus sequence were counted as false positives and divided by the number of sequences that matched the traditional consensus sequence in each dataset (841 for ValidNES and 231 for Sp-test). The mean FPRs for both datasets were used for the analysis. (C) ROC curve generated with the artificial NES and ValidNES/Sp-test datasets.