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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.

SUBMITTER: Kosugi S

PROVIDER: S-EPMC4168985 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIdentification of subcellular localization in proteins is crucial to elucidate cellular processes and molecular functions in a cell. However, given a tremendous amount of sequence data generated in the post-genomic era, determining protein localization based on biological experiments can be expensive and time-consuming. Therefore, developing prediction systems to analyze uncharacterised proteins efficiently has played an important role in high-throughput protein analyses. In a eukaryotic cell, many essential biological processes take place in the nucleus. Nuclear proteins shuttle between nucleus and cytoplasm based on recognition of nuclear translocation signals, including nuclear localization signals (NLSs) and nuclear export signals (NESs). Currently, only a few approaches have been developed specifically to predict nuclear localization using sequence features, such as putative NLSs. However, it has been shown that prediction coverage based on the NLSs is very low. In addition, most existing approaches only attained prediction accuracy and Matthew's correlation coefficient (MCC) around 54%~70% and 0.250~0.380 on independent test set, respectively. Moreover, no predictor can generate sequence motifs to characterize features of potential NESs, in which biological properties are not well understood from existing experimental studies.ResultsIn this study, first we propose PSLNuc (Protein Subcellular Localization prediction for Nucleus) for predicting nuclear localization in proteins. First, for feature representation, a protein is represented by gapped-dipeptides and the feature values are weighted by homology information from a smoothed position-specific scoring matrix. After that, we incorporate probabilistic latent semantic indexing (PLSI) for feature reduction. Finally, the reduced features are used as input for a support vector machine (SVM) classifier. In addition to PSLNuc, we further identify gapped-dipeptide signatures for putative NLSs and NESs to develop a prediction method, PSLNTS (Protein Subcellular Localization prediction using Nuclear Translocation Signals). We apply PLSI to generate gapped-dipeptide signatures from both nuclear and non-nuclear proteins, and propose candidate sequence motifs for putative NLSs and NESs. Then, we incorporate only the proposed gapped-dipeptide signatures in an SVM classifier to mimic biological properties of NLSs and NESs for predicting nuclear localization in PSLNTS.ConclusionsExperiment results demonstrate that the proposed method shows a significant improvement for nuclear localization prediction. To compare our predictive performance with other approaches, we incorporate two non-redundant benchmark data sets, a training set and an independent test set. Evaluated by five-fold cross-validation on the training set, PSLNuc attains an overall accuracy of 79.7%, which is 4.8% improvement over the state-of-the-art system. In addition, our method also enhances the MCC from 0.497 to 0.595. Compared on the independent test set, PSLNuc outperforms other predictors by 3.9%~19.9% on accuracy and 0.077~0.207 on MCC. This suggests that, in addition to NLSs, which have been shown important for nuclear proteins, NESs can also be an effective indicator to detect non-nuclear proteins. Most notably, using only a few proposed gapped-dipeptide signatures as input features for the SVM classifier, PSLNTS further enhances the accuracy and MCC to 80.9% and 0.618, respectively. Our results demonstrate that gapped-dipeptide signatures can better discriminate nuclear and non-nuclear proteins. Moreover, the proposed gapped-dipeptide signatures can be biologically interpreted and used in further experiment analyses of nuclear translocation signals, including NLSs and NESs.

Project description:BACKGROUND: Chikungunya virus (CHIKV) is an alphavirus of the Togaviridae family. After autoproteolytic cleavage, the CHIKV capsid protein (CP) is involved in RNA binding and assembly of the viral particle. The monomeric CP is approximately 30 kDa in size and is small enough for passive transport through nuclear pores. Some alphaviruses are found to harbor nuclear localization signals (NLS) and transport of these proteins between cellular compartments was shown to be energy dependent. The active nuclear import of cytoplasmic proteins is mediated by karyopherins and their export by exportins. As nuclear and cytoplasmic trafficking may play a role in the life cycle of CHIKV, we have sought to identify nuclear localization and nuclear export signals in CHIKV CP in a virus-free system. METHODS: EGFP-fusion proteins of CHIKV CP and mutants thereof were created and used to monitor their intracellular localization. Binding of cellular proteins was confirmed in pull-down assays with purified CP using co-immuoprecipitation. Nuclear localization was demonstrated in a virus-free system using fluorescence microscopy. RESULTS: Here we show that CHIKV CP is a nuclear-cytoplasmic shuttling protein with an active NLS that binds to karyopherin ? (Kar?) for its nuclear translocation. We also found that the Kar?4 C-terminal NLS binding site is sufficient for this interaction. We further demonstrate that CHIKV CP interacts directly with the export receptor CRM1 to transport this viral protein out of the nucleus via a nuclear export signal (NES). The CHIKV CP NES was mapped between amino acids 143 and 155 of CP. Deduced from in silico analyses we found that the NES has a mode of binding similar to the snurportin-1 CRM1 complex. CONCLUSIONS: We were able to show that in a virus-free system that the CHIKV capsid protein contains both, a NLS and a NES, and that it is actively transported between the cytoplasma and the nucleus. We conclude that CHIKV CP has the ability to shuttle via interaction with karyopherins for its nuclear import and, vice versa, by CRM1-dependent nuclear export.

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

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