A probabilistic model of nuclear import of proteins

Mehdi, Ahmed M.; Sehgal, Muhammad Shoaib B.; Kobe, Boštjan; Bailey, Timothy L.; Bodén, Mikael

doi:10.1093/bioinformatics/btr121

Cited by 24 publications

(29 citation statements)

References 32 publications

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“…We combined 2 state-of-the-art bioinformatics tools for prediction of NLSs (NucImport 16 ) and phosphorylation sites (Predikin 13 ) that we ourselves developed previously. NucImport uses a probabilistic (Bayesian network) approach to recognize a variety of NLSs by integrating amino acid sequence and interaction data and predicts the sequence position of the NLS, out-performing other available methods.…”

Section: Resultsmentioning

confidence: 99%

“…NucImport uses a probabilistic (Bayesian network) approach to recognize a variety of NLSs by integrating amino acid sequence and interaction data and predicts the sequence position of the NLS, out-performing other available methods. 16 Predikin uses the concept of specificity-determining residues to predict peptide specificity of protein kinases and identify substrates for protein kinases 14,17,25 ; the tool outperformed other competing tools in the protein kinase section of the Peptide Recognition Domain specificity prediction category of the 2009 DREAM4 challenge (an independent test using unpublished data). 13 We first used Predikin 13 to determine how often a Ser or Thr residue was predicted to be phosphorylated, regardless of the import status or presence of NLS.…”

Section: Resultsmentioning

confidence: 99%

“…Experimental Procedures). We then used NucImport 16 to predict the location of classical NLSs. For each NLS location, we determined the frequency of (predicted) phosphorylation of the P0 position to be 0.393 (p D 2.348e-30) and of the P-1 position to be 0.234 (p D 9.530e-05).…”

Section: Resultsmentioning

confidence: 99%

“…15 Although they pose difficulty for computational analysis because of their small size, significant progress has been made in the recent years in the computational identification of a number of different types of linear motifs and the integration of diverse types of experimental data into these computational approaches. 15 In particular, we have developed some of the most reliable approaches for the identification of NLSs 16 and protein phosphorylation sites. 13,17 While computational predictions are often hampered by less than desired accuracies, combined prediction of 2 associated motifs can in fact lead to increased accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Dynamics of re-constitution of the human nuclear proteome after cell division is regulated by NLS-adjacent phosphorylation

et al. 2014

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Phosphorylation by the cyclin-dependent kinase 1 (Cdk1) adjacent to nuclear localization signals (NLSs) is an important mechanism of regulation of nucleocytoplasmic transport. However, no systematic survey has yet been performed in human cells to analyze this regulatory process, and the corresponding cell-cycle dynamics have not yet been investigated. Here, we focused on the human proteome and found that numerous proteins, previously not identified in this context, are associated with Cdk1-dependent phosphorylation sites adjacent to their NLSs. Interestingly, these proteins are involved in key regulatory events of DNA repair, epigenetics, or RNA editing and splicing. This finding indicates that cell-cycle dependent events of genome editing and gene expression profiling may be controlled by nucleocytoplasmic trafficking. For in-depth investigations, we selected a number of these proteins and analyzed how point mutations, expected to modify the phosphorylation ability of the NLS segments, perturb nucleocytoplasmic localization. In each case, we found that mutations mimicking hyper-phosphorylation abolish nuclear import processes. To understand the mechanism underlying these phenomena, we performed a video microscopy-based kinetic analysis to obtain information on cell-cycle dynamics on a model protein, dUTPase. We show that the NLS-adjacent phosphorylation by Cdk1 of human dUTPase, an enzyme essential for genomic integrity, results in dynamic cell cycle-dependent distribution of the protein. Non-phosphorylatable mutants have drastically altered protein re-import characteristics into the nucleus during the G1 phase. Our results suggest a dynamic Cdk1-driven mechanism of regulation of the nuclear proteome composition during the cell cycle.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamics of re-constitution of the human nuclear proteome after cell division is regulated by NLS-adjacent phosphorylation

et al. 2014

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“…It has become clear that probabilistic methods, such Bayesian networks, can address realities of biological data, in terms of both their (uncertain) integration and their (incomplete) coverage (24,25). To make the most of current transcriptomic and proteomic data, we developed a Bayesian network model that links transcriptional, post-transcriptional, and pre-and post-translational factors and events to predict protein abundance even in the absence of complete data.…”

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confidence: 99%

Predicting the Dynamics of Protein Abundance

Mehdi

Patrick

Bailey

et al. 2014

Molecular & Cellular Proteomics

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Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates transcriptomic and proteomic data to predict protein abundance and to model the effects of its determinants. We aim to use this model to follow a molecular response over time, from condition-specific data, in order to understand adaptation during processes such as the cell cycle. With microarray data now available for many conditions, the general utility of a protein abundance predictor is broad.Whereas most quantitative proteomics studies have focused on higher organisms, we developed a predictive model of protein abundance for both Saccharomyces cerevisiae and Schizosaccharomyces pombe to explore the latitude at the protein level. Our predictor primarily relies on mRNA level, mRNA-protein interaction, mRNA folding energy and half-life, and tRNA adaptation. The combination of key features, allowing for the low certainty and uneven coverage of experimental observations, gives comparatively minor but robust prediction accuracy. The model substantially improved the analysis of protein regulation during the cell cycle: predicted protein abundance identified twice as many cell-cycle-associated proteins as experimental mRNA levels. Predicted protein abundance was more dynamic than observed mRNA expression, agreeing with experimental protein abundance from a human cell line. We illustrate how the same model can be used to predict the folding energy of mRNA when protein abundance is available, lending credence to the emerging view that mRNA folding affects translation efficiency. The software and data used in this research are available at http://bioinf.scmb. uq.edu.au/proteinabundance/. Molecular & Cellular Proteomics 13:

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Computational identification of post-translational modification-based nuclear import regulations by characterizing nuclear localization signal-import receptor interaction

Lin

Liu

2014

Proteins

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The binding affinity between a nuclear localization signal (NLS) and its import receptor is closely related to corresponding nuclear import activity. PTM-based modulation of the NLS binding affinity to the import receptor is one of the most understood mechanisms to regulate nuclear import of proteins. However, identification of such regulation mechanisms is challenging due to the difficulty of assessing the impact of PTM on corresponding nuclear import activities. In this study we proposed NIpredict, an effective algorithm to predict nuclear import activity given its NLS, in which molecular interaction energy components (MIECs) were used to characterize the NLS-import receptor interaction, and the support vector regression machine (SVR) was used to learn the relationship between the characterized NLS-import receptor interaction and the corresponding nuclear import activity. Our experiments showed that nuclear import activity change due to NLS change could be accurately predicted by the NIpredict algorithm. Based on NIpredict, we developed a systematic framework to identify potential PTM-based nuclear import regulations for human and yeast nuclear proteins. Application of this approach has identified the potential nuclear import regulation mechanisms by phosphorylation of two nuclear proteins including SF1 and ORC6.

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A probabilistic model of nuclear import of proteins

Abstract: http://pprowler.itee.uq.edu.au/NucImport

Cited by 24 publications

References 32 publications

Dynamics of re-constitution of the human nuclear proteome after cell division is regulated by NLS-adjacent phosphorylation

Dynamics of re-constitution of the human nuclear proteome after cell division is regulated by NLS-adjacent phosphorylation

Predicting the Dynamics of Protein Abundance

Computational identification of post-translational modification-based nuclear import regulations by characterizing nuclear localization signal-import receptor interaction

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