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Analysis of mRNA Nuclear Export Kinetics in Mammalian Cells by Microinjection
Published on: December 4, 2010
Characterization and prediction of protein nucleolar localization sequences
Michelle S Scott1, François-Michel Boisvert, Mark D McDowall
1Division of Biological Chemistry and Drug Discovery and Wellcome Trust Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK. michelle@compbio.dundee.ac.uk
Nucleic Acids Research
|July 29, 2010
Summary
This study identifies and analyzes human nucleolar localization sequences (NoLSs), revealing they are distinct targeting motifs. A computational predictor was developed and validated, confirming NoLSs are prevalent and can be identified computationally.
Area of Science:
- Cell Biology
- Molecular Biology
- Bioinformatics
Background:
- Protein localization to the nucleolus is crucial for cellular function.
- While often attributed to non-specific retention, specific nucleolar localization sequences (NoLSs) are increasingly recognized.
Purpose of the Study:
- To collate and analyze known human NoLSs.
- To develop a computational tool for predicting NoLSs.
- To experimentally validate novel predicted NoLSs.
Main Methods:
- Statistical analysis of 46 human NoLSs from literature.
- Training an artificial neural network using sequence and secondary structure data.
- Experimental validation of top-scoring predicted NoLSs in the human proteome.
Main Results:
- NoLSs are characterized by a high proportion of basic and solvent-accessible residues, with specific secondary structure profiles.
- The developed predictor achieved a 54% true positive rate with a low overall false positive rate (1.52%).
- Ten novel NoLSs were experimentally confirmed, demonstrating the predictor's efficacy.
Conclusions:
- Nucleolar localization sequences (NoLSs) are prevalent, distinct targeting motifs separate from nuclear localization signals.
- Computational prediction of NoLSs is feasible and effective.
- This work provides a valuable tool for identifying novel nucleolar proteins.
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