Karyopherin-mediated nucleocytoplasmic transport

The best understood system for the transport of macromolecules between the cytoplasm and the nucleus is the classical nuclear import pathway. In this pathway, a protein containing a classical basic nuclear localization signal (NLS) is imported by a heterodimeric import receptor consisting of the beta-karyopherin importin beta, which mediates interactions with the nuclear pore complex, and the adaptor protein importin alpha, which directly binds the classical NLS. Here we review recent studies that have advanced our understanding of this pathway and also take a bioinformatics approach to analyze the likely prevalence of this system in vivo. Finally, we describe how a predicted NLS within a protein of interest can be confirmed experimentally to be functionally important.

In a rapidly growing yeast cell, 60% of total transcription is devoted to ribosomal RNA, and 50% of RNA polymerase II transcription and 90% of mRNA splicing are devoted to ribosomal proteins (RPs). Coordinate regulation of the approximately 150 rRNA genes and 137 RP genes that make such prodigious use of resources is essential for the economy of the cell. This is entrusted to a number of signal transduction pathways that can abruptly induce or silence the ribosomal genes, leading to major implications for the expression of other genes as well.

Background Nuclear localization signals (NLSs) are stretches of residues within a protein that are important for the regulated nuclear import of the protein. Of the many import pathways that exist in yeast, the best characterized is termed the 'classical' NLS pathway. The classical NLS contains specific patterns of basic residues and computational methods have been designed to predict the location of these motifs on proteins. The consensus sequences, or patterns, for the other import pathways are less well-understood. Results In this paper, we present an analysis of characterized NLSs in yeast, and find, despite the large number of nuclear import pathways, that NLSs seem to show similar patterns of amino acid residues. We test current prediction methods and observe a low true positive rate. We therefore suggest an approach using hidden Markov models (HMMs) to predict novel NLSs in proteins. We show that our method is able to consistently find 37% of the NLSs with a low false positive rate and that our method retains its true positive rate outside of the yeast data set used for the training parameters. Conclusion Our implementation of this model, NLStradamus, is made available at: