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PEDS Advance Access originally published online on August 16, 2004
Protein Engineering Design and Selection 2004 17(6):527-536; doi:10.1093/protein/gzh062
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Protein Engineering, Design & Selection vol. 17 no. 6 © Oxford University Press 2004; all rights reserved

Analysis and prediction of leucine-rich nuclear export signals

Tanja la Cour1,2, Lars Kiemer1,2, Anne Mølgaard1, Ramneek Gupta1, Karen Skriver3 and Søren Brunak1,4

1Center for Biological Sequence Analysis, Biocentrum-DTU, Technical University of Denmark, Building 208, DK-2800 Lyngby and 3Department of Protein Chemistry, Institute of Molecular Biology, University of Copenhagen, Øster Farimagsgade 2A, DK-1353 Copenhagen K, Denmark

4 To whom correspondence should be addressed. E-mail: brunak{at}cbs.dtu.dk

We present a thorough analysis of nuclear export signals and a prediction server, which we have made publicly available. The machine learning prediction method is a significant improvement over the generally used consensus patterns. Nuclear export signals (NESs) are extremely important regulators of the subcellular location of proteins. This regulation has an impact on transcription and other nuclear processes, which are fundamental to the viability of the cell. NESs are studied in relation to cancer, the cell cycle, cell differentiation and other important aspects of molecular biology. Our conclusion from this analysis is that the most important properties of NESs are accessibility and flexibility allowing relevant proteins to interact with the signal. Furthermore, we show that not only the known hydrophobic residues are important in defining a nuclear export signals. We employ both neural networks and hidden Markov models in the prediction algorithm and verify the method on the most recently discovered NESs. The NES predictor (NetNES) is made available for general use at http://www.cbs.dtu.dk/.

Received June 15, 2004; accepted July 28, 2004.

Edited by P.Balaram


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