Analysis and prediction of leucine-rich nuclear export signals
Tanja la Cour, Lars Kiemer, Anne
Mølgaard, Ramneek Gupta, Karen Skriver
and Søren Brunak
Protein Eng. Des. Sel., 17(6):527-36, 2004.
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.