Analysis and prediction of leucine-rich nuclear export signals

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...

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Bibliographic Details
Published in:Protein engineering, design and selection design and selection, 2004-06, Vol.17 (6), p.527-536
Main Authors: la Cour, Tanja, Kiemer, Lars, Mølgaard, Anne, Gupta, Ramneek, Skriver, Karen, Brunak, Søren
Format: Article
Language:English
Subjects:
Active Transport, Cell Nucleus
Algorithms
analysis
Artificial Intelligence
Aspartic Acid - chemistry
Computational Biology - methods
Computing Methodologies
Consensus Sequence
Databases, Protein
Glutamic Acid - chemistry
Hydrophobic and Hydrophilic Interactions
Internet
Isoelectric Point
Leucine - chemistry
Markov Chains
Models, Molecular
NES
Neural Networks (Computer)
nuclear export signals
Nuclear Proteins - chemistry
prediction
Protein Sorting Signals
Protein Structure, Secondary
Protein Structure, Tertiary
Reproducibility of Results
ROC Curve
Sequence Alignment
Serine - chemistry
Structural Homology, Protein
structure
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Summary: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/.
ISSN:1741-0126
1741-0134
DOI:10.1093/protein/gzh062