Journal article
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 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/.
Language: | English |
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Year: | 2004 |
Pages: | 527-536 |
ISSN: | 17410134 and 17410126 |
Types: | Journal article |
DOI: | 10.1093/protein/gzh062 |
ORCIDs: | Gupta, Ramneek and 0000-0003-2225-4012 |
Active Transport, Cell Nucleus Algorithms Artificial Intelligence Aspartic Acid Computational Biology Computing Methodologies Consensus Sequence Databases, Protein Glutamic Acid Hydrophobic and Hydrophilic Interactions Internet Isoelectric Point Leucine Markov Chains Models, Molecular Neural Networks, Computer Nuclear Proteins Protein Sorting Signals Protein Structure, Secondary Protein Structure, Tertiary ROC Curve Reproducibility of Results Sequence Alignment Serine Structural Homology, Protein