TY - JOUR

T1 - Analysis and prediction of leucine-rich nuclear export signals.

AU - la Cour, Tanja

AU - Kiemer, Lars

AU - Mølgaard, Anne

AU - Gupta, Ramneek

AU - Skriver, Karen

AU - Brunak, Søren

N1 - Keywords: Active Transport, Cell Nucleus; Algorithms; Artificial Intelligence; Aspartic Acid; Computational Biology; Computing Methodologies; Consensus Sequence; Databases, Protein; Glutamic Acid; Hydrophobicity; 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

PY - 2004

Y1 - 2004

N2 - 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 https://www.cbs.dtu.dk/.

AB - 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 https://www.cbs.dtu.dk/.

U2 - 10.1093/protein/gzh062

DO - 10.1093/protein/gzh062

M3 - Journal article

C2 - 15314210

VL - 17

SP - 527

EP - 536

JO - Protein Engineering, Design and Selection

JF - Protein Engineering, Design and Selection

SN - 1741-0126

IS - 6

ER -