Analysis and prediction of leucine-rich nuclear export signals

TLDR

Nuclear export signals regulate protein subcellular localization, influencing transcription and other nuclear processes, and are implicated in cancer, cell cycle, and differentiation. The study conducts a comprehensive analysis of NESs and releases a publicly available prediction server. The prediction algorithm combines neural networks and hidden Markov models, validated on recently discovered NESs. The NetNES predictor outperforms consensus patterns, demonstrating that accessibility, flexibility, and hydrophobic residues are critical NES determinants, and is freely available online.

Abstract

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/.