Analysis and prediction of leucine-rich nuclear export signals
Technical University of Denmark
Indexed incrossrefpubmed
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…
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Authors
6Topics & keywords
Topics
Keywords
- Nuclear export signal
- Computer science
- Computational biology
- Nuclear localization sequence
- Flexibility (engineering)
- Nuclear transport
- Transcription factor
- Set (abstract data type)
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