Protein Feature Based Identification of Cell Cycle Regulated Proteins
Ulrik de Lichtenberg1, Thomas Skøt Jensen2, Lars Juhl Jensen, Anders Fausbøll, Søren Brunak
1ulrik@cbs.dtu.dk, Center for Biological Sequence Analysis, BioCentrum-DTU, The Technical University of Denmark; 2skot@cbs.dtu.dk, Center for Biological Sequence Analysis, BioCentrum-DTU, The Technical University of Denmark
The gene repertoire driving the eukaryotic cell cycle has recently been investigated using DNA microarray techniques leading to large-scale identification of periodically expressed transcripts. DNA microarrays have been used extensively to identify such cell cycle regulated genes in yeast and human cells, however, the overlap in the sets of genes identified by different groups is surprisingly small.
We show that certain protein features can be used to distinguish cell cycle regulated genes from other genes with high confidence (features include protein phosphorylation, glycosylation, subcellular location and instability/degradation).
A protein feature based machine-learning prediction method was developed that identified a large set of novel putative cell cycle regulated proteins, many of which presently have no known function. The method complements the DNA microarray technology in establishing the correct set of cell cycle regulated genes in yeast, and could help to guide new experimental work.
The yeast analysis is to appear in JMB (Protein Feature Based Identification of Cell Cycle Regulated Proteins in Yeast, J. Mol. Biol.), and a similar bioinformatics method is under development for identifying and characterizing human cell cycle regulated genes.
For further details, see: http://www.cbs.dtu.dk/cellcycle/