Bayesian Classifier for Anchored Protein Sorting Discovery

摘要

A typical cell has a size of only 10μm while it contains about a billion proteins. Transportation of these proteins from their synthesis sites to their target locations within or outside of the cell is precisely controlled by protein sorting signals. However, genome-wide understanding of protein sorting regulatory signals and mechanisms is still very limited. We formulate the protein sorting motif discovery problem as a classification problem and proposed a Bayesian classifier based motif discovery algorithm (BayesMotif) to find a common type of sorting motifs in which a highly conserved anchor is present along with a less conserved motif regions. Experiments showed that our algorithm has the advantage of finding long lowly conserved sorting signals compared to other protein motif discovery algorithms such as MEME. Our algorithm also has the advantage to easily include additional meta-sequence features that overcomes the limitation of PWM (position weight matrix).