Protein sub-cellular localization prediction for special compartments via optimized time series distances.
Predicting the sub-cellular localization of proteins is an important task in bioinformatics, for which many standard prediction tools are available. While these tools are powerful in general and capable of predicting protein localization for the most common compartments, their performance strongly depends on the organism of interest. More importantly, there are special compartments, such as the apicoplast of apicomplexan parasites, for which these tools cannot provide a prediction at all. In the absence of a highly conserved targeting signal, even motif searches may not be able to provide a lead for the accurate prediction of protein localization for a compartment of interest. In order to approach difficult cases of that kind, we propose an alternative method that complements existing approaches by using a more targeted protein sequence model. Moreover, our method makes use of (weighted) measures for time series comparison. To demonstrate its performance, we use this method for predicting localization in special compartments of three different species, for which existing methods yield only sub-optimal results. As shown experimentally, our method is indeed capable of producing reliable predictions of sub-cellular localization for difficult cases, i.e. if training data is scarce and a potential protein targeting signal may not be well conserved.
Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein Straße, Marburg 35032, Germany.
Marco
M
Mernberger
Daniel
D
Moog
Simone
S
Stork
Stefan
S
Zauner
Uwe G
UG
Maier
Eyke
E
Hüllermeier
D063546:Apicoplasts / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D010963:Plasmodium falciparum / Q000378:metabolism; D018087:Plastids / Q000378:metabolism; D021382:Protein Sorting Signals; D011506:Proteins / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013347:Subcellular Fractions; D014122:Toxoplasma / Q000378:metabolism
D021382:Protein Sorting Signals; D011506:Proteins