Abstract
Peroxisomes harbor a plethora of proteins, but the peroxisomal proteome as the entirety of all peroxisomal proteins is still unknown for mammalian species. Computational algorithms can be used to predict the subcellular localization of proteins based on their amino acid sequence and this method has been amply used to forecast the intracellular fate of individual proteins. However, when applying such algorithms systematically to all proteins of an organism the prediction of its peroxisomal proteome in silico should be possible. Therefore, a reliable detection of peroxisomal targeting signals (PTS ) acting as postal codes for the intracellular distribution of the encoding protein is crucial. Peroxisomal proteins can utilize different routes to reach their destination depending on the type of PTS. Accordingly, independent prediction algorithms have been developed for each type of PTS, but only those for type-1 motifs (PTS1) have so far reached a satisfying predictive performance. This is partially due to the low number of peroxisomal proteins limiting the power of statistical analyses and partially due to specific properties of peroxisomal protein import, which render functional PTS motifs inactive in specific contexts. Moreover, the prediction of the peroxisomal proteome is limited by the high number of proteins encoded in mammalian genomes, which causes numerous false positive predictions even when using reliable algorithms and buries the few yet unidentified peroxisomal proteins. Thus, the application of prediction algorithms to identify all peroxisomal proteins is currently ineffective as stand-alone method, but can display its full potential when combined with other methods.
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Abbreviations
- ANN:
-
Artificial neural network
- HMM:
-
Hidden Markov Model
- mPTS:
-
PTS for membrane proteins
- PBD:
-
Peroxisome biogenesis disorder
- PEX:
-
Peroxin
- PSSM:
-
Position specific scoring matrix
- PTS:
-
Peroxisomal targeting signal
- SETD:
-
Single enzyme and transporter deficiency
- SVM:
-
Support vector machines
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The author is grateful for multiple support, especially to Sebastian Maurer-Stroh, and Hugo Malagon-Vina for valuable discussions and to Fabian Dorninger for critically reading the manuscript.
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Kunze, M. (2018). Predicting Peroxisomal Targeting Signals to Elucidate the Peroxisomal Proteome of Mammals. In: del Río, L., Schrader, M. (eds) Proteomics of Peroxisomes. Subcellular Biochemistry, vol 89. Springer, Singapore. https://doi.org/10.1007/978-981-13-2233-4_7
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