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Book Chapter

A Guide to Computational Methods for Predicting Mitochondrial Localization

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Sun,  Su
Habermann, Bianca / Computational Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Habermann,  Bianca H.
Habermann, Bianca / Computational Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Citation

Sun, S., & Habermann, B. H. (2017). A Guide to Computational Methods for Predicting Mitochondrial Localization. In D. Mokranjac, & F. Perocchi (Eds.), Mitochondria (pp. 1-14). New York, NY: Humana Press.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002E-80ED-8
Abstract
Predicting mitochondrial localization of proteins remains challenging for two main reasons: (1) Not only one but several mitochondrial localization signals exist, which primarily dictate the final destination of a protein in this organelle. However, most localization prediction algorithms rely on the presence of a so-called presequence (or N-terminal mitochondrial targeting peptide, mTP), which occurs in only similar to 70% of mitochondrial proteins. (2) The presequence is highly divergent on sequence level and therefore difficult to identify on the computer. In this chapter, we review a number of protein localization prediction programs and propose a strategy to predict mitochondrial localization. Finally, we give some helpful suggestions for bench scientists when working with mitochondrial protein candidates in silico.