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Methods Mol Biol. 2017;1567:1-14. doi: 10.1007/978-1-4939-6824-4_1.

A Guide to Computational Methods for Predicting Mitochondrial Localization.

Author information

1
Computational Biology Group, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152, Martinsried, Germany.
2
Computational Biology Group, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152, Martinsried, Germany. habermann@biochem.mpg.de.
3
Aix Marseille Université, CNRS, IBDM UMR 7288, 13288, Marseille, France. habermann@biochem.mpg.de.

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 ~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.

KEYWORDS:

In silico; Mitochondrial protein localization; Mitochondrial targeting peptide; Prediction methods; Protein localization algorithms

PMID:
28276009
DOI:
10.1007/978-1-4939-6824-4_1
[Indexed for MEDLINE]

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