A Guide to Computational Methods for Predicting Mitochondrial Localization.

Su Sun , Bianca Habermann (1, 2)

1 Computational Biology Group [Martinsried, Germany]
2 IBDM - Institut de Biologie du Développement de Marseille

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.

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

Domaines: Bio-informatique [q-bio.QM]

Su Sun, Bianca Habermann. A Guide to Computational Methods for Predicting Mitochondrial Localization.. Methods in Molecular Biology, 2018, pp.1-14. ⟨10.1007/978-1-4939-6824-4_1⟩. ⟨hal-01874603⟩