Mitochondrial Medicine pp 305-320

Part of the Methods in Molecular Biology book series (MIMB, volume 1264) | Cite as

Computer-Based Prediction of Mitochondria-Targeting Peptides

  • Pier Luigi Martelli
  • Castrense Savojardo
  • Piero Fariselli
  • Gianluca Tasco
  • Rita Casadio
Protocol

Abstract

Computational methods are invaluable when protein sequences, directly derived from genomic data, need functional and structural annotation. Subcellular localization is a feature necessary for understanding the protein role and the compartment where the mature protein is active and very difficult to characterize experimentally. Mitochondrial proteins encoded on the cytosolic ribosomes carry specific patterns in the precursor sequence from where it is possible to recognize a peptide targeting the protein to its final destination. Here we discuss to which extent it is feasible to develop computational methods for detecting mitochondrial targeting peptides in the precursor sequences and benchmark our and other methods on the human mitochondrial proteins endowed with experimentally characterized targeting peptides. Furthermore, we illustrate our newly implemented web server and its usage on the whole human proteome in order to infer mitochondrial targeting peptides, their cleavage sites, and whether the targeting peptide regions contain or not arginine-rich recurrent motifs. By this, we add some other 2,800 human proteins to the 124 ones already experimentally annotated with a mitochondrial targeting peptide.

Key words

Targeting peptide Prediction of subcellular localization Arginine motifs Cleavage site Machine learning 

References

  1. 1.
    Goffart S, Martinsson P, Malka F, Rojo M, Spelbrink JN (2007) The mitochondria of cultured mammalian cells: II. Expression and visualization of exogenous proteins in fixed and live cells. Methods Mol Biol 372:17–32PubMedCrossRefGoogle Scholar
  2. 2.
    Meisinger C, Sickmann A, Pfanner N (2008) The mitochondrial proteome: from inventory to function. Cell 134:22–24PubMedCrossRefGoogle Scholar
  3. 3.
    Pagliarini DJ, Calvo SE, Chang B, Sheth SA, Vafai SB, Ong SE, Walford GA, Sugiana C, Boneh A, Chen WK et al (2008) A mitochondrial protein compendium elucidates complex I disease biology. Cell 134:112–123PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Smith AC, Blackshaw JA, Robinson AJ (2012) MitoMiner: a data warehouse for mitochondrial proteomics data. Nucleic Acids Res 40:D1160–D1167PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Schmidt O, Pfanner N, Meisinger C (2010) Mitochondrial protein import: from proteomics to functional mechanisms. Nat Rev Mol Cell Biol 11:655–667PubMedCrossRefGoogle Scholar
  6. 6.
    Mossmann D, Meisinger C, Vögtle FN (2012) Processing of mitochondrial presequences. Biochim Biophys Acta 1819:1098–1106PubMedCrossRefGoogle Scholar
  7. 7.
    Small I, Peeters N, Legeai F, Lurin C (2004) Predotar: a tool for rapidly screening proteomes for N-terminal targeting sequences. Proteomics 4:1581–1590PubMedCrossRefGoogle Scholar
  8. 8.
    Petsalaki EI, Bagos PG, Litou ZI, Hamodrakas SJ (2006) PredSL: a tool for the N-terminal sequence-based prediction of protein subcellular localisation. Genomics Proteomics Bioinformatics 4:48–55PubMedCrossRefGoogle Scholar
  9. 9.
    Emanuelsson O, Brunak S, von Heijne G, Nielsen H (2007) Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc 2:953–971PubMedCrossRefGoogle Scholar
  10. 10.
    Indio V, Martelli PL, Savojardo C, Fariselli P, Casadio R (2013) The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields. Bioinformatics 29:981–988PubMedCrossRefGoogle Scholar
  11. 11.
    Claros MG, Vincens P (1996) Computational method to predict mitochondrially imported proteins and their targeting sequences. Eur J Biochem 241:779–786PubMedCrossRefGoogle Scholar
  12. 12.
    Bannai H, Tamada Y, Maruyama O, Nakai K, Miyano S (2002) Extensive feature detection of N-terminal protein sorting signals. Bioinformatics 18:298–305PubMedCrossRefGoogle Scholar
  13. 13.
    Pierleoni A, Martelli PL, Fariselli P, Casadio R (2006) BaCelLo: a balanced subcellular localisation predictor. Bioinformatics 22:e408–e416PubMedCrossRefGoogle Scholar
  14. 14.
    UniProt Consortium (2014) Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res 42:D191–D198CrossRefGoogle Scholar
  15. 15.
    Crooks GE, Hon G, Chandonia JM, Brenner SE (2004) WebLogo: a sequence logo generator. Genome Res 14:1188–1190PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Fariselli P, Savojardo C, Martelli PL, Casadio R (2009) Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics applications. Algorithms Mol Biol 4:13PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Pier Luigi Martelli
    • 1
  • Castrense Savojardo
    • 1
  • Piero Fariselli
    • 1
    • 2
  • Gianluca Tasco
    • 1
  • Rita Casadio
    • 1
    • 3
  1. 1.Biocomputing Group, CIRI Health Sciences & Technologies (HST)University of BolognaBolognaItaly
  2. 2.DISI, Department of Computer Science and EngineeringUniversity of BolognaBolognaItaly
  3. 3.Biocomputing Group, c/o BIGEABolognaItaly

Personalised recommendations