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Computer-Based Prediction of Mitochondria-Targeting Peptides

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

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

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References

  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 372:17–32

    CAS  PubMed  CrossRef  Google Scholar 

  2. Meisinger C, Sickmann A, Pfanner N (2008) The mitochondrial proteome: from inventory to function. Cell 134:22–24

    CAS  PubMed  CrossRef  Google Scholar 

  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 134:112–123

    CAS  PubMed Central  PubMed  CrossRef  Google Scholar 

  4. Smith AC, Blackshaw JA, Robinson AJ (2012) MitoMiner: a data warehouse for mitochondrial proteomics data. Nucleic Acids Res 40:D1160–D1167

    CAS  PubMed Central  PubMed  CrossRef  Google Scholar 

  5. Schmidt O, Pfanner N, Meisinger C (2010) Mitochondrial protein import: from proteomics to functional mechanisms. Nat Rev Mol Cell Biol 11:655–667

    CAS  PubMed  CrossRef  Google Scholar 

  6. Mossmann D, Meisinger C, Vögtle FN (2012) Processing of mitochondrial presequences. Biochim Biophys Acta 1819:1098–1106

    CAS  PubMed  CrossRef  Google Scholar 

  7. Small I, Peeters N, Legeai F, Lurin C (2004) Predotar: a tool for rapidly screening proteomes for N-terminal targeting sequences. Proteomics 4:1581–1590

    CAS  PubMed  CrossRef  Google Scholar 

  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 4:48–55

    CAS  PubMed  CrossRef  Google Scholar 

  9. Emanuelsson O, Brunak S, von Heijne G, Nielsen H (2007) Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc 2:953–971

    CAS  PubMed  CrossRef  Google Scholar 

  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 29:981–988

    CAS  PubMed  CrossRef  Google Scholar 

  11. Claros MG, Vincens P (1996) Computational method to predict mitochondrially imported proteins and their targeting sequences. Eur J Biochem 241:779–786

    CAS  PubMed  CrossRef  Google Scholar 

  12. Bannai H, Tamada Y, Maruyama O, Nakai K, Miyano S (2002) Extensive feature detection of N-terminal protein sorting signals. Bioinformatics 18:298–305

    CAS  PubMed  CrossRef  Google Scholar 

  13. Pierleoni A, Martelli PL, Fariselli P, Casadio R (2006) BaCelLo: a balanced subcellular localisation predictor. Bioinformatics 22:e408–e416

    CAS  PubMed  CrossRef  Google Scholar 

  14. UniProt Consortium (2014) Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res 42:D191–D198

    CrossRef  Google Scholar 

  15. Crooks GE, Hon G, Chandonia JM, Brenner SE (2004) WebLogo: a sequence logo generator. Genome Res 14:1188–1190

    CAS  PubMed Central  PubMed  CrossRef  Google Scholar 

  16. Fariselli P, Savojardo C, Martelli PL, Casadio R (2009) Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics applications. Algorithms Mol Biol 4:13

    PubMed Central  PubMed  CrossRef  Google Scholar 

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Acknowledgments

This work was supported by the following projects: PRIN 2010–2011 project 20108XYHJS (to P.L.M.) (Italian Ministry for University and Research: MIUR), COST BMBS Action TD1101 (European Union RTD Framework Program, to R.C.), PON projects PON01_02249 and PAN Lab PONa3_00166 (Italian Ministry for University and Research, to R.C. and P.L.M.), and FARB-UNIBO 2012 (to R.C.).

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Correspondence to Rita Casadio .

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Martelli, P.L., Savojardo, C., Fariselli, P., Tasco, G., Casadio, R. (2015). Computer-Based Prediction of Mitochondria-Targeting Peptides. In: Weissig, V., Edeas, M. (eds) Mitochondrial Medicine. Methods in Molecular Biology, vol 1264. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2257-4_27

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  • DOI: https://doi.org/10.1007/978-1-4939-2257-4_27

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2256-7

  • Online ISBN: 978-1-4939-2257-4

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