Skip to main content
SpringerLink
Log in
Book cover

Mitochondria pp 1–14Cite as

A Guide to Computational Methods for Predicting Mitochondrial Localization

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1567))

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.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Nunnari J, Suomalainen A (2012) Mitochondria: in sickness and in health. Cell 148:1145–1159

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  3. Wallace DC, Fan W (2009) The pathophysiology of mitochondrial disease as modeled in the mouse. Genes Dev 23:1714–1736

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Calvo SE, Mootha VK (2010) The mitochondrial proteome and human disease. Annu Rev Genomics Hum Genet 11:25–44

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Omura T (1998) Mitochondria-targeting sequence, a multi-role sorting sequence recognized at all steps of protein import into mitochondria. J Biochem 123:1010–1016

    Article  CAS  PubMed  Google Scholar 

  6. Gaston D, Tsaousis AD, Roger AJ (2009) Predicting proteomes of mitochondria and related organelles from genomic and expressed sequence tag data. Methods Enzymol 457:21–47

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  9. Emanuelsson O, Nielsen H, Brunak S et al (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol 300:1005–1016

    Article  CAS  PubMed  Google Scholar 

  10. Bodén M, Hawkins J (2005) Prediction of subcellular localization using sequence-biased recurrent networks. Bioinformatics 21:2279–2286

    Article  PubMed  Google Scholar 

  11. Yu C-S, Chen Y-C, Lu C-H et al (2006) Prediction of protein subcellular localization. Proteins 64:643–651

    Article  CAS  PubMed  Google Scholar 

  12. Horton P, Park K-J, Obayashi T et al (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Res 35:W585–W587

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bannai H, Tamada Y, Maruyama O et al (2002) Extensive feature detection of N-terminal protein sorting signals. Bioinformatics 18:298–305

    Article  CAS  PubMed  Google Scholar 

  14. Nakai K, Horton P (1999) PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem Sci 24:34–36

    Article  CAS  PubMed  Google Scholar 

  15. Lu Z, Szafron D, Greiner R et al (2004) Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics 20:547–556

    Article  CAS  PubMed  Google Scholar 

  16. Shatkay H, Höglund A, Brady S et al (2007) SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data. Bioinformatics 23:1410–1417

    Article  CAS  PubMed  Google Scholar 

  17. Shen YQ, Burger G (2007) “Unite and conquer”: enhanced prediction of protein subcellular localization by integrating multiple specialized tools. BMC Bioinformatics 8:420

    Article  PubMed  PubMed Central  Google Scholar 

  18. Gardy JL, Spencer C, Wang K et al (2003) PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria. Nucleic Acids Res 31:3613–3617

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Indio V, Martelli PL, Savojardo C et al (2013) The prediction of organelle-targeting peptides in eukaryotic proteins with grammatical-restrained hidden conditional random fields. Bioinformatics 29:981–988

    Article  CAS  PubMed  Google Scholar 

  20. Savojardo C, Martelli PL, Fariselli P et al (2014) TPpred2: improving the prediction of mitochondrial targeting peptide cleavage sites by exploiting sequence motifs. Bioinformatics 30:2973–2974

    Article  CAS  PubMed  Google Scholar 

  21. Fukasawa Y, Tsuji J, Fu S-C et al (2015) MitoFates: improved prediction of mitochondrial targeting sequences and their cleavage sites. Mol Cell Proteomics 14:1113–1126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Vögtle F-N, Wortelkamp S, Zahedi RP et al (2009) Global analysis of the mitochondrial N-proteome identifies a processing peptidase critical for protein stability. Cell 139:428–439

    Article  PubMed  Google Scholar 

  23. Huang S, Taylor NL, Whelan J et al (2009) Refining the definition of plant mitochondrial presequences through analysis of sorting signals, N-terminal modifications, and cleavage motifs. Plant Physiol 150:1272–1285

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Tardif M, Atteia A, Specht M et al (2012) PredAlgo: a new subcellular localization prediction tool dedicated to green algae. Mol Biol Evol 29:3625–3639

    Article  CAS  PubMed  Google Scholar 

  25. Briesemeister S, Blum T, Brady S et al (2009) SherLoc2: a high-accuracy hybrid method for predicting subcellular localization of proteins. J Proteome Res 8:5363–5366

    Article  CAS  PubMed  Google Scholar 

  26. Yu C-S, Cheng C-W, Su W-C et al (2014) CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation. PLoS One 9:e99368

    Article  PubMed  PubMed Central  Google Scholar 

  27. Resource Coordinators NCBI (2014) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 42:D7–17

    Article  Google Scholar 

  28. Li L, Stoeckert CJ Jr, Roos DS (2003) OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13(9):2178–2189

    Google Scholar 

  29. Datta RS, Meacham C, Samad B et al (2009) Berkeley PHOG: phylofacts orthology group prediction web server. Nucleic Acids Res 37:W84–W89

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sonnhammer ELL, Östlund G (2015) InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic. Nucleic Acids Res 43:D234–D239

    Article  PubMed  Google Scholar 

  31. Huerta-Cepas J, Bueno A, Dopazo J et al (2007) PhylomeDB: a database for genome-wide collections of gene phylogenies. Nucleic Acids Res 36:D491–D496

    Article  PubMed  PubMed Central  Google Scholar 

  32. Schreiber F, Patricio M, Muffato M et al (2014) TreeFam v9: a new website, more species and orthology-on-the-fly. Nucleic Acids Res 42:D922–D925

    Article  CAS  PubMed  Google Scholar 

  33. Vilella AJ, Severin J, Ureta-Vidal A et al (2009) EnsemblCompara GeneTrees: complete, duplication-aware phylogenetic trees in vertebrates. Genome Res 19:327–335

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Altschul SF, Madden TL, Schäffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Biology Group, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152, Martinsried, Germany

    Su Sun & Bianca H. Habermann

  2. Aix Marseille Université, CNRS, IBDM UMR 7288, 13288, Marseille, France

    Bianca H. Habermann

Authors
  1. Su Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bianca H. Habermann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bianca H. Habermann .

Editor information

Editors and Affiliations

  1. Biomedical Center-Physiological Chemistry, LMU Munich, Martinsried, Germany

    Dejana Mokranjac

  2. Gene Center, LMU Munich and Institute for Diabetes and Obesity, Helmholtz Zentrum München, Munich, Germany

    Fabiana Perocchi

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Sun, S., Habermann, B.H. (2017). A Guide to Computational Methods for Predicting Mitochondrial Localization. In: Mokranjac, D., Perocchi, F. (eds) Mitochondria. Methods in Molecular Biology, vol 1567. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6824-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6824-4_1

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6822-0

  • Online ISBN: 978-1-4939-6824-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation