Skip to main content
SpringerLink
Log in

Protein Sorting Prediction

  • Protocol
  • First Online:
Bacterial Protein Secretion Systems

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1615))

Abstract

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

References

  1. Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132

    Article  PubMed  CAS  Google Scholar 

  2. von Heijne G (1983) Patterns of amino acids near signal-sequence cleavage sites. Eur J Biochem 133:17–21

    Article  Google Scholar 

  3. Gardy JL, Laird MR, Chen F et al (2005) PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics 21:617–623

    Article  PubMed  CAS  Google Scholar 

  4. Rey S, Gardy J, Brinkman F (2005) Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria. BMC Genomics 6:162

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Nielsen H (2016) Predicting subcellular localization of proteins by bioinformatic algorithms. In: Bagnoli F, Rappuoli R (eds) Protein export in gram-positive bacteria. Current topics in microbiology and immunology. Springer, Berlin, Heidelberg

    Google Scholar 

  6. Nakashima H, Nishikawa K (1994) Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol 238:54–61

    Article  PubMed  CAS  Google Scholar 

  7. Andrade MA, O’Donoghue SI, Rost B (1998) Adaptation of protein surfaces to subcellular location. J Mol Biol 276:517–525

    Article  PubMed  CAS  Google Scholar 

  8. Reinhardt A, Hubbard T (1998) Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res 26:2230–2236

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Hua S, Sun Z (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17:721–728

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. The UniProt Consortium (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212

    Article  CAS  Google Scholar 

  12. Nair R, Rost B (2002a) Sequence conserved for subcellular localization. Protein Sci 11:2836–2847

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Yu C-S, Chen Y-C, Lu C-H, Hwang J-K (2006) Prediction of protein subcellular localization. Proteins 64:643–651

    Article  PubMed  CAS  Google Scholar 

  14. Nair R, Rost B (2002b) Inferring sub-cellular localization through automated lexical analysis. Bioinformatics 18(Suppl 1):S78–S86

    Article  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  PubMed  CAS  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  PubMed  CAS  Google Scholar 

  17. 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  PubMed  CAS  Google Scholar 

  18. Chou K-C, Shen H-B (2010) Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms. Nat Sci 2:1090–1103

    CAS  Google Scholar 

  19. Chou K-C, Shen H-B (2006) Large-scale predictions of gram-negative bacterial protein subcellular locations. J Proteome Res 5:3420–3428

    Article  PubMed  CAS  Google Scholar 

  20. Shen H-B, Chou K-C (2007) Gpos-PLoc: an ensemble classifier for predicting subcellular localization of gram-positive bacterial proteins. Protein Eng Des Sel 20:39–46

    Article  PubMed  CAS  Google Scholar 

  21. Shen H-B, Chou K-C (2010) Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of gram-negative bacterial proteins. J Theor Biol 264:326–333

    Article  PubMed  CAS  Google Scholar 

  22. Shen H-B, Chou K-C (2009) Gpos-mPLoc: a top-down approach to improve the quality of predicting subcellular localization of gram-positive bacterial proteins. Protein Pept Lett 16:1478–1484

    Article  PubMed  CAS  Google Scholar 

  23. Xiao X, Wu Z-C, Chou K-C (2011) A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites. PLoS One 6:e20592

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Wu Z-C, Xiao X, Chou K-C (2012) iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex gram-positive bacterial proteins. Protein Pept Lett 19:4–14

    Article  PubMed  CAS  Google Scholar 

  25. Stormo GD, Schneider TD, Gold L, Ehrenfeucht A (1982) Use of the “perceptron” algorithm to distinguish translational initiation sites in E. coli. Nucleic Acids Res 10:2997–3011

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Schneider TD, Stephens RM (1990) Sequence logos: a new way to display consensus sequences. Nucleic Acids Res 18:6097–6100

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Krogh A, Brown M, Mian IS et al (1994) Hidden Markov models in computational biology: applications to protein modeling. J Mol Biol 235:1501–1531

    Article  PubMed  CAS  Google Scholar 

  28. Sigrist CJA, de Castro E, Cerutti L et al (2013) New and continuing developments at PROSITE. Nucleic Acids Res 41:D344–D347

    Article  PubMed  CAS  Google Scholar 

  29. Finn RD, Bateman A, Clements J et al (2014) Pfam: the protein families database. Nucleic Acids Res 42:D222–D230

    Article  PubMed  CAS  Google Scholar 

  30. Haft DH, Selengut JD, Richter RA et al (2013) TIGRFAMs and genome properties in 2013. Nucleic Acids Res 41:D387–D395

    Article  PubMed  CAS  Google Scholar 

  31. Mitchell A, Chang H-Y, Daugherty L et al (2015) The InterPro protein families database: the classification resource after 15 years. Nucleic Acids Res 43:D213–D221

    Article  PubMed  Google Scholar 

  32. Rish I (2001) An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop Empir Methods Artif Intell. IBM, New York, pp 41–46

    Google Scholar 

  33. Szafron D, Lu P, Greiner R et al (2004) Proteome analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations. Nucleic Acids Res 32:W365–W371

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Briesemeister S, Rahnenführer J, Kohlbacher O (2010) Going from where to why—interpretable prediction of protein subcellular localization. Bioinformatics 26:1232–1238

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Hertz JA, Krogh AS, Palmer RG (1991) Introduction to the theory of neural computation. Westview Press, Redwood City, CA

    Google Scholar 

  36. Noble WS (2006) What is a support vector machine? Nat Biotechnol 24:1565–1567

    Article  PubMed  CAS  Google Scholar 

  37. Hobohm U, Scharf M, Schneider R, Sander C (1992) Selection of representative protein data sets. Protein Sci 1:409–417

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Höglund A, Dönnes P, Blum T et al (2006) MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition. Bioinformatics 22:1158–1165

    Article  PubMed  CAS  Google Scholar 

  39. Sander C, Schneider R (1991) Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins 9:56–68

    Article  PubMed  CAS  Google Scholar 

  40. Nielsen H, Engelbrecht J, von Heijne G, Brunak S (1996) Defining a similarity threshold for a functional protein sequence pattern: the signal peptide cleavage site. Proteins 24:165–177

    Article  PubMed  CAS  Google Scholar 

  41. Nielsen H, Wernersson R (2006) An overabundance of phase 0 introns immediately after the start codon in eukaryotic genes. BMC Genomics 7:256

    Article  PubMed  PubMed Central  Google Scholar 

  42. 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  PubMed  PubMed Central  CAS  Google Scholar 

  43. Baldi P, Brunak S, Chauvin Y et al (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16:412–424

    Article  PubMed  CAS  Google Scholar 

  44. Gorodkin J (2004) Comparing two K-category assignments by a K-category correlation coefficient. Comput Biol Chem 28:367–374

    Article  PubMed  CAS  Google Scholar 

  45. von Heijne G (1986) A new method for predicting signal sequence cleavage sites. Nucleic Acids Res 14:4683–4690

    Article  Google Scholar 

  46. McGeoch DJ (1985) On the predictive recognition of signal peptide sequences. Virus Res 3:271–286

    Article  PubMed  CAS  Google Scholar 

  47. von Heijne G, Abrahmsén L (1989) Species-specific variation in signal peptide design: implications for protein secretion in foreign hosts. FEBS Lett 244:439–446

    Article  Google Scholar 

  48. Nielsen H, Brunak S, Engelbrecht J, von Heijne G (1997) Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng 10:1–6

    Article  PubMed  CAS  Google Scholar 

  49. Nielsen H, Krogh A (1998) Prediction of signal peptides and signal anchors by a hidden Markov model. Proc Int Conf Intell Syst Mol Biol 6:122–130

    PubMed  CAS  Google Scholar 

  50. Bendtsen JD, Nielsen H, von Heijne G, Brunak S (2004) Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 340:783–795

    Article  PubMed  CAS  Google Scholar 

  51. Petersen TN, Brunak S, von Heijne G, Nielsen H (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785–786

    Article  PubMed  CAS  Google Scholar 

  52. Menne KML, Hermjakob H, Apweiler R (2000) A comparison of signal sequence prediction methods using a test set of signal peptides. Bioinformatics 16:741–742

    Article  PubMed  CAS  Google Scholar 

  53. Klee E, Ellis L (2005) Evaluating eukaryotic secreted protein prediction. BMC Bioinformatics 6:1–7

    Article  CAS  Google Scholar 

  54. Choo K, Tan T, Ranganathan S (2009) A comprehensive assessment of N-terminal signal peptides prediction methods. BMC Bioinformatics 10:S2

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Zhang X, Li Y, Li Y (2009) Evaluating signal peptide prediction methods for gram-positive bacteria. Biologia (Bratisl) 64:655–659

    Google Scholar 

  56. Hiller K, Grote A, Scheer M et al (2004) PrediSi: prediction of signal peptides and their cleavage positions. Nucleic Acids Res 32:W375–W379

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Gomi M, Sonoyama M, Mitaku S (2004) High performance system for signal peptide prediction: SOSUIsignal. Chem-Bio Inform J 4:142–147

    Article  CAS  Google Scholar 

  58. Frank K, Sippl MJ (2008) High-performance signal peptide prediction based on sequence alignment techniques. Bioinformatics 24:2172–2176

    Article  PubMed  CAS  Google Scholar 

  59. Broome-Smith JK, Gnaneshan S, Hunt LA et al (1994) Cleavable signal peptides are rarely found in bacterial cytoplasmic membrane proteins. Mol Membr Biol 11:3–8

    Article  PubMed  CAS  Google Scholar 

  60. Juncker AS, Willenbrock H, von Heijne G et al (2003) Prediction of lipoprotein signal peptides in gram-negative bacteria. Protein Sci 12:1652–1662

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Rahman O, Cummings SP, Harrington DJ, Sutcliffe IC (2008) Methods for the bioinformatic identification of bacterial lipoproteins encoded in the genomes of gram-positive bacteria. World J Microbiol Biotechnol 24:2377–2382

    Article  CAS  Google Scholar 

  62. Fariselli P, Finocchiaro G, Casadio R (2003) SPEPlip: the detection of signal peptide and lipoprotein cleavage sites. Bioinformatics 19:2498–2499

    Article  PubMed  CAS  Google Scholar 

  63. Bagos PG, Tsirigos KD, Liakopoulos TD, Hamodrakas SJ (2008) Prediction of lipoprotein signal peptides in gram-positive bacteria with a hidden Markov model. J Proteome Res 7:5082–5093

    Article  PubMed  CAS  Google Scholar 

  64. Cristóbal S, de Gier J-W, Nielsen H, von Heijne G (1999) Competition between Sec- and TAT-dependent protein translocation in Escherichia coli. EMBO J 18:2982–2990

    Article  PubMed  PubMed Central  Google Scholar 

  65. Rose RW, Brüser T, Kissinger JC, Pohlschröder M (2002) Adaptation of protein secretion to extremely high-salt conditions by extensive use of the twin-arginine translocation pathway. Mol Microbiol 45:943–950

    Article  PubMed  CAS  Google Scholar 

  66. Bendtsen JD, Nielsen H, Widdick D et al (2005a) Prediction of twin-arginine signal peptides. BMC Bioinformatics 6:167

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Bagos PG, Nikolaou EP, Liakopoulos TD, Tsirigos KD (2010) Combined prediction of Tat and Sec signal peptides with hidden Markov models. Bioinformatics 26:2811–2817

    Article  PubMed  CAS  Google Scholar 

  68. Binnewies TT, Bendtsen JD, Hallin PF et al (2005) Genome update: protein secretion systems in 225 bacterial genomes. Microbiology 151:1013–1016

    Article  PubMed  CAS  Google Scholar 

  69. Desvaux M, Hébraud M, Talon R, Henderson IR (2009) Secretion and subcellular localizations of bacterial proteins: a semantic awareness issue. Trends Microbiol 17:139–145

    Article  PubMed  CAS  Google Scholar 

  70. Bendtsen JD, Kiemer L, Fausbøll A, Brunak S (2005b) Non-classical protein secretion in bacteria. BMC Microbiol 5:58

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Yu L, Guo Y, Li Y et al (2010a) SecretP: identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition. J Theor Biol 267:1–6

    Article  PubMed  CAS  Google Scholar 

  72. Yu L, Luo J, Guo Y et al (2013) In silico identification of gram-negative bacterial secreted proteins from primary sequence. Comput Biol Med 43:1177–1181

    Article  PubMed  CAS  Google Scholar 

  73. Lloubes R, Bernadac A, Houot L, Pommier S (2013) Non classical secretion systems. Res Microbiol 164:655–663

    Article  PubMed  CAS  Google Scholar 

  74. Luo J, Li W, Liu Z et al (2015) A sequence-based two-level method for the prediction of type I secreted RTX proteins. Analyst 140:3048–3056

    Article  PubMed  CAS  Google Scholar 

  75. Burstein D, Zusman T, Degtyar E et al (2009) Genome-scale identification of Legionella pneumophila effectors using a machine learning approach. PLoS Pathog 5:e1000508

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Chen C, Banga S, Mertens K et al (2010) Large-scale identification and translocation of type IV secretion substrates by Coxiella burnetii. Proc Natl Acad Sci U S A 107:21755–21760

    Article  PubMed  PubMed Central  Google Scholar 

  77. Lifshitz Z, Burstein D, Peeri M et al (2013) Computational modeling and experimental validation of the Legionella and Coxiellavirulence-related type-IVB secretion signal. Proc Natl Acad Sci U S A 110:E707–E715

    Article  PubMed  PubMed Central  Google Scholar 

  78. Zou L, Nan C, Hu F (2013) Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles. Bioinformatics 29:3135–3142

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Wang Y, Wei X, Bao H, Liu S-L (2014) Prediction of bacterial type IV secreted effectors by C-terminal features. BMC Genomics 15:50

    Article  PubMed  PubMed Central  Google Scholar 

  80. McDermott JE, Corrigan A, Peterson E et al (2011) Computational prediction of type III and IV secreted effectors in gram-negative bacteria. Infect Immun 79:23–32

    Article  PubMed  CAS  Google Scholar 

  81. Anderson DM, Schneewind O (1997) A mRNA signal for the type III secretion of Yop proteins by Yersinia enterocolitica. Science 278:1140–1143

    Article  PubMed  CAS  Google Scholar 

  82. Samudrala R, Heffron F, McDermott JE (2009) Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems. PLoS Pathog 5:e1000375

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Arnold R, Brandmaier S, Kleine F et al (2009) Sequence-based prediction of type III secreted proteins. PLoS Pathog 5:e1000376

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Löwer M, Schneider G (2009) Prediction of type III secretion signals in genomes of gram-negative bacteria. PLoS One 4:e5917

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Wang Y, Zhang Q, Sun M, Guo D (2011) High-accuracy prediction of bacterial type III secreted effectors based on position-specific amino acid composition profiles. Bioinformatics 27:777–784

    Article  PubMed  CAS  Google Scholar 

  86. Wang Y, Sun M, Bao H, White AP (2013) T3_MM: a Markov model effectively classifies bacterial type III secretion signals. PLoS One 8:e58173

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Dong X, Zhang Y-J, Zhang Z (2013) Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes. PLoS One 8:e56632

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Dong X, Lu X, Zhang Z (2015) BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors. Database 2015:bav064

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Goldberg T, Rost B, Bromberg Y (2016) Computational prediction shines light on type III secretion origins. Sci Rep 6:34516

    Google Scholar 

  90. Klein P, Kanehisa M, DeLisi C (1985) The detection and classification of membrane-spanning proteins. Biochim Biophys Acta 815:468–476

    Article  PubMed  CAS  Google Scholar 

  91. von Heijne G (1992) Membrane protein structure prediction: hydrophobicity analysis and the positive-inside rule. J Mol Biol 225:487–494

    Article  Google Scholar 

  92. von Heijne G, Gavel Y (1988) Topogenic signals in integral membrane proteins. Eur J Biochem 174:671–678

    Article  Google Scholar 

  93. Paul C, Rosenbusch JP (1985) Folding patterns of porin and bacteriorhodopsin. EMBO J 4:1593–1597

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Vogel H, Jähnig F (1986) Models for the structure of outer-membrane proteins of Escherichia coli derived from raman spectroscopy and prediction methods. J Mol Biol 190:191–199

    Article  PubMed  CAS  Google Scholar 

  95. Krogh A, Larsson B, von Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580

    Article  PubMed  CAS  Google Scholar 

  96. Tusnády GE, Simon I (2001) The HMMTOP transmembrane topology prediction server. Bioinformatics 17:849–850

    Article  PubMed  Google Scholar 

  97. Möller S, Croning MDR, Apweiler R (2001) Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics 17:646–653

    Article  PubMed  Google Scholar 

  98. Elofsson A, von Heijne G (2007) Membrane protein structure: prediction versus reality. Annu Rev Biochem 76:125–140

    Article  PubMed  CAS  Google Scholar 

  99. Punta M, Forrest LR, Bigelow H et al (2007) Membrane protein prediction methods. Methods 41:460–474

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Tusnády GE, Simon I (2010) Topology prediction of helical transmembrane proteins: how far have we reached? Curr Protein Pept Sci 11:550–561

    Article  PubMed  Google Scholar 

  101. Käll L, Krogh A, Sonnhammer EL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338:1027–1036

    Article  PubMed  CAS  Google Scholar 

  102. Reynolds SM, Käll L, Riffle ME et al (2008) Transmembrane topology and signal peptide prediction using dynamic Bayesian networks. PLoS Comput Biol 4:e1000213

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Jones DT (2007) Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics 23:538–544

    Article  PubMed  CAS  Google Scholar 

  104. Nugent T, Jones DT (2009) Transmembrane protein topology prediction using support vector machines. BMC Bioinformatics 10:159

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Viklund H, Bernsel A, Skwark M, Elofsson A (2008) SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology. Bioinformatics 24:2928–2929

    Article  PubMed  CAS  Google Scholar 

  106. Viklund H, Elofsson A (2008) OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar. Bioinformatics 24:1662–1668

    Article  PubMed  CAS  Google Scholar 

  107. Viklund H, Elofsson A (2004) Best α-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information. Protein Sci 13:1908–1917

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Käll L, Krogh A, Sonnhammer EL (2005) An HMM posterior decoder for sequence feature prediction that includes homology information. Bioinformatics 21:i251–i257

    Article  PubMed  Google Scholar 

  109. Bernsel A, Viklund H, Falk J et al (2008) Prediction of membrane-protein topology from first principles. Proc Natl Acad Sci 105:7177–7181

    Article  PubMed  Google Scholar 

  110. Hessa T, Meindl-Beinker NM, Bernsel A et al (2007) Molecular code for transmembrane-helix recognition by the Sec61 translocon. Nature 450:1026–1030

    Article  PubMed  CAS  Google Scholar 

  111. Taylor PD, Attwood TK, Flower DR (2003) BPROMPT: a consensus server for membrane protein prediction. Nucleic Acids Res 31:3698–3700

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  112. Bernsel A, Viklund H, Hennerdal A, Elofsson A (2009) TOPCONS: consensus prediction of membrane protein topology. Nucleic Acids Res 37:W465–W468

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  113. Tsirigos KD, Peters C, Shu N et al (2015) The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic Acids Res 43:W401–W407

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. Hennerdal A, Elofsson A (2011) Rapid membrane protein topology prediction. Bioinformatics 27:1322–1323

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  115. Diederichs K, Freigang J, Umhau S et al (1998) Prediction by a neural network of outer membrane β-strand protein topology. Protein Sci 7:2413–2420

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Martelli PL, Fariselli P, Krogh A, Casadio R (2002) A sequence-profile-based HMM for predicting and discriminating β barrel membrane proteins. Bioinformatics 18:S46–S53

    Article  PubMed  Google Scholar 

  117. Bagos P, Liakopoulos T, Spyropoulos I, Hamodrakas S (2004a) A hidden Markov model method, capable of predicting and discriminating beta-barrel outer membrane proteins. BMC Bioinformatics 5:29

    Article  PubMed  PubMed Central  Google Scholar 

  118. Bagos PG, Liakopoulos TD, Spyropoulos IC, Hamodrakas SJ (2004b) PRED-TMBB: a web server for predicting the topology of β-barrel outer membrane proteins. Nucleic Acids Res 32:W400–W404

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  119. Bigelow HR, Petrey DS, Liu J et al (2004) Predicting transmembrane beta-barrels in proteomes. Nucleic Acids Res 32:2566–2577

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Bigelow H, Rost B (2006) PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins. Nucleic Acids Res 34:W186–W188

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  121. Bagos P, Liakopoulos T, Hamodrakas S (2005) Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method. BMC Bioinformatics 6:7

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  122. Jacoboni I, Martelli PL, Fariselli P et al (2001) Prediction of the transmembrane regions of β-barrel membrane proteins with a neural network-based predictor. Protein Sci 10:779–787

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  123. Natt NK, Kaur H, Raghava GPS (2004) Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods. Proteins 56:11–18

    Article  PubMed  CAS  Google Scholar 

  124. Hayat S, Elofsson A (2012) BOCTOPUS: improved topology prediction of transmembrane β barrel proteins. Bioinformatics 28:516–522

    Article  PubMed  CAS  Google Scholar 

  125. Hayat S, Peters C, Shu N et al (2016) Inclusion of dyad-repeat pattern improves topology prediction of transmembrane β-barrel proteins. Bioinformatics 32:1571–1573

    Article  PubMed  CAS  Google Scholar 

  126. Berven FS, Flikka K, Jensen HB, Eidhammer I (2004) BOMP: a program to predict integral β-barrel outer membrane proteins encoded within genomes of gram-negative bacteria. Nucleic Acids Res 32:W394–W399

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  127. Remmert M, Linke D, Lupas AN, Söding J (2009) HHomp—prediction and classification of outer membrane proteins. Nucleic Acids Res 37:W446–W451

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Savojardo C, Fariselli P, Casadio R (2011) Improving the detection of transmembrane β-barrel chains with N-to-1 extreme learning machines. Bioinformatics 27:3123–3128

    Article  PubMed  CAS  Google Scholar 

  129. Savojardo C, Fariselli P, Casadio R (2013) BETAWARE: a machine-learning tool to detect and predict transmembrane beta-barrel proteins in prokaryotes. Bioinformatics 29:504–505

    Article  PubMed  CAS  Google Scholar 

  130. Waldispühl J, Berger B, Clote P, Steyaert J-M (2006a) transFold: a web server for predicting the structure and residue contacts of transmembrane beta-barrels. Nucleic Acids Res 34:W189–W193

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  131. Waldispühl J, Berger B, Clote P, Steyaert J-M (2006b) Predicting transmembrane β-barrels and interstrand residue interactions from sequence. Proteins 65:61–74

    Article  PubMed  CAS  Google Scholar 

  132. Randall A, Cheng J, Sweredoski M, Baldi P (2008) TMBpro: secondary structure, β-contact and tertiary structure prediction of transmembrane β-barrel proteins. Bioinformatics 24:513–520

    Article  PubMed  CAS  Google Scholar 

  133. Nakai K, Kanehisa M (1991) Expert system for predicting protein localization sites in gram-negative bacteria. Proteins 11:95–110

    Article  PubMed  CAS  Google Scholar 

  134. Yu NY, Wagner JR, Laird MR et al (2010b) PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26:1608–1615

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  135. Magnus M, Pawlowski M, Bujnicki JM (2012) MetaLocGramN: a meta-predictor of protein subcellular localization for gram-negative bacteria. Biochim Biophys Acta 1824:1425–1433

    Article  PubMed  CAS  Google Scholar 

  136. Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  137. Bhasin M, Garg A, Raghava GPS (2005) PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 21:2522–2524

    Article  PubMed  CAS  Google Scholar 

  138. Goldberg T, Hecht M, Hamp T et al (2014) LocTree3 prediction of localization. Nucleic Acids Res 42:W350–W355

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  139. Goldberg T, Hamp T, Rost B (2012) LocTree2 predicts localization for all domains of life. Bioinformatics 28:i458–i465

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  140. Imai K, Asakawa N, Tsuji T et al (2008) SOSUI-GramN: high performance prediction for sub-cellular localization of proteins in gram-negative bacteria. Bioinformation 2:417–421

    Article  PubMed  PubMed Central  Google Scholar 

  141. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol. 25, Curran Associates, Inc., Red Hook, NY, pp 1097–1105

    Google Scholar 

  142. Dahl GE, Yu D, Deng L, Acero A (2012) Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans Audio Speech Lang Process 20:30–42

    Article  Google Scholar 

  143. Magnan CN, Baldi P (2014) SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics 30:2592–2597

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Xiong HY, Alipanahi B, Lee LJ et al (2015) The human splicing code reveals new insights into the genetic determinants of disease. Science 347:1254806

    Article  PubMed  CAS  Google Scholar 

  145. Sønderby SK, Sønderby CK, Nielsen H, Winther O (2015) Convolutional LSTM networks for subcellular localization of proteins. In: Dediu A-H, Hernández-Quiroz F, Martín-Vide C, Rosenblueth DA (eds) Algorithms for computational biology, Lecture notes in computer science, vol 9199. Springer International Publishing, New York, pp 68–80

    Chapter  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Technical University of Denmark, Kemitorvet, Building 208, DK-2800, Kgs. Lyngby, Denmark

    Henrik Nielsen

Authors
  1. Henrik Nielsen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Henrik Nielsen .

Editor information

Editors and Affiliations

  1. Institut de Microbiologie de la Méditerranée, Aix-Marseille Univ - CNRS, Marseille, France

    Laure Journet

  2. Institut de Microbiologie de la Méditerranée, Aix-Marseille Univ - CNRS, Marseille, France

    Eric Cascales

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Nielsen, H. (2017). Protein Sorting Prediction. In: Journet, L., Cascales, E. (eds) Bacterial Protein Secretion Systems. Methods in Molecular Biology, vol 1615. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7033-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7033-9_2

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7031-5

  • Online ISBN: 978-1-4939-7033-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation