Protein Sorting Prediction

  • Henrik NielsenEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1615)


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.

Key words

Protein sorting Subcellular location Secretion Transmembrane proteins Prediction Machine learning 


  1. 1.
    Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132PubMedCrossRefGoogle Scholar
  2. 2.
    von Heijne G (1983) Patterns of amino acids near signal-sequence cleavage sites. Eur J Biochem 133:17–21CrossRefGoogle Scholar
  3. 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 21:617–623PubMedCrossRefGoogle Scholar
  4. 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 6:162PubMedPubMedCentralCrossRefGoogle Scholar
  5. 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, HeidelbergGoogle Scholar
  6. 6.
    Nakashima H, Nishikawa K (1994) Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol 238:54–61PubMedCrossRefGoogle Scholar
  7. 7.
    Andrade MA, O’Donoghue SI, Rost B (1998) Adaptation of protein surfaces to subcellular location. J Mol Biol 276:517–525PubMedCrossRefGoogle Scholar
  8. 8.
    Reinhardt A, Hubbard T (1998) Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res 26:2230–2236PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Hua S, Sun Z (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17:721–728PubMedCrossRefGoogle Scholar
  10. 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 25:3389PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    The UniProt Consortium (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212CrossRefGoogle Scholar
  12. 12.
    Nair R, Rost B (2002a) Sequence conserved for subcellular localization. Protein Sci 11:2836–2847PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Yu C-S, Chen Y-C, Lu C-H, Hwang J-K (2006) Prediction of protein subcellular localization. Proteins 64:643–651PubMedCrossRefGoogle Scholar
  14. 14.
    Nair R, Rost B (2002b) Inferring sub-cellular localization through automated lexical analysis. Bioinformatics 18(Suppl 1):S78–S86PubMedCrossRefGoogle Scholar
  15. 15.
    Lu Z, Szafron D, Greiner R et al (2004) Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics 20:547–556PubMedCrossRefGoogle Scholar
  16. 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 23:1410–1417PubMedCrossRefGoogle Scholar
  17. 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 8:5363–5366PubMedCrossRefGoogle Scholar
  18. 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 2:1090–1103Google Scholar
  19. 19.
    Chou K-C, Shen H-B (2006) Large-scale predictions of gram-negative bacterial protein subcellular locations. J Proteome Res 5:3420–3428PubMedCrossRefGoogle Scholar
  20. 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 20:39–46PubMedCrossRefGoogle Scholar
  21. 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 264:326–333PubMedCrossRefGoogle Scholar
  22. 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 16:1478–1484PubMedCrossRefGoogle Scholar
  23. 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:e20592PubMedPubMedCentralCrossRefGoogle Scholar
  24. 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 19:4–14PubMedCrossRefGoogle Scholar
  25. 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 10:2997–3011PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Schneider TD, Stephens RM (1990) Sequence logos: a new way to display consensus sequences. Nucleic Acids Res 18:6097–6100PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Krogh A, Brown M, Mian IS et al (1994) Hidden Markov models in computational biology: applications to protein modeling. J Mol Biol 235:1501–1531PubMedCrossRefGoogle Scholar
  28. 28.
    Sigrist CJA, de Castro E, Cerutti L et al (2013) New and continuing developments at PROSITE. Nucleic Acids Res 41:D344–D347PubMedCrossRefGoogle Scholar
  29. 29.
    Finn RD, Bateman A, Clements J et al (2014) Pfam: the protein families database. Nucleic Acids Res 42:D222–D230PubMedCrossRefGoogle Scholar
  30. 30.
    Haft DH, Selengut JD, Richter RA et al (2013) TIGRFAMs and genome properties in 2013. Nucleic Acids Res 41:D387–D395PubMedCrossRefGoogle Scholar
  31. 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–D221PubMedCrossRefGoogle Scholar
  32. 32.
    Rish I (2001) An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop Empir Methods Artif Intell. IBM, New York, pp 41–46Google Scholar
  33. 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–W371PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Briesemeister S, Rahnenführer J, Kohlbacher O (2010) Going from where to why—interpretable prediction of protein subcellular localization. Bioinformatics 26:1232–1238PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Hertz JA, Krogh AS, Palmer RG (1991) Introduction to the theory of neural computation. Westview Press, Redwood City, CAGoogle Scholar
  36. 36.
    Noble WS (2006) What is a support vector machine? Nat Biotechnol 24:1565–1567PubMedCrossRefGoogle Scholar
  37. 37.
    Hobohm U, Scharf M, Schneider R, Sander C (1992) Selection of representative protein data sets. Protein Sci 1:409–417PubMedPubMedCentralCrossRefGoogle Scholar
  38. 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 22:1158–1165PubMedCrossRefGoogle Scholar
  39. 39.
    Sander C, Schneider R (1991) Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins 9:56–68PubMedCrossRefGoogle Scholar
  40. 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 24:165–177PubMedCrossRefGoogle Scholar
  41. 41.
    Nielsen H, Wernersson R (2006) An overabundance of phase 0 introns immediately after the start codon in eukaryotic genes. BMC Genomics 7:256PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Gardy JL, Spencer C, Wang K et al (2003) PSORT-B: improving protein subcellular localization prediction for gram-negative bacteria. Nucleic Acids Res 31:3613–3617PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Baldi P, Brunak S, Chauvin Y et al (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16:412–424PubMedCrossRefGoogle Scholar
  44. 44.
    Gorodkin J (2004) Comparing two K-category assignments by a K-category correlation coefficient. Comput Biol Chem 28:367–374PubMedCrossRefGoogle Scholar
  45. 45.
    von Heijne G (1986) A new method for predicting signal sequence cleavage sites. Nucleic Acids Res 14:4683–4690CrossRefGoogle Scholar
  46. 46.
    McGeoch DJ (1985) On the predictive recognition of signal peptide sequences. Virus Res 3:271–286PubMedCrossRefGoogle Scholar
  47. 47.
    von Heijne G, Abrahmsén L (1989) Species-specific variation in signal peptide design: implications for protein secretion in foreign hosts. FEBS Lett 244:439–446CrossRefGoogle Scholar
  48. 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 10:1–6PubMedCrossRefGoogle Scholar
  49. 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 6:122–130PubMedGoogle Scholar
  50. 50.
    Bendtsen JD, Nielsen H, von Heijne G, Brunak S (2004) Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 340:783–795PubMedCrossRefGoogle Scholar
  51. 51.
    Petersen TN, Brunak S, von Heijne G, Nielsen H (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785–786PubMedCrossRefGoogle Scholar
  52. 52.
    Menne KML, Hermjakob H, Apweiler R (2000) A comparison of signal sequence prediction methods using a test set of signal peptides. Bioinformatics 16:741–742PubMedCrossRefGoogle Scholar
  53. 53.
    Klee E, Ellis L (2005) Evaluating eukaryotic secreted protein prediction. BMC Bioinformatics 6:1–7CrossRefGoogle Scholar
  54. 54.
    Choo K, Tan T, Ranganathan S (2009) A comprehensive assessment of N-terminal signal peptides prediction methods. BMC Bioinformatics 10:S2PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Zhang X, Li Y, Li Y (2009) Evaluating signal peptide prediction methods for gram-positive bacteria. Biologia (Bratisl) 64:655–659Google Scholar
  56. 56.
    Hiller K, Grote A, Scheer M et al (2004) PrediSi: prediction of signal peptides and their cleavage positions. Nucleic Acids Res 32:W375–W379PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Gomi M, Sonoyama M, Mitaku S (2004) High performance system for signal peptide prediction: SOSUIsignal. Chem-Bio Inform J 4:142–147CrossRefGoogle Scholar
  58. 58.
    Frank K, Sippl MJ (2008) High-performance signal peptide prediction based on sequence alignment techniques. Bioinformatics 24:2172–2176PubMedCrossRefGoogle Scholar
  59. 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 11:3–8PubMedCrossRefGoogle Scholar
  60. 60.
    Juncker AS, Willenbrock H, von Heijne G et al (2003) Prediction of lipoprotein signal peptides in gram-negative bacteria. Protein Sci 12:1652–1662PubMedPubMedCentralCrossRefGoogle Scholar
  61. 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 24:2377–2382CrossRefGoogle Scholar
  62. 62.
    Fariselli P, Finocchiaro G, Casadio R (2003) SPEPlip: the detection of signal peptide and lipoprotein cleavage sites. Bioinformatics 19:2498–2499PubMedCrossRefGoogle Scholar
  63. 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 7:5082–5093PubMedCrossRefGoogle Scholar
  64. 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 18:2982–2990PubMedPubMedCentralCrossRefGoogle Scholar
  65. 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 45:943–950PubMedCrossRefGoogle Scholar
  66. 66.
    Bendtsen JD, Nielsen H, Widdick D et al (2005a) Prediction of twin-arginine signal peptides. BMC Bioinformatics 6:167PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Bagos PG, Nikolaou EP, Liakopoulos TD, Tsirigos KD (2010) Combined prediction of Tat and Sec signal peptides with hidden Markov models. Bioinformatics 26:2811–2817PubMedCrossRefGoogle Scholar
  68. 68.
    Binnewies TT, Bendtsen JD, Hallin PF et al (2005) Genome update: protein secretion systems in 225 bacterial genomes. Microbiology 151:1013–1016PubMedCrossRefGoogle Scholar
  69. 69.
    Desvaux M, Hébraud M, Talon R, Henderson IR (2009) Secretion and subcellular localizations of bacterial proteins: a semantic awareness issue. Trends Microbiol 17:139–145PubMedCrossRefGoogle Scholar
  70. 70.
    Bendtsen JD, Kiemer L, Fausbøll A, Brunak S (2005b) Non-classical protein secretion in bacteria. BMC Microbiol 5:58PubMedPubMedCentralCrossRefGoogle Scholar
  71. 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 267:1–6PubMedCrossRefGoogle Scholar
  72. 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 43:1177–1181PubMedCrossRefGoogle Scholar
  73. 73.
    Lloubes R, Bernadac A, Houot L, Pommier S (2013) Non classical secretion systems. Res Microbiol 164:655–663PubMedCrossRefGoogle Scholar
  74. 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 140:3048–3056PubMedCrossRefGoogle Scholar
  75. 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:e1000508PubMedPubMedCentralCrossRefGoogle Scholar
  76. 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 107:21755–21760PubMedPubMedCentralCrossRefGoogle Scholar
  77. 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–E715PubMedPubMedCentralCrossRefGoogle Scholar
  78. 78.
    Zou L, Nan C, Hu F (2013) Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles. Bioinformatics 29:3135–3142PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    Wang Y, Wei X, Bao H, Liu S-L (2014) Prediction of bacterial type IV secreted effectors by C-terminal features. BMC Genomics 15:50PubMedPubMedCentralCrossRefGoogle Scholar
  80. 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 79:23–32PubMedCrossRefGoogle Scholar
  81. 81.
    Anderson DM, Schneewind O (1997) A mRNA signal for the type III secretion of Yop proteins by Yersinia enterocolitica. Science 278:1140–1143PubMedCrossRefGoogle Scholar
  82. 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:e1000375PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Arnold R, Brandmaier S, Kleine F et al (2009) Sequence-based prediction of type III secreted proteins. PLoS Pathog 5:e1000376PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    Löwer M, Schneider G (2009) Prediction of type III secretion signals in genomes of gram-negative bacteria. PLoS One 4:e5917PubMedPubMedCentralCrossRefGoogle Scholar
  85. 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 27:777–784PubMedCrossRefGoogle Scholar
  86. 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:e58173PubMedPubMedCentralCrossRefGoogle Scholar
  87. 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:e56632PubMedPubMedCentralCrossRefGoogle Scholar
  88. 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:bav064PubMedPubMedCentralCrossRefGoogle Scholar
  89. 89.
    Goldberg T, Rost B, Bromberg Y (2016) Computational prediction shines light on type III secretion origins. Sci Rep 6:34516Google Scholar
  90. 90.
    Klein P, Kanehisa M, DeLisi C (1985) The detection and classification of membrane-spanning proteins. Biochim Biophys Acta 815:468–476PubMedCrossRefGoogle Scholar
  91. 91.
    von Heijne G (1992) Membrane protein structure prediction: hydrophobicity analysis and the positive-inside rule. J Mol Biol 225:487–494CrossRefGoogle Scholar
  92. 92.
    von Heijne G, Gavel Y (1988) Topogenic signals in integral membrane proteins. Eur J Biochem 174:671–678CrossRefGoogle Scholar
  93. 93.
    Paul C, Rosenbusch JP (1985) Folding patterns of porin and bacteriorhodopsin. EMBO J 4:1593–1597PubMedPubMedCentralCrossRefGoogle Scholar
  94. 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 190:191–199PubMedCrossRefGoogle Scholar
  95. 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 305:567–580PubMedCrossRefGoogle Scholar
  96. 96.
    Tusnády GE, Simon I (2001) The HMMTOP transmembrane topology prediction server. Bioinformatics 17:849–850PubMedCrossRefGoogle Scholar
  97. 97.
    Möller S, Croning MDR, Apweiler R (2001) Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics 17:646–653PubMedCrossRefGoogle Scholar
  98. 98.
    Elofsson A, von Heijne G (2007) Membrane protein structure: prediction versus reality. Annu Rev Biochem 76:125–140PubMedCrossRefGoogle Scholar
  99. 99.
    Punta M, Forrest LR, Bigelow H et al (2007) Membrane protein prediction methods. Methods 41:460–474PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Tusnády GE, Simon I (2010) Topology prediction of helical transmembrane proteins: how far have we reached? Curr Protein Pept Sci 11:550–561PubMedCrossRefGoogle Scholar
  101. 101.
    Käll L, Krogh A, Sonnhammer EL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338:1027–1036PubMedCrossRefGoogle Scholar
  102. 102.
    Reynolds SM, Käll L, Riffle ME et al (2008) Transmembrane topology and signal peptide prediction using dynamic Bayesian networks. PLoS Comput Biol 4:e1000213PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Jones DT (2007) Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics 23:538–544PubMedCrossRefGoogle Scholar
  104. 104.
    Nugent T, Jones DT (2009) Transmembrane protein topology prediction using support vector machines. BMC Bioinformatics 10:159PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Viklund H, Bernsel A, Skwark M, Elofsson A (2008) SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology. Bioinformatics 24:2928–2929PubMedCrossRefGoogle Scholar
  106. 106.
    Viklund H, Elofsson A (2008) OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar. Bioinformatics 24:1662–1668PubMedCrossRefGoogle Scholar
  107. 107.
    Viklund H, Elofsson A (2004) Best α-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information. Protein Sci 13:1908–1917PubMedPubMedCentralCrossRefGoogle Scholar
  108. 108.
    Käll L, Krogh A, Sonnhammer EL (2005) An HMM posterior decoder for sequence feature prediction that includes homology information. Bioinformatics 21:i251–i257PubMedCrossRefGoogle Scholar
  109. 109.
    Bernsel A, Viklund H, Falk J et al (2008) Prediction of membrane-protein topology from first principles. Proc Natl Acad Sci 105:7177–7181PubMedCrossRefGoogle Scholar
  110. 110.
    Hessa T, Meindl-Beinker NM, Bernsel A et al (2007) Molecular code for transmembrane-helix recognition by the Sec61 translocon. Nature 450:1026–1030PubMedCrossRefGoogle Scholar
  111. 111.
    Taylor PD, Attwood TK, Flower DR (2003) BPROMPT: a consensus server for membrane protein prediction. Nucleic Acids Res 31:3698–3700PubMedPubMedCentralCrossRefGoogle Scholar
  112. 112.
    Bernsel A, Viklund H, Hennerdal A, Elofsson A (2009) TOPCONS: consensus prediction of membrane protein topology. Nucleic Acids Res 37:W465–W468PubMedPubMedCentralCrossRefGoogle Scholar
  113. 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–W407PubMedPubMedCentralCrossRefGoogle Scholar
  114. 114.
    Hennerdal A, Elofsson A (2011) Rapid membrane protein topology prediction. Bioinformatics 27:1322–1323PubMedPubMedCentralCrossRefGoogle Scholar
  115. 115.
    Diederichs K, Freigang J, Umhau S et al (1998) Prediction by a neural network of outer membrane β-strand protein topology. Protein Sci 7:2413–2420PubMedPubMedCentralCrossRefGoogle Scholar
  116. 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–S53PubMedCrossRefGoogle Scholar
  117. 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 5:29PubMedPubMedCentralCrossRefGoogle Scholar
  118. 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–W404PubMedPubMedCentralCrossRefGoogle Scholar
  119. 119.
    Bigelow HR, Petrey DS, Liu J et al (2004) Predicting transmembrane beta-barrels in proteomes. Nucleic Acids Res 32:2566–2577PubMedPubMedCentralCrossRefGoogle Scholar
  120. 120.
    Bigelow H, Rost B (2006) PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins. Nucleic Acids Res 34:W186–W188PubMedPubMedCentralCrossRefGoogle Scholar
  121. 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 6:7PubMedPubMedCentralCrossRefGoogle Scholar
  122. 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 10:779–787PubMedPubMedCentralCrossRefGoogle Scholar
  123. 123.
    Natt NK, Kaur H, Raghava GPS (2004) Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods. Proteins 56:11–18PubMedCrossRefGoogle Scholar
  124. 124.
    Hayat S, Elofsson A (2012) BOCTOPUS: improved topology prediction of transmembrane β barrel proteins. Bioinformatics 28:516–522PubMedCrossRefGoogle Scholar
  125. 125.
    Hayat S, Peters C, Shu N et al (2016) Inclusion of dyad-repeat pattern improves topology prediction of transmembrane β-barrel proteins. Bioinformatics 32:1571–1573PubMedCrossRefGoogle Scholar
  126. 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–W399PubMedPubMedCentralCrossRefGoogle Scholar
  127. 127.
    Remmert M, Linke D, Lupas AN, Söding J (2009) HHomp—prediction and classification of outer membrane proteins. Nucleic Acids Res 37:W446–W451PubMedPubMedCentralCrossRefGoogle Scholar
  128. 128.
    Savojardo C, Fariselli P, Casadio R (2011) Improving the detection of transmembrane β-barrel chains with N-to-1 extreme learning machines. Bioinformatics 27:3123–3128PubMedCrossRefGoogle Scholar
  129. 129.
    Savojardo C, Fariselli P, Casadio R (2013) BETAWARE: a machine-learning tool to detect and predict transmembrane beta-barrel proteins in prokaryotes. Bioinformatics 29:504–505PubMedCrossRefGoogle Scholar
  130. 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–W193PubMedPubMedCentralCrossRefGoogle Scholar
  131. 131.
    Waldispühl J, Berger B, Clote P, Steyaert J-M (2006b) Predicting transmembrane β-barrels and interstrand residue interactions from sequence. Proteins 65:61–74PubMedCrossRefGoogle Scholar
  132. 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 24:513–520PubMedCrossRefGoogle Scholar
  133. 133.
    Nakai K, Kanehisa M (1991) Expert system for predicting protein localization sites in gram-negative bacteria. Proteins 11:95–110PubMedCrossRefGoogle Scholar
  134. 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 26:1608–1615PubMedPubMedCentralCrossRefGoogle Scholar
  135. 135.
    Magnus M, Pawlowski M, Bujnicki JM (2012) MetaLocGramN: a meta-predictor of protein subcellular localization for gram-negative bacteria. Biochim Biophys Acta 1824:1425–1433PubMedCrossRefGoogle Scholar
  136. 136.
    Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29PubMedPubMedCentralCrossRefGoogle Scholar
  137. 137.
    Bhasin M, Garg A, Raghava GPS (2005) PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 21:2522–2524PubMedCrossRefGoogle Scholar
  138. 138.
    Goldberg T, Hecht M, Hamp T et al (2014) LocTree3 prediction of localization. Nucleic Acids Res 42:W350–W355PubMedPubMedCentralCrossRefGoogle Scholar
  139. 139.
    Goldberg T, Hamp T, Rost B (2012) LocTree2 predicts localization for all domains of life. Bioinformatics 28:i458–i465PubMedPubMedCentralCrossRefGoogle Scholar
  140. 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 2:417–421PubMedPubMedCentralCrossRefGoogle Scholar
  141. 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 1097–1105Google Scholar
  142. 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 20:30–42CrossRefGoogle Scholar
  143. 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 30:2592–2597PubMedPubMedCentralCrossRefGoogle Scholar
  144. 144.
    Xiong HY, Alipanahi B, Lee LJ et al (2015) The human splicing code reveals new insights into the genetic determinants of disease. Science 347:1254806PubMedCrossRefGoogle Scholar
  145. 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 68–80CrossRefGoogle Scholar
  146. 146.
    Crooks GE, Hon G, Chandonia J-M, Brenner SE (2004) WebLogo: a sequence logo generator. Genome Res 14:1188–1190PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Technical University of DenmarkKgs. LyngbyDenmark

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