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Protein Sorting Prediction

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Bacterial Secretion Systems

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2715))

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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 are 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.

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References

  1. Nielsen H, Tsirigos KD, Brunak S, von Heijne G (2019) A brief history of protein sorting prediction. Protein J 38:200–216. https://doi.org/10.1007/s10930-019-09838-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132. https://doi.org/10.1016/0022-2836(82)90515-0

    Article  CAS  PubMed  Google Scholar 

  3. von Heijne G (1983) Patterns of amino acids near signal-sequence cleavage sites. Eur J Biochem 133:17–21. https://doi.org/10.1111/j.1432-1033.1983.tb07424.x

    Article  Google Scholar 

  4. 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. https://doi.org/10.1093/bioinformatics/bti057

    Article  CAS  PubMed  Google Scholar 

  5. 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. https://doi.org/10.1186/1471-2164-6-162

    Article  CAS  PubMed  PubMed Central  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. https://doi.org/10.1006/jmbi.1994.1267

    Article  CAS  PubMed  Google Scholar 

  7. Andrade MA, O’Donoghue SI, Rost B (1998) Adaptation of protein surfaces to subcellular location. J Mol Biol 276:517–525. https://doi.org/10.1006/jmbi.1997.1498

    Article  CAS  PubMed  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. https://doi.org/10.1093/nar/26.9.2230

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hua S, Sun Z (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17:721–728. https://doi.org/10.1093/bioinformatics/17.8.721

    Article  CAS  PubMed  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. https://doi.org/10.1093/nar/25.17.3389

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. The UniProt Consortium (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480–D489. https://doi.org/10.1093/nar/gkaa1100

    Article  CAS  Google Scholar 

  12. Nair R, Rost B (2002) Sequence conserved for subcellular localization. Protein Sci 11:2836–2847. https://doi.org/10.1110/ps.0207402

    Article  CAS  PubMed  PubMed Central  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. https://doi.org/10.1002/prot.21018

    Article  CAS  PubMed  Google Scholar 

  14. Nair R, Rost B (2002) Inferring sub-cellular localization through automated lexical analysis. Bioinformatics 18(Suppl 1):S78–S86. https://doi.org/10.1093/bioinformatics/18.suppl_1.S78

    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. https://doi.org/10.1093/bioinformatics/btg447

    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. https://doi.org/10.1093/bioinformatics/btm115

    Article  CAS  PubMed  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. https://doi.org/10.1021/pr900665y

    Article  CAS  PubMed  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 02:1090–1103. https://doi.org/10.4236/ns.2010.210136

    Article  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. https://doi.org/10.1021/pr060404b

    Article  CAS  PubMed  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. https://doi.org/10.1093/protein/gzl053

    Article  CAS  PubMed  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. https://doi.org/10.1016/j.jtbi.2010.01.018

    Article  CAS  PubMed  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. https://doi.org/10.2174/092986609789839322

    Article  CAS  PubMed  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. https://doi.org/10.1371/journal.pone.0020592

    Article  CAS  PubMed  PubMed Central  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. https://doi.org/10.2174/092986612798472839

    Article  CAS  PubMed  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. https://doi.org/10.1093/nar/10.9.2997

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Schneider TD, Stephens RM (1990) Sequence logos: a new way to display consensus sequences. Nucleic Acids Res 18:6097–6100. https://doi.org/10.1093/nar/18.20.6097

    Article  CAS  PubMed  PubMed Central  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. https://doi.org/10.1006/jmbi.1994.1104

    Article  CAS  PubMed  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. https://doi.org/10.1093/nar/gks1067

    Article  CAS  PubMed  Google Scholar 

  29. Finn RD, Bateman A, Clements J et al (2014) Pfam: the protein families database. Nucleic Acids Res 42:D222–D230. https://doi.org/10.1093/nar/gkt1223

    Article  CAS  PubMed  Google Scholar 

  30. Haft DH, Selengut JD, Richter RA et al (2013) TIGRFAMs and genome properties in 2013. Nucleic Acids Res 41:D387–D395. https://doi.org/10.1093/nar/gks1234

    Article  CAS  PubMed  Google Scholar 

  31. Blum M, Chang H-Y, Chuguransky S et al (2021) The InterPro protein families and domains database: 20 years on. Nucleic Acids Res 49:D344–D354. https://doi.org/10.1093/nar/gkaa977

    Article  CAS  PubMed  Google Scholar 

  32. de Castro E, Sigrist CJA, Gattiker A et al (2006) ScanProsite: detection of PROSITE signature matches and ProRule-associated functional and structural residues in proteins. Nucleic Acids Res 34:W362–W365. https://doi.org/10.1093/nar/gkl124

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Rish I (2001) An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop empirical methods for artificial intelligence. IBM, New York, pp 41–46

    Google Scholar 

  34. 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. https://doi.org/10.1093/nar/gkh485

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Briesemeister S, Rahnenführer J, Kohlbacher O (2010) Going from where to why—interpretable prediction of protein subcellular localization. Bioinformatics 26:1232–1238. https://doi.org/10.1093/bioinformatics/btq115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  37. Noble WS (2006) What is a support vector machine? Nat Biotechnol 24:1565–1567. https://doi.org/10.1038/nbt1206-1565

    Article  CAS  PubMed  Google Scholar 

  38. Min S, Lee B, Yoon S (2017) Deep learning in bioinformatics. Brief Bioinform 18:851–869. https://doi.org/10.1093/bib/bbw068

    Article  PubMed  Google Scholar 

  39. Shi Q, Chen W, Huang S et al (2021) Deep learning for mining protein data. Brief Bioinform 22:194–218. https://doi.org/10.1093/bib/bbz156

    Article  CAS  PubMed  Google Scholar 

  40. Alley EC, Khimulya G, Biswas S et al (2019) Unified rational protein engineering with sequence-based deep representation learning. Nat Methods 16:1315–1322. https://doi.org/10.1038/s41592-019-0598-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Rives A, Meier J, Sercu T et al (2021) Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc Natl Acad Sci 118:e2016239118. https://doi.org/10.1073/pnas.2016239118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Elnaggar A, Heinzinger M, Dallago C et al (2021) ProtTrans: Toward understanding the language of life through self-supervised learning. IEEE Trans Pattern Anal Mach Intell 44:7112–7127. https://doi.org/10.1109/TPAMI.2021.3095381

  43. Hobohm U, Scharf M, Schneider R, Sander C (1992) Selection of representative protein data sets. Protein Sci 1:409–417. https://doi.org/10.1002/pro.5560010313

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 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. https://doi.org/10.1093/bioinformatics/btl002

    Article  CAS  PubMed  Google Scholar 

  45. Sander C, Schneider R (1991) Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins Struct Funct Bioinforma 9:56–68. https://doi.org/10.1002/prot.340090107

    Article  CAS  Google Scholar 

  46. 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 Struct Funct Bioinforma 24:165–177. https://doi.org/10.1002/(SICI)1097-0134(199602)24:2<165::AID-PROT4>3.0.CO;2-I

  47. Nielsen H, Wernersson R (2006) An overabundance of phase 0 introns immediately after the start codon in eukaryotic genes. BMC Genomics 7:256. https://doi.org/10.1186/1471-2164-7-256

    Article  PubMed  PubMed Central  Google Scholar 

  48. 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. https://doi.org/10.1093/nar/gkg602

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Baldi P, Brunak S, Chauvin Y et al (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16:412–424. https://doi.org/10.1093/bioinformatics/16.5.412

    Article  CAS  PubMed  Google Scholar 

  50. Gorodkin J (2004) Comparing two K-category assignments by a K-category correlation coefficient. Comput Biol Chem 28:367–374. https://doi.org/10.1016/j.compbiolchem.2004.09.006

    Article  CAS  PubMed  Google Scholar 

  51. von Heijne G (1986) A new method for predicting signal sequence cleavage sites. Nucleic Acids Res 14:4683–4690. https://doi.org/10.1093/nar/14.11.4683

    Article  Google Scholar 

  52. McGeoch DJ (1985) On the predictive recognition of signal peptide sequences. Virus Res 3:271–286. https://doi.org/10.1016/0168-1702(85)90051-6

    Article  CAS  PubMed  Google Scholar 

  53. 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. https://doi.org/10.1016/0014-5793(89)80579-4

    Article  Google Scholar 

  54. 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. https://doi.org/10.1093/protein/10.1.1

    Article  CAS  PubMed  Google Scholar 

  55. 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

    CAS  PubMed  Google Scholar 

  56. Bendtsen JD, Nielsen H, von Heijne G, Brunak S (2004) Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 340:783–795. https://doi.org/10.1016/j.jmb.2004.05.028

    Article  CAS  PubMed  Google Scholar 

  57. Petersen TN, Brunak S, von Heijne G, Nielsen H (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785–786. https://doi.org/10.1038/nmeth.1701

    Article  CAS  PubMed  Google Scholar 

  58. Almagro Armenteros JJ, Tsirigos KD, Sønderby CK et al (2019) SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 37:420–423. https://doi.org/10.1038/s41587-019-0036-z

    Article  CAS  PubMed  Google Scholar 

  59. Teufel F, Almagro Armenteros JJ, Johansen AR et al (2022) SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol 40:1023–1025. https://doi.org/10.1038/s41587-021-01156-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 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. https://doi.org/10.1093/bioinformatics/16.8.741

    Article  CAS  PubMed  Google Scholar 

  61. Klee E, Ellis L (2005) Evaluating eukaryotic secreted protein prediction. BMC Bioinform 6:1–7. https://doi.org/10.1186/1471-2105-6-256

    Article  CAS  Google Scholar 

  62. Choo K, Tan T, Ranganathan S (2009) A comprehensive assessment of N-terminal signal peptides prediction methods. BMC Bioinform 10:S2. https://doi.org/10.1186/1471-2105-10-S15-S2

    Article  CAS  Google Scholar 

  63. Zhang X, Li Y, Li Y (2009) Evaluating signal peptide prediction methods for gram-positive bacteria. Biologia (Bratisl) 64:655–659. https://doi.org/10.2478/s11756-009-0118-3

    Article  Google Scholar 

  64. Savojardo C, Martelli PL, Fariselli P et al (2018) DeepSig: deep learning improves signal peptide detection in proteins. Bioinformatics 34:1690–1696. https://doi.org/10.1093/bioinformatics/btx818

    Article  CAS  PubMed  Google Scholar 

  65. Hiller K, Grote A, Scheer M et al (2004) PrediSi: prediction of signal peptides and their cleavage positions. Nucl Acids Res 32:W375–W379. https://doi.org/10.1093/nar/gkh378

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Frank K, Sippl MJ (2008) High-performance signal peptide prediction based on sequence alignment techniques. Bioinformatics 24:2172–2176. https://doi.org/10.1093/bioinformatics/btn422

    Article  CAS  PubMed  Google Scholar 

  67. 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. https://doi.org/10.3109/09687689409161023

    Article  CAS  PubMed  Google Scholar 

  68. The UniProt Consortium (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212. https://doi.org/10.1093/nar/gku989

    Article  CAS  Google Scholar 

  69. Juncker AS, Willenbrock H, von Heijne G et al (2003) Prediction of lipoprotein signal peptides in gram-negative bacteria. Protein Sci 12:1652–1662. https://doi.org/10.1110/ps.0303703

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 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. https://doi.org/10.1021/pr800162c

    Article  CAS  PubMed  Google Scholar 

  71. Fariselli P, Finocchiaro G, Casadio R (2003) SPEPlip: the detection of signal peptide and lipoprotein cleavage sites. Bioinformatics 19:2498–2499. https://doi.org/10.1093/bioinformatics/btg360

    Article  CAS  PubMed  Google Scholar 

  72. 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. https://doi.org/10.1093/emboj/18.11.2982

    Article  PubMed  PubMed Central  Google Scholar 

  73. 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. https://doi.org/10.1046/j.1365-2958.2002.03090.x

    Article  CAS  PubMed  Google Scholar 

  74. Bendtsen JD, Nielsen H, Widdick D et al (2005) Prediction of twin-arginine signal peptides. BMC Bioinform 6:167. https://doi.org/10.1186/1471-2105-6-167

    Article  CAS  Google Scholar 

  75. 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. https://doi.org/10.1093/bioinformatics/btq530

    Article  CAS  PubMed  Google Scholar 

  76. Binnewies TT, Bendtsen JD, Hallin PF et al (2005) Genome update: protein secretion systems in 225 bacterial genomes. Microbiology 151:1013–1016. https://doi.org/10.1099/mic.0.27966-0

    Article  CAS  PubMed  Google Scholar 

  77. 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. https://doi.org/10.1016/j.tim.2009.01.004

    Article  CAS  PubMed  Google Scholar 

  78. Bendtsen JD, Kiemer L, Fausbøll A, Brunak S (2005) Non-classical protein secretion in bacteria. BMC Microbiol 5:58. https://doi.org/10.1186/1471-2180-5-58

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Yu L, Guo Y, Li Y et al (2010) SecretP: identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition. J Theor Biol 267:1–6. https://doi.org/10.1016/j.jtbi.2010.08.001

    Article  CAS  PubMed  Google Scholar 

  80. 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. https://doi.org/10.1016/j.compbiomed.2013.06.001

    Article  CAS  PubMed  Google Scholar 

  81. Lloubes R, Bernadac A, Houot L, Pommier S (2013) Non classical secretion systems. Res Microbiol 164:655–663. https://doi.org/10.1016/j.resmic.2013.03.015

    Article  CAS  PubMed  Google Scholar 

  82. Dhroso A, Eidson S, Korkin D (2018) Genome-wide prediction of bacterial effector candidates across six secretion system types using a feature-based statistical framework. Sci Rep 8:17209. https://doi.org/10.1038/s41598-018-33,874-1

    Article  PubMed  PubMed Central  Google Scholar 

  83. Hui X, Chen Z, Zhang J et al (2021) Computational prediction of secreted proteins in gram-negative bacteria. Comput Struct Biotechnol J 19:1806–1828. https://doi.org/10.1016/j.csbj.2021.03.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. 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. https://doi.org/10.1039/C5AN00311C

    Article  CAS  PubMed  Google Scholar 

  85. Chen Z, Zhao Z, Hui X et al (2021) T1SEstacker: a tri-layer stacking model effectively predicts bacterial type 1 secreted proteins based on C-terminal non-RTX-motif sequence features. Front Microbiol 12:813094. https://doi.org/10.1101/2021.11.10.468166

    Article  PubMed  Google Scholar 

  86. Wang J, Yang B, Leier A et al (2018) Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors. Bioinformatics 34:2546–2555. https://doi.org/10.1093/bioinformatics/bty155

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 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. https://doi.org/10.1371/journal.ppat.1000508

  88. 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 107:21755–21760. https://doi.org/10.1073/pnas.1010485107

  89. Lifshitz Z, Burstein D, Peeri M et al (2013) Computational modeling and experimental validation of the legionella and Coxiella virulence-related type-IVB secretion signal. Proc Natl Acad Sci 110:E707–E715. https://doi.org/10.1073/pnas.1215278110

    Article  PubMed  PubMed Central  Google Scholar 

  90. 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. https://doi.org/10.1186/1471-2164-15-50

    Article  PubMed  PubMed Central  Google Scholar 

  91. Wang J, Yang B, An Y et al (2019) Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches. Brief Bioinform 20:931–951. https://doi.org/10.1093/bib/bbx164

    Article  CAS  PubMed  Google Scholar 

  92. Chen T, Wang X, Chu Y et al (2020) T4SE-XGB: interpretable sequence-based prediction of type IV secreted effectors using eXtreme gradient boosting algorithm. Front Microbiol 11:580382

    Article  PubMed  PubMed Central  Google Scholar 

  93. Yu L, Liu F, Li Y, et al. (2021) DeepT3_4: a hybrid deep neural network model for the distinction between bacterial type III and IV secreted effectors. Front Microbiol. 12: 605782

    Google Scholar 

  94. 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. https://doi.org/10.1128/IAI.00537-10

    Article  CAS  PubMed  Google Scholar 

  95. Anderson DM, Schneewind O (1997) A mRNA signal for the type III secretion of Yop proteins by Yersinia enterocolitica. Science 278:1140–1143. https://doi.org/10.1126/science.278.5340.1140

    Article  CAS  PubMed  Google Scholar 

  96. Deng W, Marshall NC, Rowland JL et al (2017) Assembly, structure, function and regulation of type III secretion systems. Nat Rev Microbiol 15:323–337. https://doi.org/10.1038/nrmicro.2017.20

    Article  CAS  PubMed  Google Scholar 

  97. 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. https://doi.org/10.1371/journal.ppat.1000375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Arnold R, Brandmaier S, Kleine F et al (2009) Sequence-based prediction of type III secreted proteins. PLoS Pathog 5:e1000376. https://doi.org/10.1371/journal.ppat.1000376

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Löwer M, Schneider G (2009) Prediction of type III secretion signals in genomes of gram-negative bacteria. PLoS One 4:e5917. https://doi.org/10.1371/journal.pone.0005917

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. 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. https://doi.org/10.1093/bioinformatics/btr021

    Article  CAS  PubMed  Google Scholar 

  101. 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. https://doi.org/10.1371/journal.pone.0058173

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. 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. https://doi.org/10.1371/journal.pone.0056632

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. 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. https://doi.org/10.1093/database/bav064

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Goldberg T, Rost B, Bromberg Y (2016) Computational prediction shines light on type III secretion origins. Sci Rep 6:34516. https://doi.org/10.1038/srep34516

  105. Wang J, Li J, Yang B et al (2019) Bastion3: a two-layer ensemble predictor of type III secreted effectors. Bioinformatics 35:2017–2028. https://doi.org/10.1093/bioinformatics/bty914

    Article  CAS  PubMed  Google Scholar 

  106. Xue L, Tang B, Chen W, Luo J (2019) DeepT3: deep convolutional neural networks accurately identify gram-negative bacterial type III secreted effectors using the N-terminal sequence. Bioinformatics 35:2051–2057. https://doi.org/10.1093/bioinformatics/bty931

    Article  CAS  PubMed  Google Scholar 

  107. Fu X, Yang Y (2019) WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning. Quant Biol 7:293–301. https://doi.org/10.1007/s40484-019-0184-7

    Article  CAS  Google Scholar 

  108. Sidorczuk K, Gagat P, Pietluch F et al (2022) Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data. Brief Bioinform 23:bbac343. https://doi.org/10.1093/bib/bbac343

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Klein P, Kanehisa M, DeLisi C (1985) The detection and classification of membrane-spanning proteins. Biochim Biophys Acta BBA Biomembr 815:468–476. https://doi.org/10.1016/0005-2736(85)90375-X

    Article  CAS  Google Scholar 

  110. von Heijne G (1992) Membrane protein structure prediction: hydrophobicity analysis and the positive-inside rule. J Mol Biol 225:487–494. https://doi.org/10.1016/0022-2836(92)90934-C

    Article  Google Scholar 

  111. von Heijne G, Gavel Y (1988) Topogenic signals in integral membrane proteins. Eur J Biochem 174:671–678. https://doi.org/10.1111/j.1432-1033.1988.tb14150.x

    Article  Google Scholar 

  112. Paul C, Rosenbusch JP (1985) Folding patterns of porin and bacteriorhodopsin. EMBO J 4:1593–1597. https://doi.org/10.1002/j.1460-2075.1985.tb03822.x

  113. 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. https://doi.org/10.1016/0022-2836(86)90292-5

    Article  CAS  PubMed  Google Scholar 

  114. Krogh A, Larsson B, von Heijne G, Sonnhammer ELL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580. https://doi.org/10.1006/jmbi.2000.4315

    Article  CAS  PubMed  Google Scholar 

  115. Tusnády GE, Simon I (2001) The HMMTOP transmembrane topology prediction server. Bioinformatics 17:849–850. https://doi.org/10.1093/bioinformatics/17.9.849

    Article  PubMed  Google Scholar 

  116. Möller S, Croning MDR, Apweiler R (2001) Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics 17:646–653. https://doi.org/10.1093/bioinformatics/17.7.646

    Article  PubMed  Google Scholar 

  117. Elofsson A, von Heijne G (2007) Membrane protein structure: prediction versus reality. Annu Rev Biochem 76:125–140. https://doi.org/10.1146/annurev.biochem.76.052705.163539

    Article  CAS  PubMed  Google Scholar 

  118. Punta M, Forrest LR, Bigelow H et al (2007) Membrane protein prediction methods. Methods 41:460–474. https://doi.org/10.1016/j.ymeth.2006.07.026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Tusnády GE, Simon I (2010) Topology prediction of helical transmembrane proteins: how far have we reached? Curr Protein Pept Sci 11:550–561. https://doi.org/10.2174/138920310794109184

    Article  PubMed  Google Scholar 

  120. Käll L, Krogh A, Sonnhammer ELL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338:1027–1036. https://doi.org/10.1016/j.jmb.2004.03.016

    Article  CAS  PubMed  Google Scholar 

  121. Reynolds SM, Käll L, Riffle ME et al (2008) Transmembrane topology and signal peptide prediction using dynamic bayesian networks. PLoS Comput Biol 4:e1000213. https://doi.org/10.1371/journal.pcbi.1000213

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Jones DT (2007) Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics 23:538–544. https://doi.org/10.1093/bioinformatics/btl677

    Article  CAS  PubMed  Google Scholar 

  123. Nugent T, Jones DT (2009) Transmembrane protein topology prediction using support vector machines. BMC Bioinform 10:159. https://doi.org/10.1186/1471-2105-10-159

    Article  CAS  Google Scholar 

  124. Viklund H, Bernsel A, Skwark M, Elofsson A (2008) SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology. Bioinformatics 24:2928–2929. https://doi.org/10.1093/bioinformatics/btn550

    Article  CAS  PubMed  Google Scholar 

  125. 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. https://doi.org/10.1093/bioinformatics/btn221

    Article  CAS  PubMed  Google Scholar 

  126. 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. https://doi.org/10.1110/ps.04625404

  127. Käll L, Krogh A, Sonnhammer ELL (2005) An HMM posterior decoder for sequence feature prediction that includes homology information. Bioinformatics 21:i251–i257. https://doi.org/10.1093/bioinformatics/bti1014

    Article  PubMed  Google Scholar 

  128. Bernsel A, Viklund H, Falk J et al (2008) Prediction of membrane-protein topology from first principles. Proc Natl Acad Sci 105:7177–7181. https://doi.org/10.1073/pnas.0711151105

    Article  PubMed  PubMed Central  Google Scholar 

  129. Hessa T, Meindl-Beinker NM, Bernsel A et al (2007) Molecular code for transmembrane-helix recognition by the Sec61 translocon. Nature 450:1026–1030. https://doi.org/10.1038/nature06387

    Article  CAS  PubMed  Google Scholar 

  130. Taylor PD, Attwood TK, Flower DR (2003) BPROMPT: a consensus server for membrane protein prediction. Nucleic Acids Res 31:3698–3700. https://doi.org/10.1093/nar/gkg554

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Bernsel A, Viklund H, Hennerdal A, Elofsson A (2009) TOPCONS: consensus prediction of membrane protein topology. Nucleic Acids Res 37:W465–W468. https://doi.org/10.1093/nar/gkp363

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. 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. https://doi.org/10.1093/nar/gkv485

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Hennerdal A, Elofsson A (2011) Rapid membrane protein topology prediction. Bioinformatics 27:1322–1323. https://doi.org/10.1093/bioinformatics/btr119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Dobson L, Reményi I, Tusnády GE (2015) CCTOP: a consensus constrained TOPology prediction web server. Nucleic Acids Res 43:W408–W412. https://doi.org/10.1093/nar/gkv451

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Bernhofer M, Rost B (2022) TMbed: transmembrane proteins predicted through language model embeddings. BMC Bioinform 23:326. https://doi.org/10.1186/s12859-022-04873-x

    Article  CAS  Google Scholar 

  136. Hallgren J, Tsirigos KD, Pedersen MD, et al. (2022) DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. bioRxiv 2022.04.08.487609. https://doi.org/10.1101/2022.04.08.487609

  137. 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. https://doi.org/10.1002/pro.5560071119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. 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. https://doi.org/10.1093/bioinformatics/18.suppl_1.S46

    Article  PubMed  Google Scholar 

  139. Bagos P, Liakopoulos T, Spyropoulos I, Hamodrakas S (2004) A hidden Markov model method, capable of predicting and discriminating beta-barrel outer membrane proteins. BMC Bioinform 5:29. https://doi.org/10.1186/1471-2105-5-29

    Article  Google Scholar 

  140. Bagos PG, Liakopoulos TD, Spyropoulos IC, Hamodrakas SJ (2004) PRED-TMBB: a web server for predicting the topology of β-barrel outer membrane proteins. Nucleic Acids Res 32:W400–W404. https://doi.org/10.1093/nar/gkh417

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Bigelow HR, Petrey DS, Liu J et al (2004) Predicting transmembrane beta-barrels in proteomes. Nucleic Acids Res 32:2566–2577. https://doi.org/10.1093/nar/gkh580

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Bigelow H, Rost B (2006) PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins. Nucleic Acids Res 34:W186–W188. https://doi.org/10.1093/nar/gkl262

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. 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 Bioinform 6:7. https://doi.org/10.1186/1471-2105-6-7

    Article  CAS  Google Scholar 

  144. 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. https://doi.org/10.1110/ps.37201

  145. Natt NK, Kaur H, Raghava GPS (2004) Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods. Proteins Struct Funct Bioinforma 56:11–18. https://doi.org/10.1002/prot.20092

    Article  CAS  Google Scholar 

  146. Hayat S, Elofsson A (2012) BOCTOPUS: improved topology prediction of transmembrane β barrel proteins. Bioinformatics 28:516–522. https://doi.org/10.1093/bioinformatics/btr710

    Article  CAS  PubMed  Google Scholar 

  147. 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. https://doi.org/10.1093/bioinformatics/btw025

    Article  CAS  PubMed  Google Scholar 

  148. 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. https://doi.org/10.1093/nar/gkh351

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Remmert M, Linke D, Lupas AN, Söding J (2009) HHomp—prediction and classification of outer membrane proteins. Nucleic Acids Res 37:gkp325. https://doi.org/10.1093/nar/gkp325

    Article  CAS  Google Scholar 

  150. 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. https://doi.org/10.1093/bioinformatics/btr549

    Article  CAS  PubMed  Google Scholar 

  151. 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. https://doi.org/10.1093/bioinformatics/bts728

    Article  CAS  PubMed  Google Scholar 

  152. Ton-That H, Marraffini LA, Schneewind O (2004) Protein sorting to the cell wall envelope of gram-positive bacteria. Biochim Biophys Acta BBA – Mol Cell Res 1694:269–278. https://doi.org/10.1016/j.bbamcr.2004.04.014

    Article  CAS  Google Scholar 

  153. Litou ZI, Bagos PG, Tsirigos KD et al (2008) Prediction of cell wall sorting signals in gram-positive bacteria with a hidden Markov model: application to complete genomes. J Bioinforma Comput Biol 6:387–401. https://doi.org/10.1142/S0219720008003382

    Article  CAS  Google Scholar 

  154. Fimereli DK, Tsirigos KD, Litou ZI et al (2012) CW-PRED: a HMM-based method for the classification of Cell Wall-anchored proteins of gram-positive bacteria. In: Maglogiannis I, Plagianakos V, Vlahavas I (eds) Artificial intelligence: theory and applications. Springer, Berlin/Heidelberg, pp 285–290. https://doi.org/10.1007/978-3-642-30448-4_36

  155. Janeček Š, Svensson B, Russell RRB (2000) Location of repeat elements in glucansucrases of Leuconostoc and streptococcus species. FEMS Microbiol Lett 192:53–57. https://doi.org/10.1111/j.1574-6968.2000.tb09358.x

    Article  PubMed  Google Scholar 

  156. López R, García E (2004) Recent trends on the molecular biology of pneumococcal capsules, lytic enzymes, and bacteriophage. FEMS Microbiol Rev 28:553–580. https://doi.org/10.1016/j.femsre.2004.05.002

    Article  CAS  PubMed  Google Scholar 

  157. Shah DSH, Joucla G, Remaud-Simeon M et al (2004) Conserved repeat motifs and glucan binding by glucansucrases of oral streptococci and Leuconostoc mesenteroides. J Bacteriol 186:8301–8308. https://doi.org/10.1128/JB.186.24.8301-8308.2004

  158. Krogh S, Jørgensen ST, Devine KM (1998) Lysis genes of the Bacillus subtilis defective prophage PBSX. J Bacteriol 180:2110–2117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Brinster S, Furlan S, Serror P (2007) C-terminal WxL domain mediates cell wall binding in Enterococcus faecalis and other gram-positive bacteria. J Bacteriol 189:1244–1253. https://doi.org/10.1128/JB.00773-06

  160. Nakai K, Kanehisa M (1991) Expert system for predicting protein localization sites in gram-negative bacteria. Proteins Struct Funct Bioinforma 11:95–110. https://doi.org/10.1002/prot.340110203

    Article  CAS  Google Scholar 

  161. Yu NY, Wagner JR, Laird MR et al (2010) PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26:1608–1615. https://doi.org/10.1093/bioinformatics/btq249

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Magnus M, Pawlowski M, Bujnicki JM (2012) MetaLocGramN: a meta-predictor of protein subcellular localization for gram-negative bacteria. Biochim Biophys Acta BBA Proteins Proteomics 1824:1425–1433. https://doi.org/10.1016/j.bbapap.2012.05.018

    Article  CAS  PubMed  Google Scholar 

  163. Peabody MA, Lau WYV, Hoad GR et al (2020) PSORTm: a bacterial and archaeal protein subcellular localization prediction tool for metagenomics data. Bioinformatics 36:3043–3048. https://doi.org/10.1093/bioinformatics/btaa136

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29. https://doi.org/10.1038/75556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Bhasin M, Garg A, Raghava GPS (2005) PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 21:2522–2524. https://doi.org/10.1093/bioinformatics/bti309

    Article  CAS  PubMed  Google Scholar 

  166. Goldberg T, Hecht M, Hamp T et al (2014) LocTree3 prediction of localization. Nucleic Acids Res 42:W350–W355. https://doi.org/10.1093/nar/gku396

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Goldberg T, Hamp T, Rost B (2012) LocTree2 predicts localization for all domains of life. Bioinformatics 28:i458–i465. https://doi.org/10.1093/bioinformatics/bts390

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. 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. https://doi.org/10.6026/97320630002417

  169. Grasso S, van Rij T, van Dijl JM (2020) GP4: an integrated gram-positive protein prediction pipeline for subcellular localization mimicking bacterial sorting. Brief Bioinform 22:bbaa302. https://doi.org/10.1093/bib/bbaa302

  170. Savojardo C, Martelli PL, Fariselli P, et al. (2018) BUSCA: an integrative web server to predict subcellular localization of proteins. Nucleic Acids Res 46:W459–W466. https://doi.org/10.1093/nar/gky320

  171. Almagro Armenteros JJ, Sønderby CK, Nielsen H, Winther O (2017) DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics 33:3387–3395. https://doi.org/10.1093/bioinformatics/btx431

    Article  CAS  PubMed  Google Scholar 

  172. Thumuluri V, Almagro Armenteros JJ, Johansen AR et al (2022) DeepLoc 2.0: multi-label subcellular localization prediction using protein language models. Nucleic Acids Res 50:W228–W234. https://doi.org/10.1093/nar/gkac278

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Crooks GE, Hon G, Chandonia J-M, Brenner SE (2004) WebLogo: a sequence logo generator. Genome Res 14:1188–1190. https://doi.org/10.1101/gr.849004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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  1. Department of Health Technology, Technical University of Denmark, Lyngby, Denmark

    Henrik Nielsen

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  1. Henrik Nielsen
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  1. Laboratoire d’Ingénierie des Systèmes Macromoléculaires, Institut de Microbiologie de la Méditerranée, Aix-Marseille Univ, CNRS, Marseille, France

    Laure Journet

  2. Laboratoire d’Ingénierie des Systèmes Macromoléculaires, Institut de Microbiologie de la Méditerranée, Aix-Marseille Univ, CNRS, Marseille, France

    Eric Cascales

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Nielsen, H. (2024). Protein Sorting Prediction. In: Journet, L., Cascales, E. (eds) Bacterial Secretion Systems . Methods in Molecular Biology, vol 2715. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3445-5_2

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