Predicting Beta Barrel Transmembrane Proteins Using HMMs

  • Georgios N. Tsaousis
  • Stavros J. Hamodrakas
  • Pantelis G. Bagos
Part of the Methods in Molecular Biology book series (MIMB, volume 1552)


Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for beta barrel transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane beta strands of TMBBs and discriminating them from globular proteins.

Key words

Hidden Markov model Algorithms Prediction Membrane Transmembrane Beta barrel Protein 


  1. 1.
    Schulz GE (2003) Transmembrane beta-barrel proteins. Adv Protein Chem 63:47–70PubMedCrossRefGoogle Scholar
  2. 2.
    Wimley WC (2003) The versatile beta-barrel membrane protein. Curr Opin Struct Biol 13(4):404–411PubMedCrossRefGoogle Scholar
  3. 3.
    Bagos PG, Hamodrakas SJ (2009) Bacterial beta-barrel outer membrane proteins: a common structural theme implicated in a wide variety of functional roles. In: Daskalaki A (ed) Handbook of research on systems biology applications in medicine, pp: 182–207. doi: 10.4018/978–1-60566-076-9.ch010
  4. 4.
    Tsirigos KD, Bagos PG, Hamodrakas SJ (2011) OMPdb: a database of {beta}-barrel outer membrane proteins from Gram-negative bacteria. Nucleic Acids Res 39(Database issue):D324–D331. doi: 10.1093/nar/gkq863 PubMedCrossRefGoogle Scholar
  5. 5.
    Vogel H, Jahnig F (1986) Models for the structure of outer-membrane proteins of Escherichia coli derived from Raman spectroscopy and prediction methods. J Mol Biol 190(2):191–199, doi:0022-2836(86)90292-5 [pii]PubMedCrossRefGoogle Scholar
  6. 6.
    Jeanteur D, Lakey JH, Pattus F (1991) The bacterial porin superfamily: sequence alignment and structure prediction. Mol Microbiol 5(9):2153–2164PubMedCrossRefGoogle Scholar
  7. 7.
    Rauch G, Moran O (1995) Prediction of polypeptide secondary structures analysing the oscillation of the hydropathy profile. Comput Methods Programs Biomed 48(3):193–200, doi:0169260795016988 [pii]PubMedCrossRefGoogle Scholar
  8. 8.
    Schirmer T, Cowan SW (1993) Prediction of membrane-spanning beta-strands and its application to maltoporin. Protein Sci 2(8):1361–1363. doi: 10.1002/pro.5560020820 PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Neuwald AF, Liu JS, Lawrence CE (1995) Gibbs motif sampling: detection of bacterial outer membrane protein repeats. Protein Sci 4(8):1618–1632. doi: 10.1002/pro.5560040820 PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Gromiha MM, Majumdar R, Ponnuswamy PK (1997) Identification of membrane spanning beta strands in bacterial porins. Protein Eng 10(5):497–500PubMedCrossRefGoogle Scholar
  11. 11.
    Diederichs K, Freigang J, Umhau S et al (1998) Prediction by a neural network of outer membrane beta-strand protein topology. Protein Sci 7(11):2413–2420. doi: 10.1002/pro.5560071119 PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Gromiha MM, Ahmad S, Suwa M (2004) Neural network-based prediction of transmembrane beta-strand segments in outer membrane proteins. J Comput Chem 25(5):762–767. doi: 10.1002/jcc.10386 PubMedCrossRefGoogle Scholar
  13. 13.
    Jacoboni I, Martelli PL, Fariselli P et al (2001) Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor. Protein Sci 10(4):779–787. doi: 10.1110/ps.37201 PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Bagos PG, Liakopoulos TD, Spyropoulos IC et al (2004) A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins. BMC Bioinformatics 5:29. doi: 10.1186/1471-2105-5-29 PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Bigelow HR, Petrey DS, Liu J et al (2004) Predicting transmembrane beta-barrels in proteomes. Nucleic Acids Res 32(8):2566–2577. doi: 10.1093/nar/gkh580 PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Martelli PL, Fariselli P, Krogh A et al (2002) A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins. Bioinformatics 18(Suppl 1):S46–S53PubMedCrossRefGoogle Scholar
  17. 17.
    Park KJ, Gromiha MM, Horton P et al (2005) Discrimination of outer membrane proteins using support vector machines. Bioinformatics 21(23):4223–4229. doi: 10.1093/bioinformatics/bti697 PubMedCrossRefGoogle Scholar
  18. 18.
    Garrow AG, Agnew A, Westhead DR (2005) TMB-Hunt: an amino acid composition based method to screen proteomes for beta-barrel transmembrane proteins. BMC Bioinformatics 6:56. doi: 10.1186/1471-2105-6-56 PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Yan C, Hu J, Wang Y (2008) Discrimination of outer membrane proteins using a K-nearest neighbor method. Amino Acids 35(1):65–73. doi: 10.1007/s00726-007-0628-7 PubMedCrossRefGoogle Scholar
  20. 20.
    Lin H (2008) The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou’s pseudo amino acid composition. J Theor Biol 252(2):350–356. doi: 10.1016/j.jtbi.2008.02.004 PubMedCrossRefGoogle Scholar
  21. 21.
    Ou YY, Gromiha MM, Chen SA et al (2008) TMBETADISC-RBF: discrimination of beta-barrel membrane proteins using RBF networks and PSSM profiles. Comput Biol Chem 32(3):227–231. doi: 10.1016/j.compbiolchem.2008.03.002 PubMedCrossRefGoogle Scholar
  22. 22.
    Fariselli P, Savojardo C, Martelli PL et al (2009) Grammatical-restrained hidden conditional random fields for bioinformatics applications. Algorithms Mol Biol 4:13. doi: 10.1186/1748-7188-4-13 PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Hayat S, Elofsson A (2012) BOCTOPUS: improved topology prediction of transmembrane beta barrel proteins. Bioinformatics 28(4):516–522. doi: 10.1093/bioinformatics/btr710 PubMedCrossRefGoogle Scholar
  24. 24.
    Natt NK, Kaur H, Raghava GP (2004) Prediction of transmembrane regions of beta-barrel proteins using ANN- and SVM-based methods. Proteins 56(1):11–18. doi: 10.1002/prot.20092 PubMedCrossRefGoogle Scholar
  25. 25.
    Bagos PG, Liakopoulos TD, Hamodrakas SJ (2005) Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method. BMC Bioinformatics 6:7. doi: 10.1186/1471-2105-6-7 PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    von Heijne G (1992) Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. J Mol Biol 225(2):487–494CrossRefGoogle Scholar
  27. 27.
    Bannwarth M, Schulz GE (2003) The expression of outer membrane proteins for crystallization. Biochim Biophys Acta 1610(1):37–45, doi:S0005273602007113 [pii]PubMedCrossRefGoogle Scholar
  28. 28.
    Pautsch A, Schulz GE (1998) Structure of the outer membrane protein A transmembrane domain. Nat Struct Biol 5(11):1013–1017. doi: 10.1038/2983 PubMedCrossRefGoogle Scholar
  29. 29.
    Kozma D, Simon I, Tusnady GE (2013) PDBTM: Protein Data Bank of transmembrane proteins after 8 years. Nucleic Acids Res 41(Database issue):D524–D529. doi: 10.1093/nar/gks1169 PubMedCrossRefGoogle Scholar
  30. 30.
    Delano WL (2002) The PyMOL molecular graphics system.
  31. 31.
    Schulz GE (2002) The structure of bacterial outer membrane proteins. Biochim Biophys Acta 1565(2):308–317, doi:S0005273602005771 [pii]PubMedCrossRefGoogle Scholar
  32. 32.
    Wimley WC (2002) Toward genomic identification of beta-barrel membrane proteins: composition and architecture of known structures. Protein Sci 11(2):301–312. doi: 10.1110/ps.29402 PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Gromiha MM, Ponnuswamy PK (1993) Prediction of transmembrane beta-strands from hydrophobic characteristics of proteins. Int J Pept Protein Res 42(5):420–431PubMedCrossRefGoogle Scholar
  34. 34.
    Zhai Y, Saier MH Jr (2002) The beta-barrel finder (BBF) program, allowing identification of outer membrane beta-barrel proteins encoded within prokaryotic genomes. Protein Sci 11(9):2196–2207. doi: 10.1110/ps.0209002 PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Bishop CM, Walkenhorst WF, Wimley WC (2001) Folding of beta-sheets in membranes: specificity and promiscuity in peptide model systems. J Mol Biol 309(4):975–988. doi: 10.1006/jmbi.2001.4715 PubMedCrossRefGoogle Scholar
  36. 36.
    Gnanasekaran TV, Peri S, Arockiasamy A et al (2000) Profiles from structure based sequence alignment of porins can identify beta stranded integral membrane proteins. Bioinformatics 16(9):839–842PubMedCrossRefGoogle Scholar
  37. 37.
    Freeman TC Jr, Wimley WC (2010) A highly accurate statistical approach for the prediction of transmembrane beta-barrels. Bioinformatics 26(16):1965–1974. doi: 10.1093/bioinformatics/btq308 PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Liu Q, Zhu Y, Wang B et al (2003) Identification of beta-barrel membrane proteins based on amino acid composition properties and predicted secondary structure. Comput Biol Chem 27(3):355–361, doi:S1476927102000853 [pii]PubMedCrossRefGoogle Scholar
  39. 39.
    Berven FS, Flikka K, Jensen HB et al (2004) BOMP: a program to predict integral beta-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria. Nucleic Acids Res 32(Web Server issue):W394–W399. doi: 10.1093/nar/gkh351 PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    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(17):3389–3402, doi:gka562 [pii]PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Bagos PG, Liakopoulos TD, Spyropoulos IC et al (2004) PRED-TMBB: a web server for predicting the topology of beta-barrel outer membrane proteins. Nucleic Acids Res 32(Web Server issue):W400–W404. doi: 10.1093/nar/gkh417 PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Randall A, Cheng J, Sweredoski M et al (2008) TMBpro: secondary structure, beta-contact and tertiary structure prediction of transmembrane beta-barrel proteins. Bioinformatics 24(4):513–520. doi: 10.1093/bioinformatics/btm548 PubMedCrossRefGoogle Scholar
  43. 43.
    Waldispuhl J, Berger B, Clote P et al (2006) transFold: a web server for predicting the structure and residue contacts of transmembrane beta-barrels. Nucleic Acids Res 34(Web Server issue):W189–193. doi: 10.1093/nar/gkl205 PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Remmert M, Linke D, Lupas AN et al (2009) HHomp—prediction and classification of outer membrane proteins. Nucleic Acids Res 37(Web Server issue):W446–W451. doi: 10.1093/nar/gkp325 PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286CrossRefGoogle Scholar
  46. 46.
    Eddy SR (1998) Profile hidden Markov models. Bioinformatics 14(9):755–763, doi:btb114 [pii]PubMedCrossRefGoogle Scholar
  47. 47.
    Krogh A, Larsson B, von Heijne G et al (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305(3):567–580. doi: 10.1006/jmbi.2000.4315 PubMedCrossRefGoogle Scholar
  48. 48.
    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
  49. 49.
    Krogh A, Mian IS, Haussler D (1994) A hidden Markov model that finds genes in E. coli DNA. Nucleic Acids Res 22(22):4768–4778PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    Krogh A (1994) Hidden Markov models for labelled sequences. In: Proceedings of the12th IAPR international conference on pattern recognition, pp 140–144Google Scholar
  51. 51.
    Chamberlain AK, Bowie JU (2004) Asymmetric amino acid compositions of transmembrane beta-strands. Protein Sci 13(8):2270–2274PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Slusky JS, Dunbrack RL Jr (2013) Charge asymmetry in the proteins of the outer membrane. Bioinformatics 29(17):2122–2128. doi: 10.1093/bioinformatics/btt355 PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Jackups R Jr, Liang J (2005) Interstrand pairing patterns in beta-barrel membrane proteins: the positive-outside rule, aromatic rescue, and strand registration prediction. J Mol Biol 354(4):979–993. doi: 10.1016/j.jmb.2005.09.094 PubMedCrossRefGoogle Scholar
  54. 54.
    Berman HM, Westbrook J, Feng Z et al (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242, doi:gkd090 [pii]PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Andreeva A, Howorth D, Brenner SE et al (2004) SCOP database in 2004: refinements integrate structure and sequence family data. Nucleic Acids Res 32(Database issue):D226–D229. doi: 10.1093/nar/gkh039 PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Lomize MA, Lomize AL, Pogozheva ID et al (2006) OPM: orientations of proteins in membranes database. Bioinformatics 22(5):623–625. doi: 10.1093/bioinformatics/btk023 PubMedCrossRefGoogle Scholar
  57. 57.
    Dobson L, Lango T, Remenyi I et al (2015) Expediting topology data gathering for the TOPDB database. Nucleic Acids Res 43(Database issue):D283–D289. doi: 10.1093/nar/gku1119 PubMedCrossRefGoogle Scholar
  58. 58.
    Bagos PG, Tsaousis GN, Hamodrakas SJ (2009) How many 3D structures do we need to train a predictor? Genomics Proteomics Bioinformatics 7(3):128–137. doi: 10.1016/S1672-0229(08)60041-8 PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Bagos PG, Hamodrakas SJ (2009) Bacterial beta-barrel outer membrane proteins: a common structural theme implicated in a wide variety of functional roles. In: Daskalaki A (ed) Handbook of research on systems biology applications in medicine, pp 182–207. doi:  10.4018/978–1-60566-076-9.ch010
  60. 60.
    Punta M, Coggill PC, Eberhardt RY et al (2012) The Pfam protein families database. Nucleic Acids Res 40(Database issue):D290–D301. doi: 10.1093/nar/gkr1065 PubMedCrossRefGoogle Scholar
  61. 61.
    Fariselli P, Finelli M, Marchignoli D et al (2003) MaxSubSeq: an algorithm for segment-length optimization. The case study of the transmembrane spanning segments. Bioinformatics 19(4):500–505PubMedCrossRefGoogle Scholar
  62. 62.
    Zemla A, Venclovas C, Fidelis K et al (1999) A modified definition of Sov, a segment-based measure for protein secondary structure prediction assessment. Proteins 34(2):220–223. doi: 10.1002/(SICI)1097-0134(19990201)34:2<220::AID-PROT7>3.0.CO;2-K [pii]PubMedCrossRefGoogle Scholar
  63. 63.
    Baldi P, Brunak S, Chauvin Y et al (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16(5):412–424PubMedCrossRefGoogle Scholar
  64. 64.
    Baum LE (1972) An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. Inequalities 3:1–8Google Scholar
  65. 65.
    Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B Methodol 39(1):1–38. doi: 10.2307/2984875 Google Scholar
  66. 66.
    Krogh A (1997) Two methods for improving performance of an HMM and their application for gene finding. Proc Int Conf Intell Syst Mol Biol 5:179–186PubMedGoogle Scholar
  67. 67.
    Bagos P, Liakopoulos T, Hamodrakas S (2004) Faster gradient descent training of hidden Markov models, using individual learning rate adaptation. In: Paliouras G, Sakakibara Y (eds) Grammatical inference: algorithms and applications, vol 3264, Lecture notes in computer science. Springer, Berlin, Heidelberg, pp 40–52. doi: 10.1007/978-3-540-30195-0_5 CrossRefGoogle Scholar
  68. 68.
    Krogh A, Riis SK (1999) Hidden neural networks. Neural Comput 11(2):541–563PubMedCrossRefGoogle Scholar
  69. 69.
    Zou L, Wang Z, Wang Y et al (2010) Combined prediction of transmembrane topology and signal peptide of beta-barrel proteins: using a hidden Markov model and genetic algorithms. Comput Biol Med 40(7):621–628. doi: 10.1016/j.compbiomed.2010.04.006 PubMedCrossRefGoogle Scholar
  70. 70.
    Schwartz R, Chow YL (1990) The N-best algorithms: an efficient and exact procedure for finding the N most likely sentence hypotheses. In: 1990 international conference on acoustics, speech, and signal processing, 1990. ICASSP-90, 3–6 Apr 1990, vol 81, pp 81–84. doi: 10.1109/icassp.1990.115542
  71. 71.
    Kall L, Krogh A, Sonnhammer EL (2005) An HMM posterior decoder for sequence feature prediction that includes homology information. Bioinformatics 21(Suppl 1):i251–i257. doi: 10.1093/bioinformatics/bti1014 PubMedCrossRefGoogle Scholar
  72. 72.
    Fariselli P, Martelli PL, Casadio R (2005) A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins. BMC Bioinformatics 6(Suppl 4):S12PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    Won KJ, Hamelryck T, Prugel-Bennett A et al (2007) An evolutionary method for learning HMM structure: prediction of protein secondary structure. BMC Bioinformatics 8:357. doi: 10.1186/1471-2105-8-357 PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Won KJ, Prugel-Bennett A, Krogh A (2004) Training HMM structure with genetic algorithm for biological sequence analysis. Bioinformatics 20(18):3613–3619. doi: 10.1093/bioinformatics/bth454 PubMedCrossRefGoogle Scholar
  75. 75.
    Petersen TN, Brunak S, von Heijne G et al (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8(10):785–786. doi: 10.1038/nmeth.1701 PubMedCrossRefGoogle Scholar
  76. 76.
    Lin K, Simossis VA, Taylor WR et al (2005) A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics 21(2):152–159. doi: 10.1093/bioinformatics/bth487 PubMedCrossRefGoogle Scholar
  77. 77.
    Martelli PL, Fariselli P, Casadio R (2004) Prediction of disulfide-bonded cysteines in proteomes with a hidden neural network. Proteomics 4(6):1665–1671. doi: 10.1002/pmic.200300745 PubMedCrossRefGoogle Scholar
  78. 78.
    Bagos PG, Liakopoulos TD, Hamodrakas SJ (2006) Algorithms for incorporating prior topological information in HMMs: application to transmembrane proteins. BMC Bioinformatics 7:189. doi: 10.1186/1471-2105-7-189 PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    Viklund H, Elofsson A (2004) Best alpha-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information. Protein Sci 13(7):1908–1917. doi: 10.1110/ps.04625404 PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Georgios N. Tsaousis
    • 1
  • Stavros J. Hamodrakas
    • 1
  • Pantelis G. Bagos
    • 2
  1. 1.Department of Cell Biology and Biophysics, Faculty of BiologyNational and Kapodistrian University of AthensAthensGreece
  2. 2.Department of Computer Science and Biomedical InformaticsUniversity of ThessalyLamiaGreece

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