Advertisement

Bioinformatics on β-Barrel Membrane Proteins: Sequence and Structural Analysis, Discrimination and Prediction

  • M. Michael Gromiha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)

Abstract

The analysis on the amino acid sequences of transmembrane beta barrel proteins (TMBs) provides deep insights about their structure and function. We found that the occurrence of Ser, Asn and Gln is significantly higher in TMBs than globular proteins, which might be due to their importance in the formation of β-barrel structures in the membrane, stability of binding pockets and the function of TMBs. Utilizing this information, we have devised statistical methods and machine learning techniques to discriminate TMBs from other folding types of globular and membrane proteins and we obtained the maximum accuracy of 96%. Further, we have devised protocols for identifying the membrane spanning β-strand segments and detecting TMBs in genomic sequences.

Keywords

β-barrel membrane protein amino acid composition sequence analysis discrimination prediction genome 

References

  1. 1.
    Gardy, J.L., Spencer, C., Wang, K., Ester, M., Tusnady, G.E., Simon, I., Hua, S., de Fays, K., Lambert, C., Nakai, K., Brinkman, F.S.: PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria. Nucleic Acids Res. 31, 3613–3617 (2003)CrossRefGoogle Scholar
  2. 2.
    Li, W., Jaroszewski, L., Godzik, A.: Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 17, 282–283 (2001)CrossRefGoogle Scholar
  3. 3.
    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000)CrossRefGoogle Scholar
  4. 4.
    Branden, C., Tooze, C.: Introduction to protein structure. Garland Publishing Inc., New York (1999)Google Scholar
  5. 5.
    Gromiha, M.M., Suwa, M.A.: Simple statistical method for discriminating outer membrane proteins with better accuracy. Bioinformatics 21, 961–968 (2005)CrossRefGoogle Scholar
  6. 6.
    Pautsch, A., Schulz, G.E.: High-resolution structure of the OmpA membrane domain. J. Mol. Biol. 298, 273–282 (2000)CrossRefGoogle Scholar
  7. 7.
    Vandeputte-Rutten, L., Kramer, R.A., Kroon, J., Dekker, N., Egmond, M.R., Gros, P.: Crystal structure of the outer membrane protease OmpT from Escherichia coli suggests a novel catalytic site. EMBO J. 20, 5033–5039 (2001)CrossRefGoogle Scholar
  8. 8.
    Yue, W.W., Grizot, S., Buchanan, S.K.: Structural evidence for iron-free citrate and ferric citrate binding to the TonB-dependent outer membrane transporter FecA. J. Mol. Biol. 332, 353–368 (2003)CrossRefGoogle Scholar
  9. 9.
    Gromiha, M.M., Ahmad, S., Suwa, M.: Application of residue distribution along the sequence for discriminating outer membrane proteins. Comput. Biol. Chem. 29, 135–142 (2005)zbMATHCrossRefGoogle Scholar
  10. 10.
    Gromiha, M.M.: Motifs in outer membrane protein sequences: Applications for discrimination. Biophys. Chem. 117, 65–71 (2005)CrossRefGoogle Scholar
  11. 11.
    Gromiha, M.M., Suwa, M.: Discrimination of outer membrane proteins using machine learning algorithms. Proteins: Struct. Funct. Bioinf. 63, 1031–1037 (2006)CrossRefGoogle Scholar
  12. 12.
    Gromiha, M.M., Suwa, M.: Influence of amino acid properties for discriminating outer membrane proteins at better accuracy. Biochim. Biophys. Acta 1764, 1493–1497 (2006)Google Scholar
  13. 13.
    Ou, Y.-Y., Gromiha, M.M., Chen, S.-A., Suwa, M.: Discrimination of beta barrel membrane proteins using RBF networks and PSSM profiles. Proteins: Struct. Funct. Bioinf. (in press) Google Scholar
  14. 14.
    Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247, 536–540 (1995)Google Scholar
  15. 15.
    Gnanasekaran, T.V., Peri, S., Arockiasamy, A., Krishnaswamy, S.: Profiles from structure based sequence alignment of porins can identify beta stranded integral membrane proteins. Bioinformatics 16, 839–842 (2000)CrossRefGoogle Scholar
  16. 16.
    Liu, Q., Zhu, Y., Wang, B., Li, Y.: Identification of beta-barrel membrane proteins based on amino acid composition properties and predicted secondary structure. Comput. Biol. Chem. 27, 355–361 (2003)CrossRefGoogle Scholar
  17. 17.
    Martelli, P.L., Fariselli, P., Krogh, A., Casadio, R.: A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins. Bioinformatics 18, S46–S53 (2002)Google Scholar
  18. 18.
    Bagos, P.G., Liakopoulos, T.D., Spyropoulos, I.C., Hamodrakas, S.J.: A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins. BMC Bioinformatics 5, 29 (2004)CrossRefGoogle Scholar
  19. 19.
    Park, K.J., Gromiha, M.M., Horton, P., Suwa, M.: Discrimination of outer membrane proteins using support vector machines. Bioinformatics 21, 4223–4229 (2005)CrossRefGoogle Scholar
  20. 20.
    Garrow, A.G., Agnew, A., Westhead, D.R.: TMB-Hunt: a web server to screen sequence sets for transmembrane beta-barrel proteins. Nucleic Acids Res. 33, W188–W192 (2005)CrossRefGoogle Scholar
  21. 21.
    Gromiha, M.M., Majumdar, R., Ponnuswamy, P.K.: Identification of membrane spanning beta strands in bacterial porins. Protein Eng. 10, 497–500 (1997)CrossRefGoogle Scholar
  22. 22.
    Gromiha, M.M., Ahmad, S., Suwa, M.: TMBETA-NET: Discrimination and prediction of membrane spanning?-strands in outer membrane proteins. Nucleic Acids Res. 33, W164–W167 (2005)CrossRefGoogle Scholar
  23. 23.
    Busch, W., Saier Jr., M.H.: The transporter classification (TC) system, 2002. Crit. Rev. Biochem. Mol. Biol. 37, 287–337 (2002)CrossRefGoogle Scholar
  24. 24.
    Gromiha, M.M., Yabuki, Y., Kundu, S., Suharnan, S., Suwa, M.: TMBETA-GENOME: database for annotated beta-barrel membrane proteins in genomic sequences. Nucleic Acids Res. 35, 314–316 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • M. Michael Gromiha
    • 1
  1. 1.Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064Japan

Personalised recommendations