Database Development and Discrimination Algorithms for Membrane Protein Functions
We have developed a database for membrane protein functions, which has more than 3000 experimental data on functionally important amino acid residues in membrane proteins along with sequence, structure and literature information. Further, we have proposed different methods for identifying membrane proteins based on their functions: (i) discrimination of membrane transport proteins from other globular and membrane proteins and classifying them into channels/pores, electrochemical and active transporters, and (ii) β-signal for the insertion of mitochondrial β-barrel outer membrane proteins and potential targets. Our method showed an accuracy of 82% in discriminating transport proteins and 68% to classify them into three different transporters. In addition, we have identified a motif for targeting β-signal and potential candidates for mitochondrial β-barrel membrane proteins. Our methods can be used as effective tools for genome-wide annotations.
[1] T. Hirokawa, S. Boon-Chieng, S. Mitaku, SOSUI: classification and
secondary structure prediction system for membrane proteins,
Bioinformatics 14 (1998) 378-379.
[2] M.M. Gromiha, M. Suwa. A simple statistical method for discriminating
outer membrane proteins with better accuracy. Bioinformatics 21 (2005)
961-968.
[3] Y.D. Cai, K.C. Chou. Predicting membrane protein type by functional
domain composition and pseudo-amino acid composition. J Theor Biol.
(2006) 238: 395-400.
[4] P.L. Martelli, P. Fariselli, A. Krogh, R. Casadio, A sequence-profilebased
HMM for predicting and discriminating beta barrel membrane
proteins, Bioinformatics 18 (2002) S46-S53.
[5] P.G. Bagos, T.D. Liakopoulos, I.C. Spyropoulos, S.J. Hamodrakas, A
Hidden Markov Model method, capable of predicting and discriminating
beta-barrel outer membrane proteins, BMC Bioinformatics 5 (2004) 29.
[6] N.K. Natt, H. Kaur, G.P. Raghava. Prediction of transmembrane regions
of beta-barrel proteins using ANN- and SVM-based methods. Proteins
56 (2004) 11-18.
[7] M.M. Gromiha, M. Suwa. Discrimination of outer membrane proteins
using machine learning algorithms. Proteins 63 (2006) 1031-1037.
[8] M.M. Gromiha, M. Suwa. Influence of amino acid properties for
discriminating outer membrane proteins at better accuracy. Biochim
Biophys Acta. 2006 Sep;1764(9):1493-7.
[9] Gromiha MM, Ahmad S, Suwa M. Neural network-based prediction of
transmembrane beta-strand segments in outer membrane proteins. J
Comput Chem. 2004 Apr 15;25(5):762-7.
[10] Gromiha MM, Yabuki Y, Kundu S, Suharnan S, Suwa M. (2007)
TMBETA-GENOME: database for annotated beta-barrel membrane
proteins in genomic sequences. Nucleic Acids Res. 35: D314-6
[11] M.H. Saier, Jr, C.V. Tran, R.D. Barabote. (2006) TCDB: the transporter
classification database for membrane transport protein analyses and
information Nucleic Acids Res, 34, D181-D186
[12] Horn F, Bettler E, Oliveira L, Campagne F, Cohen FE, Vriend G (2003)
GPCRDB information system for G protein-coupled receptors. Nucleic
Acids Res. 31:294-297.
[13] Schwacke R, Schneider A, Van Der Graaff E, Fischer K, Catoni E,
Desimone M, Frommer WB, Flugge UI, Kunze R. (2003)
ARAMEMNON, a Novel Database for Arabidopsis Integral Membrane
Proteins. Plant Physiol. 131: 16-26.
[14] Edvardsen O, Reiersen AL, Beukers MW, Kristiansen K. (2002) tGRAP,
the G-protein coupled receptors mutant database. Nucleic Acids Res. 30:
361-3
[15] Li H, Dai X, Zhao X. (2008) A nearest neighbor approach for automated
transporter prediction and categorization from protein sequences.
Bioinformatics. 24:1129-36.
[16] Kutik S, Stojanovski D, Becker L, Becker T, Meinecke M, Kr├╝ger V,
Prinz C, Meisinger C, Guiard B, Wagner R, Pfanner N, Wiedemann N.
Dissecting membrane insertion of mitochondrial beta-barrel proteins.
Cell. 2008 Mar 21;132(6):1011-24.
[17] M.H. Saier, Jr. A functional-phylogenetic classification system for
transmembrane solute transporters Microbiol. Mol. Biol. Rev,. (2000)
64, 354-411
[18] Gromiha MM, Yabuki Y, Suresh MX, Thangakani AM, Suwa M, Fukui
K. TMFunction: database for functional residues in membrane proteins.
Nucleic Acids Res. 2008; doi:10.1093/nar/gkn672.
[19] Wu CH, Apweiler R, Bairoch A, Natale DA, Barker WC, Boeckmann B,
Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ,
Mazumder R, O'Donovan C, Redaschi N, Suzek B. (2006) The
Universal Protein Resource (UniProt): an expanding universe of protein
information. Nucleic Acids Res. 34: D187-91.
[20] Berman H, Henrick K, Nakamura H, Markley JL. The worldwide Protein
Data Bank (wwPDB): ensuring a single, uniform archive of PDB data.
(2007) Nucleic Acids Res. 35: D301-3
[21] Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W,
Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of
protein database search programs. Nucleic Acids Res. 1997; 25: 3389-
3402.
[22] Witten IH, Frank E: Data Mining: Practical machine learning tools and
techniques, 2nd Edition, Morgan Kaufmann, San Francisco, 2005.
[23] Gromiha MM, Yabuki Y. (2008) Functional discrimination of membrane
proteins using machine learning techniques. BMC Bioinformatics.
9:135.
[24] Gromiha MM, Suwa M. (2007) Current developments on beta-barrel
membrane proteins: sequence and structure analysis, discrimination and
prediction. Curr. Protein Pept Sci. 8: 580-99.
[25] Chimento DP, Mohanty AK, Kadner RJ, Wiener MC. Substrate-induced
transmembrane signaling in the cobalamin transporter BtuB. Nat Struct
Biol. 2003, 10: 394-401.
[26] Chimento DP, Kadner RJ, Wiener MC. The Escherichia coli outer
membrane cobalamin transporter BtuB: structural analysis of calcium
and substrate binding, and identification of orthologous transporters by
sequence/ structure conservation. J Mol Biol. 2003, 332: 999-1014.
[27] Dutzler R, Campbell EB, MacKinnon R. Gating the selectivity filter in
ClC chloride channels. Science. 2003; 300:108-12.
[28] Huang Y, Lemieux MJ, Song J, Auer M, Wang DN. Structure and
mechanism of the glycerol-3-phosphate transporter from Escherichia
coli. Science. 2003; 301:616-20.
[29] Abramson J, Smirnova I, Kasho V, Verner G, Kaback HR, Iwata S.
Structure and mechanism of the lactose permease of Escherichia coli.
Science. 2003; 301:610-5.
[30] Taguchi YH, Gromiha MM. Application of amino acid occurrence for
discriminating different folding types of globular proteins. BMC
Bioinformatics 2007, 8, 404.
[31] Imai K, Horton P, Gromiha MM. Mitochondrial β-Signal; The End of
the Story? Cell (in press).
[32] Velours G, Boucheron C, Manon S, Camougrand N. Dual cell
wall/mitochondria localization of the 'SUN' family proteins. FEMS
Microbiol Lett. 2002 Feb 5;207(2):165-72
[33] Wimley WC. The versatile beta-barrel membrane protein. Curr Opin
Struct Biol. 2003 Aug;13(4):404-11
[34] Hiller S, Garces RG, Malia TJ, Orekhov VY, Colombini M, Wagner G.
Solution structure of the integral human membrane protein VDAC-1 in
detergent micelles. Science. 2008 Aug 29;321(5893):1206-10
[35] Tusnády GE, Dosztányi Z, Simon I. PDB_TM: selection and membrane
localization of transmembrane proteins in the protein data bank. Nucleic
Acids Res. 2005 Jan 1;33(Database issue):D275-8.
[1] T. Hirokawa, S. Boon-Chieng, S. Mitaku, SOSUI: classification and
secondary structure prediction system for membrane proteins,
Bioinformatics 14 (1998) 378-379.
[2] M.M. Gromiha, M. Suwa. A simple statistical method for discriminating
outer membrane proteins with better accuracy. Bioinformatics 21 (2005)
961-968.
[3] Y.D. Cai, K.C. Chou. Predicting membrane protein type by functional
domain composition and pseudo-amino acid composition. J Theor Biol.
(2006) 238: 395-400.
[4] P.L. Martelli, P. Fariselli, A. Krogh, R. Casadio, A sequence-profilebased
HMM for predicting and discriminating beta barrel membrane
proteins, Bioinformatics 18 (2002) S46-S53.
[5] P.G. Bagos, T.D. Liakopoulos, I.C. Spyropoulos, S.J. Hamodrakas, A
Hidden Markov Model method, capable of predicting and discriminating
beta-barrel outer membrane proteins, BMC Bioinformatics 5 (2004) 29.
[6] N.K. Natt, H. Kaur, G.P. Raghava. Prediction of transmembrane regions
of beta-barrel proteins using ANN- and SVM-based methods. Proteins
56 (2004) 11-18.
[7] M.M. Gromiha, M. Suwa. Discrimination of outer membrane proteins
using machine learning algorithms. Proteins 63 (2006) 1031-1037.
[8] M.M. Gromiha, M. Suwa. Influence of amino acid properties for
discriminating outer membrane proteins at better accuracy. Biochim
Biophys Acta. 2006 Sep;1764(9):1493-7.
[9] Gromiha MM, Ahmad S, Suwa M. Neural network-based prediction of
transmembrane beta-strand segments in outer membrane proteins. J
Comput Chem. 2004 Apr 15;25(5):762-7.
[10] Gromiha MM, Yabuki Y, Kundu S, Suharnan S, Suwa M. (2007)
TMBETA-GENOME: database for annotated beta-barrel membrane
proteins in genomic sequences. Nucleic Acids Res. 35: D314-6
[11] M.H. Saier, Jr, C.V. Tran, R.D. Barabote. (2006) TCDB: the transporter
classification database for membrane transport protein analyses and
information Nucleic Acids Res, 34, D181-D186
[12] Horn F, Bettler E, Oliveira L, Campagne F, Cohen FE, Vriend G (2003)
GPCRDB information system for G protein-coupled receptors. Nucleic
Acids Res. 31:294-297.
[13] Schwacke R, Schneider A, Van Der Graaff E, Fischer K, Catoni E,
Desimone M, Frommer WB, Flugge UI, Kunze R. (2003)
ARAMEMNON, a Novel Database for Arabidopsis Integral Membrane
Proteins. Plant Physiol. 131: 16-26.
[14] Edvardsen O, Reiersen AL, Beukers MW, Kristiansen K. (2002) tGRAP,
the G-protein coupled receptors mutant database. Nucleic Acids Res. 30:
361-3
[15] Li H, Dai X, Zhao X. (2008) A nearest neighbor approach for automated
transporter prediction and categorization from protein sequences.
Bioinformatics. 24:1129-36.
[16] Kutik S, Stojanovski D, Becker L, Becker T, Meinecke M, Kr├╝ger V,
Prinz C, Meisinger C, Guiard B, Wagner R, Pfanner N, Wiedemann N.
Dissecting membrane insertion of mitochondrial beta-barrel proteins.
Cell. 2008 Mar 21;132(6):1011-24.
[17] M.H. Saier, Jr. A functional-phylogenetic classification system for
transmembrane solute transporters Microbiol. Mol. Biol. Rev,. (2000)
64, 354-411
[18] Gromiha MM, Yabuki Y, Suresh MX, Thangakani AM, Suwa M, Fukui
K. TMFunction: database for functional residues in membrane proteins.
Nucleic Acids Res. 2008; doi:10.1093/nar/gkn672.
[19] Wu CH, Apweiler R, Bairoch A, Natale DA, Barker WC, Boeckmann B,
Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ,
Mazumder R, O'Donovan C, Redaschi N, Suzek B. (2006) The
Universal Protein Resource (UniProt): an expanding universe of protein
information. Nucleic Acids Res. 34: D187-91.
[20] Berman H, Henrick K, Nakamura H, Markley JL. The worldwide Protein
Data Bank (wwPDB): ensuring a single, uniform archive of PDB data.
(2007) Nucleic Acids Res. 35: D301-3
[21] Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W,
Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of
protein database search programs. Nucleic Acids Res. 1997; 25: 3389-
3402.
[22] Witten IH, Frank E: Data Mining: Practical machine learning tools and
techniques, 2nd Edition, Morgan Kaufmann, San Francisco, 2005.
[23] Gromiha MM, Yabuki Y. (2008) Functional discrimination of membrane
proteins using machine learning techniques. BMC Bioinformatics.
9:135.
[24] Gromiha MM, Suwa M. (2007) Current developments on beta-barrel
membrane proteins: sequence and structure analysis, discrimination and
prediction. Curr. Protein Pept Sci. 8: 580-99.
[25] Chimento DP, Mohanty AK, Kadner RJ, Wiener MC. Substrate-induced
transmembrane signaling in the cobalamin transporter BtuB. Nat Struct
Biol. 2003, 10: 394-401.
[26] Chimento DP, Kadner RJ, Wiener MC. The Escherichia coli outer
membrane cobalamin transporter BtuB: structural analysis of calcium
and substrate binding, and identification of orthologous transporters by
sequence/ structure conservation. J Mol Biol. 2003, 332: 999-1014.
[27] Dutzler R, Campbell EB, MacKinnon R. Gating the selectivity filter in
ClC chloride channels. Science. 2003; 300:108-12.
[28] Huang Y, Lemieux MJ, Song J, Auer M, Wang DN. Structure and
mechanism of the glycerol-3-phosphate transporter from Escherichia
coli. Science. 2003; 301:616-20.
[29] Abramson J, Smirnova I, Kasho V, Verner G, Kaback HR, Iwata S.
Structure and mechanism of the lactose permease of Escherichia coli.
Science. 2003; 301:610-5.
[30] Taguchi YH, Gromiha MM. Application of amino acid occurrence for
discriminating different folding types of globular proteins. BMC
Bioinformatics 2007, 8, 404.
[31] Imai K, Horton P, Gromiha MM. Mitochondrial β-Signal; The End of
the Story? Cell (in press).
[32] Velours G, Boucheron C, Manon S, Camougrand N. Dual cell
wall/mitochondria localization of the 'SUN' family proteins. FEMS
Microbiol Lett. 2002 Feb 5;207(2):165-72
[33] Wimley WC. The versatile beta-barrel membrane protein. Curr Opin
Struct Biol. 2003 Aug;13(4):404-11
[34] Hiller S, Garces RG, Malia TJ, Orekhov VY, Colombini M, Wagner G.
Solution structure of the integral human membrane protein VDAC-1 in
detergent micelles. Science. 2008 Aug 29;321(5893):1206-10
[35] Tusnády GE, Dosztányi Z, Simon I. PDB_TM: selection and membrane
localization of transmembrane proteins in the protein data bank. Nucleic
Acids Res. 2005 Jan 1;33(Database issue):D275-8.
@article{"International Journal of Information, Control and Computer Sciences:51788", author = "M. Michael Gromiha and Y. Yabuki and K. Imai and P. Horton and K. Fukui", title = "Database Development and Discrimination Algorithms for Membrane Protein Functions", abstract = "We have developed a database for membrane protein functions, which has more than 3000 experimental data on functionally important amino acid residues in membrane proteins along with sequence, structure and literature information. Further, we have proposed different methods for identifying membrane proteins based on their functions: (i) discrimination of membrane transport proteins from other globular and membrane proteins and classifying them into channels/pores, electrochemical and active transporters, and (ii) β-signal for the insertion of mitochondrial β-barrel outer membrane proteins and potential targets. Our method showed an accuracy of 82% in discriminating transport proteins and 68% to classify them into three different transporters. In addition, we have identified a motif for targeting β-signal and potential candidates for mitochondrial β-barrel membrane proteins. Our methods can be used as effective tools for genome-wide annotations.
", keywords = "Membrane proteins, database, transporters,
discrimination, β-signal.", volume = "3", number = "1", pages = "40-5", }
