메뉴 건너뛰기




Volumn 40, Issue 9, 2013, Pages 3412-3420

Applications of evolutionary SVM to prediction of membrane alpha-helices

Author keywords

Alpha helix transmembrane domain; Genetic algorithm; Support vector machine

Indexed keywords

ALPHA-HELIX; AMINO ACID SEQUENCE; BIOCHEMICAL RESEARCH; CONVENTIONAL TECHNIQUES; CROSS VALIDATION; MEMBRANE PROTEINS; OPTIMISATIONS; PARAMETER OPTIMISATION; PHARMACEUTICAL INDUSTRY; REGION IDENTIFICATION; SLIDING WINDOW; STRUCTURAL DETERMINATION; STRUCTURAL INFORMATION; STRUCTURE BASED DRUG DESIGNS; TECHNICAL DIFFICULTIES; TRANS-MEMBRANE DOMAINS; TRANSMEMBRANE REGIONS;

EID: 84874658814     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.12.049     Document Type: Article
Times cited : (13)

References (52)
  • 1
    • 2942522711 scopus 로고    scopus 로고
    • A hidden Markov model method, capable of predicting and discriminating beta-barrel outer membrane proteins
    • P.G. Bagos, T.D. Liakopoulos, I.C. Spyropoulos, and S.J. Hamodrakas A hidden Markov model method, capable of predicting and discriminating beta-barrel outer membrane proteins BMC Bioinformatics 5 2004 29
    • (2004) BMC Bioinformatics , vol.5 , pp. 29
    • Bagos, P.G.1    Liakopoulos, T.D.2    Spyropoulos, I.C.3    Hamodrakas, S.J.4
  • 4
  • 6
    • 84874667915 scopus 로고    scopus 로고
    • Classifying membrane proteins in the proteome by using artificial neural networks based on the preferential parameters of amino acids
    • doi:10.1007/978-1-4020-8678-6-6 Tenreiro Machado, J. A.; Patkai, B.; Rudas, I. J. (Eds.) Springer, ISBN: 978-1-4020-8677-9
    • Bose, S. K.; Browne, A.; Kazemian H.; White K. (2009). Classifying membrane proteins in the proteome by using artificial neural networks based on the preferential parameters of amino acids, In Tenreiro Machado, J. A.; Patkai, B.; Rudas, I. J. (Eds.), Intelligent engineering systems and computational cybernetics, Springer, ISBN: 978-1-4020-8677-9, http://dx.doi.org/10.1007/978-1- 4020-8678-6-6, pp. 63-71.
    • (2009) Intelligent Engineering Systems and Computational Cybernetics , pp. 63-71
    • Bose, S.K.1    Browne, A.2    Kazemian, H.3    White, K.4
  • 10
    • 34249753618 scopus 로고
    • Support-vector networks, machine learning, Kluwer academic publishers, Boston
    • C. Cortes, and V.N. Vapnik Support-vector networks, machine learning, Kluwer academic publishers, Boston Manufactured in The Netherlands 20 1995 273 297
    • (1995) Manufactured in the Netherlands , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.N.2
  • 11
    • 0030759333 scopus 로고    scopus 로고
    • Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: The dense alignment surface method
    • M. Cserzo, E. Wallin, I. Simon, G. von Heijne, and A. Elofsson Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: The dense alignment surface method Protein Engineering 10 1997 673 676
    • (1997) Protein Engineering , vol.10 , pp. 673-676
    • Cserzo, M.1    Wallin, E.2    Simon, I.3    Von Heijne, G.4    Elofsson, A.5
  • 12
    • 34249683488 scopus 로고    scopus 로고
    • Membrane protein structure: Prediction versus reality
    • A. Elofsson, and G. von Heijne Membrane protein structure: Prediction versus reality Annual Review of Biochemistry 76 2007 125 140
    • (2007) Annual Review of Biochemistry , vol.76 , pp. 125-140
    • Elofsson, A.1    Von Heijne, G.2
  • 13
    • 0000484499 scopus 로고
    • Hydrophobic parameters - Pi of aminoacid side chains from the partitioning of N-acetyl-amino-acid amides
    • J.L. Fauchere, and V. Pliska Hydrophobic parameters - pi of aminoacid side chains from the partitioning of N-acetyl-amino-acid amides European Journal of Medicinal Chemistry 18 1983 369 375
    • (1983) European Journal of Medicinal Chemistry , vol.18 , pp. 369-375
    • Fauchere, J.L.1    Pliska, V.2
  • 15
    • 25444484398 scopus 로고    scopus 로고
    • TMB-hunt: An amino acid composition based method to screen proteomes for beta-barrel transmembrane proteins
    • A.G. Garrow, A. Agnew, and D.R. Westhead TMB-hunt: An amino acid composition based method to screen proteomes for beta-barrel transmembrane proteins BMC Bioinformatics 6 2005 56
    • (2005) BMC Bioinformatics , vol.6 , pp. 56
    • Garrow, A.G.1    Agnew, A.2    Westhead, D.R.3
  • 16
    • 0003722376 scopus 로고
    • Genetic algorithms in search, optimization, and machine learning
    • ed. Reading, Mass; Wokingham: Addison-Wesley
    • Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Repr. with corrections. ed. Reading, Mass; Wokingham: Addison-Wesley.
    • (1989) Repr. with Corrections
    • Goldberg, D.E.1
  • 17
    • 0042888610 scopus 로고    scopus 로고
    • Variation of amino acid properties in all-beta globular and outer membrane protein structures
    • M.M. Gromiha, and M. Suwa Variation of amino acid properties in all-beta globular and outer membrane protein structures International Journal of Biological Macromolecules 32 2003 93 98
    • (2003) International Journal of Biological Macromolecules , vol.32 , pp. 93-98
    • Gromiha, M.M.1    Suwa, M.2
  • 18
    • 27844596815 scopus 로고    scopus 로고
    • Transmembrane segments prediction and understanding using support vector machine and decision tree
    • J. He, H.J. Hu, R. Harrison, P.C. Tai, and Y. Pan Transmembrane segments prediction and understanding using support vector machine and decision tree Expert Systems with Applications 30 2006 64 72
    • (2006) Expert Systems with Applications , vol.30 , pp. 64-72
    • He, J.1    Hu, H.J.2    Harrison, R.3    Tai, P.C.4    Pan, Y.5
  • 19
    • 0000651660 scopus 로고
    • The distribution of positively charged residues in bacterial inner membrane proteins correlates with the trans-membrane topology
    • G. Heijne The distribution of positively charged residues in bacterial inner membrane proteins correlates with the trans-membrane topology EMBO Journal 5 1986 3021 3027
    • (1986) EMBO Journal , vol.5 , pp. 3021-3027
    • Heijne, G.1
  • 20
    • 0031826040 scopus 로고    scopus 로고
    • SOSUI: Classification and secondary structure prediction system for membrane proteins
    • T. Hirokawa, S. Boon-Chieng, and S. Mitaku SOSUI: Classification and secondary structure prediction system for membrane proteins Bioinformatics 14 1998 378 379
    • (1998) Bioinformatics , vol.14 , pp. 378-379
    • Hirokawa, T.1    Boon-Chieng, S.2    Mitaku, S.3
  • 21
    • 0000207681 scopus 로고
    • TMbase a database of membrane spanning proteins segments
    • K. Hofmann, and W. Stoffel TMbase a database of membrane spanning proteins segments Biological Chemistry Hoppe-Seyler 374 1993 166 170
    • (1993) Biological Chemistry Hoppe-Seyler , vol.374 , pp. 166-170
    • Hofmann, K.1    Stoffel, W.2
  • 23
    • 0035078840 scopus 로고    scopus 로고
    • Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor
    • I. Jacoboni, P.L. Martelli, P. Fariselli, V. de Pinto, and R. Casadio Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor Protein Science 10 2001 779 787
    • (2001) Protein Science , vol.10 , pp. 779-787
    • Jacoboni, I.1    Martelli, P.L.2    Fariselli, P.3    De Pinto, V.4    Casadio, R.5
  • 24
    • 0033578684 scopus 로고    scopus 로고
    • Protein secondary structure prediction based on position-specific scoring matrices
    • D.T. Jones Protein secondary structure prediction based on position-specific scoring matrices Journal of Molecular Biology 292 1999 195 202
    • (1999) Journal of Molecular Biology , vol.292 , pp. 195-202
    • Jones, D.T.1
  • 25
    • 34047151404 scopus 로고    scopus 로고
    • Improving the accuracy of transmembrane protein topology prediction using evolutionary information
    • D.T. Jones Improving the accuracy of transmembrane protein topology prediction using evolutionary information Bioinformatics 23 2007 538 544
    • (2007) Bioinformatics , vol.23 , pp. 538-544
    • Jones, D.T.1
  • 28
    • 2142657817 scopus 로고    scopus 로고
    • A combined transmembrane topology and signal peptide prediction method
    • L. Kall, A. Krogh, and E.L.L. Sonnhammer A combined transmembrane topology and signal peptide prediction method Journal of Molecular Biology 338 2004 1027 1036
    • (2004) Journal of Molecular Biology , vol.338 , pp. 1027-1036
    • Kall, L.1    Krogh, A.2    Sonnhammer, E.L.L.3
  • 30
    • 48349146731 scopus 로고    scopus 로고
    • Transmembrane helix prediction in proteins using hydrophobicity properties and higher-order statistics
    • I.K. Kitsas, L.J. Hadjileontiadis, and S.M. Panas Transmembrane helix prediction in proteins using hydrophobicity properties and higher-order statistics Computers in Biology and Medicine 38 2008 867 880
    • (2008) Computers in Biology and Medicine , vol.38 , pp. 867-880
    • Kitsas, I.K.1    Hadjileontiadis, L.J.2    Panas, S.M.3
  • 31
    • 0035910270 scopus 로고    scopus 로고
    • Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes
    • A. Krogh, B. Larsson, G. von Heijne, and E.L.L. Sonnhammer Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes Journal of Molecular Biology 305 2001 567 580
    • (2001) Journal of Molecular Biology , vol.305 , pp. 567-580
    • Krogh, A.1    Larsson, B.2    Von Heijne, G.3    Sonnhammer, E.L.L.4
  • 32
    • 0020475449 scopus 로고
    • A Simple method for displaying the hydropathic character of a protein
    • J. Kyte, and R.F. Doolittle A Simple method for displaying the hydropathic character of a protein Journal of Molecular Biology. 157 1982 105 132
    • (1982) Journal of Molecular Biology. , vol.157 , pp. 105-132
    • Kyte, J.1    Doolittle, R.F.2
  • 33
    • 0036389409 scopus 로고    scopus 로고
    • Amino acid encoding schemes from protein structure alignments: Multi-dimensional vectors to describe residue types
    • K. Lin, A.C.W. May, and W.R. Taylor Amino acid encoding schemes from protein structure alignments: Multi-dimensional vectors to describe residue types Journal of Theoretical Biology 216 2002 361 365
    • (2002) Journal of Theoretical Biology , vol.216 , pp. 361-365
    • Lin, K.1    May, A.C.W.2    Taylor, W.R.3
  • 34
    • 4243671284 scopus 로고    scopus 로고
    • A sequence-profile-based HMM for predicting and discriminating beta-barrel membrane proteins
    • P.L. Martelli, P. Fariselli, A. Krogh, and R. Casadio A sequence-profile-based HMM for predicting and discriminating beta-barrel membrane proteins Journal of Molecular Biology 18 2002 S46 S53
    • (2002) Journal of Molecular Biology , vol.18
    • Martelli, P.L.1    Fariselli, P.2    Krogh, A.3    Casadio, R.4
  • 35
  • 36
    • 67649472570 scopus 로고    scopus 로고
    • Transmembrane protein topology prediction using support vector machines
    • T. Nugent, and D.T. Jones Transmembrane protein topology prediction using support vector machines BMC Bioinformatics 10 2009 159
    • (2009) BMC Bioinformatics , vol.10 , pp. 159
    • Nugent, T.1    Jones, D.T.2
  • 37
    • 0032834939 scopus 로고    scopus 로고
    • An hierarchical artificial neural network system for the classification of transmembrane proteins
    • C. Pasquier, and S.J. Hamodrakas An hierarchical artificial neural network system for the classification of transmembrane proteins Protein Engineering 12 1999 631 634
    • (1999) Protein Engineering , vol.12 , pp. 631-634
    • Pasquier, C.1    Hamodrakas, S.J.2
  • 38
    • 0032989348 scopus 로고    scopus 로고
    • A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: The PRED-TMR algorithm
    • C. Pasquier, V.J. Promponas, G.A. Palaios, J.S. Hamodrakas, and S.J. Hamodrakas A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: The PRED-TMR algorithm Protein Engineering 12 1999 381 385
    • (1999) Protein Engineering , vol.12 , pp. 381-385
    • Pasquier, C.1    Promponas, V.J.2    Palaios, G.A.3    Hamodrakas, J.S.4    Hamodrakas, S.J.5
  • 39
    • 0028279520 scopus 로고
    • Prediction of transmembrane segments in proteins utilizing multiple sequence alignments
    • B. Persson, and P. Argos Prediction of transmembrane segments in proteins utilizing multiple sequence alignments Journal of Molecular Biology 237 1994 182 192
    • (1994) Journal of Molecular Biology , vol.237 , pp. 182-192
    • Persson, B.1    Argos, P.2
  • 40
    • 44649124374 scopus 로고    scopus 로고
    • A new SVM-based decision fusion method using multiple granular windows for protein secondary structure prediction
    • A. Reyaz-Ahmed, and Y. Zhang A new SVM-based decision fusion method using multiple granular windows for protein secondary structure prediction Rough Sets and Knowledge Technology 5009 2008 324 331
    • (2008) Rough Sets and Knowledge Technology , vol.5009 , pp. 324-331
    • Reyaz-Ahmed, A.1    Zhang, Y.2
  • 42
    • 0030038634 scopus 로고    scopus 로고
    • Topology prediction for helical transmembrane proteins at 86% accuracy
    • B. Rost, P. Fariselli, and R. Casadio Topology prediction for helical transmembrane proteins at 86% accuracy Protein Science 5 1996 1704 1718
    • (1996) Protein Science , vol.5 , pp. 1704-1718
    • Rost, B.1    Fariselli, P.2    Casadio, R.3
  • 44
    • 84874645825 scopus 로고    scopus 로고
    • [Accessed 02 November 2011]
    • SwissProt UniProtKB/Swiss-Prot. (2011). [Accessed 02 November 2011]. Available at: < http://web.expasy.org/docs/swiss-prot-guideline.html >.
    • (2011) SwissProt UniProtKB/Swiss-Prot
  • 45
    • 0032561132 scopus 로고    scopus 로고
    • Principles governing amino acid composition of integral membrane proteins: Application to topology prediction
    • G.E. Tusnady, and I. Simon Principles governing amino acid composition of integral membrane proteins: Application to topology prediction Journal of Molecular Biology 283 1998 489 506
    • (1998) Journal of Molecular Biology , vol.283 , pp. 489-506
    • Tusnady, G.E.1    Simon, I.2
  • 46
    • 33746361201 scopus 로고    scopus 로고
    • Structural classification and prediction of reentrant regions in α-helical transmembrane proteins: Application to complete genomes
    • H. Viklund, E. Granseth, and A. Elofsson Structural classification and prediction of reentrant regions in α-helical transmembrane proteins: Application to complete genomes Journal of Molecular Biology 361 2006 591 603
    • (2006) Journal of Molecular Biology , vol.361 , pp. 591-603
    • Viklund, H.1    Granseth, E.2    Elofsson, A.3
  • 47
    • 48249151108 scopus 로고    scopus 로고
    • OCTOPUS: Improving topology prediction by two-track ANN-based preference scores and an extended topological grammar
    • H. Viklund, and A. Elofsson OCTOPUS: Improving topology prediction by two-track ANN-based preference scores and an extended topological grammar Bioinformatics 24 2008 1662 1668
    • (2008) Bioinformatics , vol.24 , pp. 1662-1668
    • Viklund, H.1    Elofsson, A.2
  • 50
    • 8444248846 scopus 로고    scopus 로고
    • Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition
    • M. Wang, J. Yang, G.-P. Liu, Z.-J. Xu, and K.-C. Chou Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition Protein Engineering Design and Selection 17 2004 509 516
    • (2004) Protein Engineering Design and Selection , vol.17 , pp. 509-516
    • Wang, M.1    Yang, J.2    Liu, G.-P.3    Xu, Z.-J.4    Chou, K.-C.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.