Knowledge-based computational intelligence development for predicting protein secondary structures from sequences

Expert Review of Proteomics

Volumn 5, Issue 5, 2008, Pages 653-662

  Shen, Hong Bin ^a   Yi, Dong Liang ^a   Yao, Li Xiu ^a   Yang, Jie ^a   Chou, Kuo Chen ^a,b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b GORDON LIFE SCIENCE INSTITUTE   (United States)

Author keywords

Computational intelligence; K nearest neighbor; MemBrain; Membrane protein; Neural networks; Secondary structure; Support vector machine; Transmembrane helix

Indexed keywords

GLOBULAR PROTEIN; MEMBRANE PROTEIN;

ACCURACY; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; AUTOMATION; BIOINFORMATICS; COMPUTER PREDICTION; KNOWLEDGE BASE; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; REVIEW; STRUCTURAL PROTEOMICS;

ARTIFICIAL INTELLIGENCE; MEMBRANE PROTEINS; PROTEIN STRUCTURE, SECONDARY; PROTEOME; PROTEOMICS;

EID: 54349114910     PISSN: 14789450     EISSN: 17448387     Source Type: Journal    
DOI: 10.1586/14789450.5.5.653     Document Type: Review

References (116)

1. Chou KC, Shen HB. Cell-PLoc: a package of web servers for predicting subcellular localization of proteins in various organisms. Nat. Protoc. 3(2), 153-162 (2008).
2. Punta M, Forrest LR, Bigelow H, Kernytsky A, Liu J, Rost B. Membrane protein prediction methods. Methods 41(4), 460-474 (2007). •• Paper describing the membrane protein structure prediction.
3. Elofsson A, von Heijne G. Membrane protein structure: prediction versus reality. Annu. Rev. Biochem. 76, 125-140 (2007).
4. Schnell JR, Chou JJ. Structure and mechanism of the M2 proton channel of influenza A virus. Nature 451(7178), 591-595 (2008).
5. Douglas SM, Chou JJ, Shih WM. DNA-nanotube-induced alignment of membrane proteins for NMR structure determination. Proc. Natl Acad. Sci. USA 104(16), 6644-6648 (2007).
6. Bairoch A, Apweiler R. The SWISS-PROT protein sequence data bank and its supplement TrEMBL. Nucleic Acids Res. 25, 31-36 (2000).
7. Apgar JR, Gutwin KN, Keating AE. Predicting helix orientation for coiled-coil dimers. Proteins 72(3), 1048-65 (2008).
8. Kolodny R, Petrey D, Honig B. Protein structure comparison: implications for the nature of 'fold space', and structure and function prediction. Curr. Opin. Struct. Biol. 16(3), 393-398 (2006).
9. Baker D. A surprising simplicity to protein folding. Nature 405(6782), 39-42 (2000).
10. Di Francesco V, Munson PJ, Garnier J. FORESST: fold recognition from secondary structure predictions of proteins. Bioinformatics 15(2), 131-140 (1999).
11. Wallqvist A, Fukunishi Y, Murphy LR, Fadel A, Levy RM. Iterative sequence/ secondary structure search for protein homologs: comparison with amino acid sequence alignments and application to fold recognition in genome databases. Bioinformatics 16(11), 988-1002 (2000).
12. An Y, Friesner RA. A novel fold recognition method using composite predicted secondary structures. Proteins 48(2), 352-366 (2002).
13. Bindewald E, Cestaro A, Hesser J, Heiler M, Tosatto SC. MANIFOLD: protein fold recognition based on secondary structure, sequence similarity and enzyme classification. Protein Eng. 16(11), 785-789 (2003).
14. Wang K, Samudrala R. FSSA: a novel method for identifying functional signatures from structural alignments. Bioinformatics 21(13), 2969-2977 (2005).
15. Bystroff C, Thorsson V, Baker D. HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. J. Mol. Biol. 301(1), 173-190 (2000).
16. Rost B. Review: protein secondary structure prediction continues to rise. J. Struct. Biol. 134(2-3), 204-218 (2001).
17. Shen HB, Chou KC. Ensemble classifier for protein fold pattern recognition. Bioinformatics 22(14), 1717-1722 (2006).
18. Chen K, Kurgan L. PFRES: protein fold classification by using evolutionary information and predicted secondary structure. Bioinformatics 23(21), 2843-2850 (2007).
19. Song J, Yuan Z, Tan H, Huber T, Burrage K. Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure. Bioinformatics 23(23), 3147-3154 (2007).
20. Ferre F, Clote P. Disulfide connectivity prediction using secondary structure information and diresidue frequencies. Bioinformatics 21(10), 2336-2346 (2005).
21. Song J, Burrage K. Predicting residue-wise contact orders in proteins by support vector regression. BMC Bioinformatics 7, 425 (2006).
22. Ivankov DN, Finkelstein AV. Prediction of protein folding rates from the amino acid sequence-predicted secondary structure. Proc. Natl Acad. Sci. USA 101(24), 8942-8944 (2004).
23. Song J, Burrage K, Yuan Z, Huber T. Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information. BMC Bioinformatics 7, 124 (2006).
24. Schlessinger A, Rost B. Protein flexibility and rigidity predicted from sequence. Proteins 61(1), 115-126 (2005).
25. Schlessinger A, Yachdav G, Rost B. PROFbval: predict flexible and rigid residues in proteins. Bioinformatics 22(7), 891-893 (2006).
26. Ofran Y, Rost B. ISIS: interaction sites identified from sequence. Bioinformatics 23(2), E13-E16 (2007).
27. Ofran Y, Rost B. Protein-protein interaction hotspots carved into sequences. PLoS Comput. Biol. 3(7), e119 (2007).
28. Song J, Tan H, Takemoto K, Akutsu T. HSEpred: predict half-sphere exposure from protein sequences. Bioinformatics 24(13), 1489-1497 (2008).
29. Garg A, Kaur H, Raghava GP. Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure. Proteins 61(2), 318-324 (2005).
30. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12), 2577-2637 (1983).
31. Lange OF, Schafer LV, Grubmuller H. Flooding in GROMACS: accelerated barrier crossings in molecular dynamics. J. Comput. Chem. 27(14), 1693-1702 (2006).
32. Frishman D, Argos P. Knowledge-based protein secondary structure assignment. Proteins 23(4), 566-579 (1995).
33. Fodje MN, Al-Karadaghi S. Occurrence, conformational features and amino acid propensities for the pi-helix. Protein Eng. 15(5), 353-358 (2002).
34. Martin J, Letellier G, Marin A, Taly JF, de Brevern AG, Gibrat JF. Protein secondary structure assignment revisited: a detailed analysis of different assignment methods. BMC Struct. Biol. 5, 17 (2005).
35. Homaeian L, Kurgan LA, Ruan J, Cios KJ, Chen K. Prediction of protein secondary structure content for the twilight zone sequences. Proteins 69(3), 486-498 (2007).
36. Zhou H, Zhou Y. Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model-based method. Protein Sci. 12(7), 1547-1555 (2003).
37. Koebnik R, Locher KP, Van Gelder P. Structure and function of bacterial outer membrane proteins: barrels in a nutshell. Mol. Microbiol. 37(2), 239-253 (2000).
38. Berman HM, Westbrook J, Feng Z et al. The protein data bank. Nucleic Acids Res. 28(1), 235-242 (2000).
39. Boberg J, Salakoski T, Vihinen M. Selection of a representative set of structures from Brookhaven Protein Data Bank. Proteins 14(2), 265-276 (1992).
40. Hobohm U, Sander C. Enlarged representative set of protein structures. Protein Sci. 3(3), 522-524 (1994).
41. Cuff JA, Barton GJ. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins 34(4), 508-519 (1999).
42. Riis SK, Krogh A. Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments. J. Comput. Biol. 3(1), 163-183 (1996).
43. Van Walle I, Lasters I, Wyns L. SABmark - a benchmark for sequence alignment that covers the entire known fold space. Bioinformatics 21(7), 1267-1268 (2005).
44. Carugo O. Predicting residue solvent accessibility from protein sequence by considering the sequence environment. Protein Eng. 13(9), 607-609 (2000).
45. White SH. Biochemistry. Crowds of syntaxins. Science 317(5841), 1045-1046 (2007).
46. Jayasinghe S, Hristova K, White SH. MPtopo: a database of membrane protein topology. Protein Sci. 10(2), 455-458 (2001).
47. Horn F, Bettler E, Oliveira L, Campagne F, Cohen FE, Vriend G. GPCRDB information system for G protein-coupled receptors. Nucleic Acids Res. 31(1), 294-297 (2003).
48. Krigbaum WR, Knutton SP. Prediction of the amount of secondary structure in a globular protein from its aminoacid composition. Proc. Natl Acad. Sci. USA 70(10), 2809-2813 (1973).
49. Chou PY, Fasman GD. Prediction of protein conformation. Biochemistry 13(2), 222-245 (1974). •• Pioneering work in protein secondary structure prediction.
50. Chou PY, Fasman GD. Prediction of the secondary structure of proteins from their amino acid sequence. Adv. Enzymol. Relat. Areas Mol. Biol. 47, 45-148 (1978).
51. Kazemian M, Moshiri B, Nikbakht H, Lucas C. A new expertness index for assessment of secondary structure prediction engines. Comput. Biol. Chem. 31(1), 44-47 (2007). •• First paper describing secondary structure prediction by using evolutional information.
52. Rost B. PHD: predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol. 266, 525-539 (1996).
53. Rost B, Casadio R, Fariselli P, Sander C. Transmembrane helices predicted at 95% accuracy. Protein Sci. 4(3), 521-533 (1995).
54. Schaffer AA, Aravind L, Madden TL et al. Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res. 29(14), 2994-3005 (2001).
55. Claros MG, von Heijne G. TopPred II: an improved software for membrane protein structure predictions. Comput. Appl. Biosci. 10(6), 685-686 (1994).
56. Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157(1), 105-132 (1982).
57. Chamberlain AK, Lee Y, Kim S, Bowie JU. Snorkeling preferences foster an amino acid composition bias in transmembrane helices. J. Mol. Biol. 339(2), 471-479 (2004).
58. Wimley WC, White SH. Experimentally determined hydrophobicity scale for proteins at membrane interfaces. Nat. Struct. Biol. 3(10), 842-848 (1996). •• A tool developed to predict half-transmembrane helices.
59. Shen H, Chou JJ. MemBrain: improving the accuracy of predicting transmembrane helices. PLoS ONE 3(6), E2399 (2008).
60. Qian N, Sejnowski TJ. Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol. 202(4), 865-884 (1988).
61. Asai K, Hayamizu S, Handa K. Prediction of protein secondary structure by the hidden Markov model. Comput. Appl. Biosci. 9(2), 141-146 (1993).
62. Hua S, Sun Z. A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J. Mol. Biol. 308(2), 397-407 (2001).
63. Bondugula R, Duzlevski O, Xu D. Profiles and fuzzy K-Nearest neighbor algorithm for protein secondary structure prediction. Pages 85-94. Chen Y-PP, Wong L (Eds). Proceedings of 3rd Asia-Pacific Bioinformatics Conference. Singapore, 17-21 January 2005. Imperial College Press, London, UK.
64. Bondugula R, Xu D. MUPRED: a tool for bridging the gap between template based methods and sequence profile based methods for protein secondary structure prediction. Proteins 66(3), 664-670 (2007).
65. Shen HB, Chou KC. Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. Protein Eng. Des. Sel. 20(11), 561-567 (2007).
66. Shen HB, Yang J, Chou KC. Methodology development for predicting subcellular localization and other attributes of proteins. Expert Rev. Proteomics 4(4), 453-463 (2007).
67. Chou KC, Shen HB. Recent progress in protein subcellular location prediction. Anal. Biochem. 370(1), 1-16 (2007).
68. Frey L, Edgerton M, Fisher D, Levy S. Ensemble stump classifiers and gene expression signatures in lung cancer. Medinfo 12(Pt 2), 1255-1259 (2007).
69. Rodriguez JJ, Kuncheva LI, Alonso CJ. Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619-1630 (2006). • Describes the consensus method development for predicting secondary structure.
70. Albrecht M, Tosatto SC, Lengauer T, Valle G. Simple consensus procedures are effective and sufficient in secondary structure prediction. Protein Eng. 16(7), 459-462 (2003).
71. Martelli PL, Fariselli P, Casadio R. An ENSEMBLE machine learning approach for the prediction of all-α membrane proteins. Bioinformatics 19(Suppl. 1), I205-I211 (2003).
72. Taylor PD, Attwood TK, Flower DR. BPROMPT: A consensus server for membrane protein prediction. Nucleic Acids Res. 31(13), 3698-3700 (2003).
73. Nilsson J, Persson B, von Heijne G. Consensus predictions of membrane protein topology. FEBS Lett. 486(3), 267-269 (2000).
74. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292(2), 195-202 (1999).
75. Cuff JA, Barton GJ. Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 40(3), 502-511 (2000).
76. JM K, MR G, JA G. A fuzzy K-nearest neighbor algorithm. IEEE Trans. SMC 15, 580-586 (1985).
77. Baldi P, Brunak S, Frasconi P, Soda G, Pollastri G. Exploiting the past and the future in protein secondary structure prediction. Bioinformatics 15(11), 937-946 (1999).
78. Cuthbertson JM, Doyle DA, Sansom MS. Transmembrane helix prediction: a comparative evaluation and analysis. Protein Eng. Des. Sel. 18(6), 295-308 (2005). •• Describes the use of membrane insertion free-energies to predict transmembrane helices.
79. Bernsel A, Viklund H, Falk J, Lindahl E, von Heijne G, Elofsson A. Prediction of membrane-protein topology from first principles. Proc. Natl Acad. Sci. USA 105(20), 7177-7181 (2008).
80. Fu D, Libson A, Miercke LJ et al. Structure of a glycerol-conducting channel and the basis for its selectivity. Science 290(5491), 481-486 (2000).
81. Lieberman RL, Rosenzweig AC. Crystal structure of a membrane-bound metalloenzyme that catalyses the biological oxidation of methane. Nature 434(7030), 177-182 (2005).
82. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305(3), 567-580 (2001).
83. Hirokawa T, Boon-Chieng S, Mitaku S. SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics 14(4), 378-379 (1998).
84. Meyer B, Kuever J. Homology modeling of dissimilatory APS reductases (AprBA) of sulfur-oxidizing and sulfate-reducing prokaryotes. PLoS ONE 3(1), e1514 (2008).
85. Li D, Tang HY, Speicher DW. A structural model of the erythrocyte spectrin heterodimer initiation site determined using homology modeling and chemical cross-linking. J. Biol. Chem. 283(3), 1553-1562 (2008).
86. Li M, Wang B. Homology modeling and examination of the effect of the D92E mutation on the H5N1 nonstructural protein NS1 effector domain. J. Mol. Model 13(12), 1237-1244 (2007).
87. Fernandez-Fuentes N, Rai BK, Madrid-Aliste CJ, Fajardo JE, Fiser A. Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments. Bioinformatics 23(19), 2558-2565 (2007).
88. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305(3), 567-580 (2001).
89. Prodohl A, Weber M, Dreher C, Schneider D. A mutational study of transmembrane helix-helix interactions. Biochimie 89(11), 1433-1437 (2007).
90. Guharoy M, Chakrabarti P. Secondary structure based analysis and classification of biological interfaces: identification of binding motifs in protein-protein interactions. Bioinformatics 23(15), 1909-1918 (2007).
91. Sal-Man N, Gerber D, Bloch I, Shai Y. Specificity in transmembrane helix-helix interactions mediated by aromatic residues. J. Biol. Chem. 282(27), 19753-19761 (2007).
92. Liang Y, Fotiadis D, Filipek S, Saperstein DA, Palczewski K, Engel A. Organization of the G protein-coupled receptors rhodopsin and opsin in native membranes. J. Biol. Chem. 278(24), 21655-21662 (2003).
93. Granseth E, von Heijne G, Elofsson A. A study of the membrane-water interface region of membrane proteins. J. Mol. Biol. 346(1), 377-385 (2005).
94. Rapp M, Seppala S, Granseth E, von Heijne G. Emulating membrane protein evolution by rational design. Science 315(5816), 1282-1284 (2007).
95. Kurgan LA, Stach W, Ruan J. Novel scales based on hydrophobicity indices for secondary protein structure. J. Theor. Biol. 248(2), 354-366 (2007).
96. Ouali M, King RD. Cascaded multiple classifiers for secondary structure prediction. Protein Sci. 9(6), 1162-1176 (2000).
97. Zhou H, Zhou Y. Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model-based method. Protein Sci. 12(7), 1547-1555 (2003).
98. Cserzo M, Eisenhaber F, Eisenhaber B, Simon I. TM or not TM: transmembrane protein prediction with low false positive rate using DAS-TMfilter. Bioinformatics 20(1), 136-137 (2004).
99. Kall L, Krogh A, Sonnhammer EL. A combined transmembrane topology and signal peptide prediction method. J. Mol. Biol. 338(5), 1027-1036 (2004).
100. The UniProtKB/Swiss-Prot Protein Knowledgebase www.ebi.ac.uk/swissprot/
101. MemBrain: improving the accuracy of predicting transmembrane helices www.csbio.sjtu.edu.cn/bioinf/MemBrain/
102. MemBrain: improving the accuracy of predicting transmembrane helices http://chou.med.harvard.edu/bioinf/MemBrain/
103. RCSB protein data bank www.rcsb.org/pdb/home/home.do
104. PDBselect - selection of a representative set of PDB chains http://bioinfo.tg.fh-giessen.de/pdbselect/
105. University of Dundee: JPRED training data www.compbio.dundee.ac.uk/ jpred_v2/data/
106. Vrije Universiteit Brussel: Informatics http://bioinformatics.vub.ac.be/
107. Index of /raghava/sarpred/data/Manesh-215 www.imtech.res.in/raghava/ sarpred/data/Manesh-215/
108. Membrane Proteins of Known Structure http://blanco.biomol.uci.edu/ Membrane_Proteins_xtal.html
109. MPtopo Database Search http://blanco.biomol.uci.edu/mptopo/
110. GPCRDB: information system for G-protein-coupled receptors www.gpcr.org/7tm/
111. THUMBUP: a transmembrane-helical-protein topology predictor using mean burial propensity of amino acid residues. http://sparks.informatics.iupui.edu/ Softwares-Services_files/thumbup.htm
112. SOSUI: classification and Secondary Structure Prediction of Membrane Proteins http://bp.nuap.nagoya-u.ac.jp/sosui/
113. DAS-TMfilter server http://mendel.imp.ac.at/sat/DAS/DAS.html
114. Mobyle portal - toppred http://bioweb.pasteur.fr/seqanal/interfaces/ toppred.html
115. TMHMM Server v. 2.0: prediction of transmembrane helices in proteins www.cbs.dtu.dk/services/TMHMM/
116. Phobius: a combined transmembrane topology and signal peptide predictor http://phobius.cgb.ki.se/