Review: Protein secondary structure prediction continues to rise

Journal of Structural Biology

Volumn 134, Issue 2-3, 2001, Pages 204-218

  Rost, Burkhard ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MEMBRANE PROTEIN; PROTEIN;

ACCURACY; BINDING SITE; DATA BASE; GENOME ANALYSIS; NERVE CELL NETWORK; POINT MUTATION; PREDICTION; PRIORITY JOURNAL; PROTEIN FUNCTION; PROTEIN STRUCTURE; REVIEW;

EQUUS CABALLUS;

EID: 0035782925     PISSN: 10478477     EISSN: None     Source Type: Journal    
DOI: 10.1006/jsbi.2001.4336     Document Type: Article

References (128)

1. Young, M., Kirshenbaum, K., Dill, K. A., and Highsmith, S. (1999) Predicting conformational switches in proteins, Protein Sci. 8, 1752-1764. Regions predicted with equally strong preferences for two secondary structure states are identified and correlated to regions undergoing structural rearrangements upon binding or environmental changes. The authors collect a data set of 16 test proteins and achieve an impressive accuracy in predicting structural switches.
2. Kabsch, W., and Sander, C. (1983) Dictionary of protein secondary structure: Pattern recognition of hydrogen bonded and geometrical features, Biopolymers 22, 2577-2637.
3. Bystroff, C., Thorsson, V., and Baker, D. (2000) HMMSTR: A hidden Markov model for local sequence-structure correlations in proteins, J. Mol. Biol. 301, 173-190. The authors develop a hidden Markov model with highly branched topology to assemble local regions of protein structure. The resulting prediction of 3D structure appears often correct. Reduced to predicting secondary structure, the authors report a surprisingly high level of 74%.
4. Cuff, J. A., Clamp, M. E., Siddiqui, A. S., Finlay, M., and Barton, G. J. (1998) JPred: A consensus secondary structure prediction server, Bioinformatics 14, 892-893.
5. Cuff, J. A., and Barton, G. J. (1999) Evaluation and improvement of multiple sequence methods for protein secondary structure prediction, Proteins 34, 508-519. JPred combines various prediction methods. The paper presents a good example of how to carefully evaluate prediction methods.
6. Cuff, J. A., and Barton, G. J. (2000) Application of multiple sequence alignment profiles to improve protein secondary structure prediction, Proteins 40, 502-511. The authors input divergent families into neural network systems. In particular, they show for the first time that PSI-BLAST alignments are competitive with dynamic programming and with hidden Markov-based alignments.
7. Rost, B. (1996) PHD: Predicting one-dimensional protein structure by profile based neural networks, Methods Enzy-mol. 266, 525-539.
8. Przybylski, D., and Rost, B. (2000) PSI-BLAST for Structure Prediction: Plug-in and Win, Columbia University. World Wide Web URL: http://cubic.bioc.columbia.edu/papers/.
9. Rost, B. (2000) Better Secondary Structure Prediction Through more Data, Columbia University. World Wide Web URL: http://cubic.bioc.columbia.edu/predictprotein.
10. Altschul, S., Madden, T., Shaffer, A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D. (1997) Gapped Blast and PSI-Blast: A new generation of protein database search programs, Nucleic Acids Res. 25, 3389-3402. BLAST became useful due to its speed. PSI-BLAST extends the original concept in many ways (introducing gaps, basing alignments on position-specific profiles, allowing iterated searches, applying dynamic programming to fill regions between very similar fragments). The result is a method that is both fast and accurate. Its impact on bioinformatics continues to grow not only for secondary structure prediction.
11. Jones, D. T. (1999) Protein secondary structure prediction based on position-specific scoring matrices, J. Mol. Biol. 292, 195-202. David Jones was the first to successfully use PSI-BLAST profiles to automatically predict secondary structure. In this trendsetting paper, he describes some of the precautions necessary to avoid accumulating nonsimilar proteins in the iterated PSI-BLAST search.
12. Karplus, K., Barrett, C., and Hughey, R. (1998) Hidden Markov models for detecting remote protein homologies, Bioinformatics 14, 846-856.
13. Baldi, P., Brunak, S., Frasconi, P., Soda, G., and Pollastri, G. (1999) Exploiting the past and the future in protein secondary structure prediction, Bioinformatics 15, 937-946. A fairly smart system of recursive neural network is pioneered in this work that addresses the problem of losing correlations between adjacent residues. The resulting method appears to reach levels comparable to those of PSIPRED without making full use of very diverged families.
14. Pauling, L., and Corey, R. B. (1951) Configurations of polypeptide chains with favored orientations around single bonds: Two new pleated sheets, Proc. Natl. Acad. Sci. USA 37, 729-740.
15. Pauling, L., Corey, R. B., and Branson, H. R. (1951) The structure of proteins: Two hydrogen-bonded helical configu-rations of the polypeptide chain, Proc. Natl. Acad. Sci. USA 37, 205-234.
16. Kendrew, J. C., Dickerson, R. E., Strandberg, B. E., Hart, R. J., Davies, D. R., and Phillips, D. C. (1960) Structure of myoglobin: A three-dimensional Fourier synthesis at 2 Å resolution, Nature 185, 422-427.
17. Perutz, M. F., Rossmann, M. G., Cullis, A. F., Muirhead, G., Will, G., and North, A. T. (1960) Structure of haemoglobin: A three-dimensional Fourier synthesis at 5.5 Å resolution, obtained by X-ray analysis, Nature 185, 416-422.
18. Szent-Györgyi, A. G., and Cohen, C. (1957) Role of proline in polypeptide chain configuration of proteins, Science 126, 697.
19. Rost, B., and Sander, C. (2000) Third generation prediction of secondary structure, in Webster, D. (Ed.), Protein Structure Prediction: Methods and Protocols, pp. 71-95, Humana Press, Clifton, NJ.
20. Dickerson, R. E., Timkovich, R., and Almassy, R. J. (1976) The cytochrome fold and the evolution of bacterial energy metabolism, J. Mol. Biol. 100, 473-491.
21. Zvelebil, M. J., Barton, G. J., Taylor, W. R., and Sternberg, M. J. E. (1987) Prediction of protein secondary structure and active sites using alignment of homologous sequences, J. Mol. Biol. 195, 957-961.
22. Rost, B., and Sander, C. (1993) Prediction of protein secondary structure at better than 70% accuracy, J. Mol. Biol. 232, 584-599.
23. Rost, B. (1999) Twilight zone of protein sequence alignments, Protein Eng. 12, 85-94.
24. Levin, J. M., Pascarella, S., Argos, P., and Garnier, J. (1993) Quantification of secondary structure prediction improvement using multiple alignment, Protein Eng. 6, 849-854.
25. Barton, G. J. (1996) Protein sequence alignment and database scanning, in Sternberg, M. J. E. (Ed.), Protein Structure Prediction, Vol. 170, pp. 31-64, Oxford Univ. Press, London.
26. Eddy, S. R. (1998) Profile hidden Markov models, Bioinfor-matics 14, 755-763.
27. Gerstein, M., and Levitt, M. (1997) A structural census of the current population of protein sequences, Proc. Natl. Acad. Sci. USA 94, 11911-11916.
28. Teichmann, S. A., Chothia, C., and Gerstein, M. (1999) Advances in structural genomics, Curr. Opin. Struct. Biol. 9, 390-399.
29. Frishman, D. (2000) PEDANT: Protein Extraction, Description, and Analysis Tool, Max-Planck-Institute, Munich. World Wide Web URL: http://pedant.mips.biochem.mpg.de/.
30. Liu, J., and Rost, B. (2000) Analysing All Proteins in Entire Genomes, CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, World Wide Web URL: http://cubic.bioc.columbia.edu/genomes/.
31. Monne, M., Hermansson, M., and von Heijne, G. (1999) A turn propensity scale for transmembrane helices, J. Mol. Biol. 288, 141-145. A particular problem of predicting membrane helices is that often short hydrophobic segments are evolutionarily conserved between membrane helices. In this paper the authors measure the propensities of forming short turns for all amino acids.
32. Lio, P., and Vannucci, M. (2000) Wavelet change-point prediction of transmembrane proteins, Bioinformatics 16, 376-382.
33. Pappu, R. V., Marshall, G. R., and Ponder, J. W. (1999) A potential smoothing algorithm accurately predicts trans-membrane helix packing [Published erratum appears in Nat. Struct. Biol. 1999, 6(2), 199], Nat. Struct. Biol. 6, 50-55.
34. Pilpel, Y., Ben-Tal, N., and Lancet, D. (1999) kPROT: A knowledge-based scale for the propensity of residue orientation in transmembrane segments. Application to membrane protein structure prediction, J. Mol. Biol. 294, 921-935. Based on a repository of predicted membrane helices, the authors investigate the information about topology contained in the hydrophobicity scale introduced. They also show that single-span helices differ from multispan helices. Finally, the kPROT scale is shown to predict the angle of the helices with errors of less than 41°.
35. Pasquier, C., Promponas, V. J., Palaios, G. A., Hamodrakas, J. S., and Hamodrakas, S. J. (1999) A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: The PRED-TMR algorithm, Protein Eng. 12, 381-385.
36. Chou, K. C., and Elrod, D. W. (1999) Prediction of membrane protein types and subcellular locations, Proteins 34, 137-153. Membrane helices are predicted and used to distinguish between different membrane types. The work excels in many ways. However, the evaluation of prediction accuracy is not fully convincing.
37. Kühlbrandt, W., and Gouaux, E. (1999) Membrane proteins, Curr. Opin. Struct. Biol. 9, 445-447.
38. Rost, B., and O'Donoghue, S. I. (1997) Sisyphus and prediction of protein structure, Comput. Appl. Biosci. 13, 345-356.
39. De Fays, K., Tibor, A., Lambert, C., Vinals, C., Denoel, P., De Bolle, X., Wouters, J., Letesson, J. J., and Depiereux, E. (1999) Structure and function prediction of the Brucella abortus P39 protein by comparative modeling with marginal sequence similarities, Protein Eng. 12, 217-223.
40. Lupas, A. (1997) Predicting coiled-coil regions in proteins, Curr. Opin. Struct. Biol. 7, 388-393.
41. Wolf, E., Kim, P. S., and Berger, B. (1997) MultiCoil: A program for predicting two- and three-stranded coiled coils, Protein Sci. 6, 1179-1189.
42. O'Donoghue, S. I., and Nilges, M. (1997) Tertiary structure prediction using mean-force potentials and internal energy functions: Successful prediction for coiled-coil geometries, Folding Design 2, S47-S52.
43. Kolinski, A., Skolnick, J., Godzik, A., and Hu, W. P. (1997) A method for the prediction of surface "U"-turns and transglobular connections in small proteins, Proteins 27, 290-308.
44. Shepherd, A. J., Gorse, D., and Thornton, J. M. (1999) Prediction of the location and type of beta-turns in proteins using neural networks, Protein Sci. 8, 1045-1055.
45. Muñoz, V., Cronet, P., López-Hernández, E., and Serrano, L. (1996) Analysis of the effect of local interactions on protein stability, Folding Design 1, 167-178.
46. Villegas, V., Zurdo, J., Filimonov, V. V., Aviles, F. X., Dob-son, C. M., and Serrano, L. (2000) Protein engineering as a strategy to avoid formation of amyloid fibrils, Protein Sci. 9, 1700-1708.
47. Orengo, C. A., Bray, J. E., Hubbard, T., LoConte, L., and Sillitoe, I. (1999) Analysis and assessment of ab initio three-dimensional prediction, secondary structure, and contacts prediction, Proteins 37, 149-170.
48. Karplus, K., Barrett, C., Cline, M., Diekhans, M., Grate, L., and Hughey, R. (1999) Predicting protein structure using only sequence information, Proteins S3, 121-125.
49. Cuff, J. A., Birney, E., Clamp, M. E., and Barton, G. J. (2000) ProtEST: Protein multiple sequence alignments from expressed sequence tags, Bioinformatics 16, 111-116.
50. Jennings, A. J., Edge, C. M., and Sternberg, M. J. E. An approach to improve multiple alignments of protein sequences using predicted secondary structure, Protein Eng., in press. The authors use secondary structure predictions to improve alignment accuracy. For a few examples, the method is shown to be beneficial.
51. Higgins, D. G., Thompson, J. D., and Gibson, T. J. (1996) Using CLUSTAL for multiple sequence alignments, Methods Enzymol. 266, 383-402.
52. King, R. D., and Sternberg, M. J. (1996) Identification and application of the concepts important for accurate and reliable protein secondary structure prediction, Protein Sci. 5, 2298-2310.
53. Altschul, S. F., and Gish, W. (1996) Local alignment statistics, Methods Enzymol. 266, 460-480.
54. Bairoch, A., and Apweiler, R. (2000) The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000, Nucleic Acids Res. 28, 45-48.
55. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., and Bourne, P. E. (2000) The protein data bank, Nucleic Acids Res. 28, 235-242.
56. Rost, B., and Sander, C. (1994) 1D secondary structure prediction through evolutionary profiles, in Bohr, H., and Brunak, S. (Eds.), Protein Structure by Distance Analysis, pp. 257-276, IOS Press, Amsterdam.
57. Chandonia, J. M., and Karplus, M. (1999) New methods for accurate prediction of protein secondary structure, Proteins 35, 293-306.
58. Rost, B., Eyrich, V. A., Przybylski, D., Pazos, F., Valencia, A., Fiser, A., Marti-Renom, M., Sanchez, R., and Sali, A. (2000) EVA - Evaluation of Automatic Protein Structure Prediction Services, Columbia University/Rockefeller University/CNB Madrid. World Wide Web URL: http://cubic.bioc. columbia.edu/eva.
59. Wang, Z.-X., and Yuan, Z. (2000) How good is prediction of protein structural class by the component-coupled method? Proteins 38, 165-175. The authors argue that most methods predicting secondary structural class from amino acid composition have significantly overestimated performance accuracy. They suggest that approaches based on composition alone can never reach above 60%. The method they develop is estimated at slightly above 50% accuracy.
60. Rychlewski, W. W. W. L., and Fischer, D. (2000) LiveBench: Continuous Benchmarking of Prediction Servers, IIMCB Warsaw. World Wide Web URL: http://BioInfo.PL/LiveBench/.
61. Reczko, M. (1993) Protein secondary structure prediction with partially recurrent neural networks, in First International Workshop on Neural Networks Applied to Chemistry and Environmental Sciences, Lyon, France, pp. 153-159, Gordon and Breach, New York.
62. Salamov, A. A., and Solovyev, V. V. (1997) Protein secondary structure prediction using local alignments, J. Mol. Biol. 268, 31-36.
63. Petersen, T. N., Lundegaard, C., Nielsen, M., Bohr, H., Bohr, J., Brunak, S., Gippert, G. P., and Lund, O. (2000) Prediction of protein secondary structure at 80% accuracy, Proteins 41, 17-20. The authors use divergent PSI-BLAST profiles to train and test neural networks. Novel is the particular way of averaging over many networks, as well as the amazing number of networks averaged (up to 800). The authors also replace the standard three output units (helix, strand, and other) by nine units coding for the three secondary structure states of three adjacent residues. Prediction accuracy is estimated to be higher than 77%.
64. Ouali, M., and King, R. D. (2000) Cascaded multiple classifiers for secondary structure prediction, Protein Sci. 9, 1162-1176.
65. Schmidler, S. C., Liu, J. S., and Brutlag, D. L. (2000) Bayesian segmentation of protein secondary structure, J. Comput. Biol. 7, 233-248.
66. Aurora, R., and Rose, G. D. (1998) Helix capping, Protein Sci. 7, 21-38.
67. Figureau, A., Angelica Soto, M., and Toha, J. (1999) Secondary structure of proteins and three-dimensional pattern recognition, J. Theor. Biol. 201, 103-111.
68. Przytycka, T., Aurora, R., and Rose, G. D. (1999) A protein taxonomy based on secondary structure, Nat. Struct. Biol. 6, 672-682.
69. Liu, W., and Chou, K. C. (1999) Prediction of protein secondary structure content, Protein Eng. 12, 1041-1050.
70. Zhang, C. T., and Zhang, R. (1999) Skewed distribution of protein secondary structure contents over the conformational triangle, Protein Eng. 12, 807-810.
71. Hansen, L. K., and Salamon, P. (1990) Neural network ensembles, IEEE Pattern Anal. Machine Intell. 12, 993-1001.
72. King, R. D., Ouali, M., Strong, A. T., Aly, A., Elmaghraby, A., Kantardzic, M., and Page, D. (2000) Is it better to combine predictions? Protein Eng. 13, 15-19.
73. Selbig, J., Mevissen, T., and Lengauer, T. (1999) Decision tree-based formation of consensus protein secondary structure prediction, Bioinformatics 15, 1039-1046.
74. Guermeur, Y., Geourjon, C., Gallinari, P., and Deleage, G. (1999) Improved performance in protein secondary structure prediction by inhomogeneous score combination, Bioinformatics 15, 413-421.
75. Rost, B., Sander, C., and Schneider, R. (1994) Redefining the goals of protein secondary structure prediction, J. Mol. Biol. 235, 13-26.
76. Kirshenbaum, K., Young, M., and Highsmith, S. (1999) Predicting allosteric switches in myosins, Protein Sci. 8, 1806-1815.
77. Liu, T. J., Tan, H., and Rost, B. (2000) Genomes Full of Proteins with Long Non-structured Regions? Columbia University. World Wide Web URL: http://cubic.bioc.columbia. edu/papers/.
78. Paquet, J. Y., Vinals, C., Wouters, J., Letesson, J. J., and Depiereux, E. (2000) Topology prediction of Brucella abortus Omp2b and Omp2a porins after critical assessment of transmembrane beta strands prediction by several secondary structure prediction methods, J. Biomol. Struct. Dyn. 17, 747-757.
79. Di Stasio, E., Sciandra, F., Maras, B., Di Tommaso, F., Petrucci, T. C., Giardina, B., and Brancaccio, A. (1999) Structural and functional analysis of the N-terminal extra-cellular region of beta-dystroglycan, Biochem. Biophys. Res. Commun. 266, 274-278.
80. Juan, H. F., Hung, C. C., Wang, K. T., and Chiou, S. H. (1999) Comparison of three classes of snake neurotoxins by homology modeling and computer simulation graphics, Biochem. Biophys. Res. Commun. 257, 500-510.
81. Laval, V., Chabannes, M., Carriere, M., Canut, H., Barre, A., Rouge, P., Pont-Lezica, R., and Galaud, J. (1999) A family of Arabidopsis plasma membrane receptors presenting animal beta-integrin domains, Biochimica et Biophysica Acta 1435, 61-70.
82. Seto, M. H., Liu, H. L., Zajchowski, D. A., and Whitlow, M. (1999) Protein fold analysis of the B30.2-like domain, Proteins 35, 235-249.
83. Xu, H., Aurora, R., Rose, G. D., and White, R. H. (1999) Identifying two ancient enzymes in Archaea using predicted secondary structure alignment, Nat. Struct. Biol. 6, 750-754.
84. Jackson, R. M., and Russell, R. B. (2000) The serine protease inhibitor canonical loop conformation: Examples found in extracellular hydrolases, toxins, cytokines and viral proteins, J. Mol. Biol. 296, 325-334.
85. Stawiski, E. W., Baucom, A. E., Lohr, S. C., and Gregoret, L. M. (2000) Predicting protein function from structure: Unique structural features of proteases, Proc. Natl. Acad. Sci. USA 97, 3954-3958.
86. Shah, P. S., Bizik, F., Dukor, R. K., and Qasba, P. K. (2000) Active site studies of bovine alpha1→3-galactosyltransferase and its secondary structure prediction, Biochimica et Biophysica Acta 1480, 222-234.
87. Brautigam, C., Steenbergen-Spanjers, G. C., Hoffmann, G. F., Dionisi-Vici, C., van den Heuvel, L. P., Smeitink, J. A., and Wevers, R. A. (1999) Biochemical and molecular genetic characteristics of the severe form of tyrosine hydroxylase deficiency, Clin. Chem. 45, 2073-2078.
88. Davies, G. P., Martin, I., Sturrock, S. S., Cronshaw, A., Murray, N. E., and Dryden, D. T. (1999) On the structure and operation of type I DNA restriction enzymes, J. Mol. Biol. 290, 565-579.
89. Gerloff, D. L., Cannarozzi, G. M., Joachimiak, M., Cohen, F. E., Schreiber, D., and Benner, S. A. (1999) Evolutionary, mechanistic, and predictive analyses of the hydroxymethyl-dihydropterin pyrophosphokinase family of proteins, Biochem. Biophys. Res. Commun. 254, 70-76.
90. Fischer, D., and Eisenberg, D. (1996) Fold recognition using sequence-derived properties, Protein Sci. 5, 947-955.
91. Russell, R. B., Copley, R. R., and Barton, G. J. (1996) Protein fold recognition by mapping predicted secondary structures, J. Mol. Biol. 259, 349-365.
92. Rost, B. (1995) TOPITS: Threading one-dimensional predictions into three-dimensional structures, in Rawlings, C., Clark, D., Altman, R., Hunter, L., Lengauer, T., and Wodak, S. (Eds.), Third International Conference on Intelligent Systems for Molecular Biology, Cambridge, England, pp. 314-321, AAAI Press, Menlo Park, CA.
93. De la Cruz, X., and Thornton, J. M. (1999) Factors limiting the performance of prediction-based fold recognition methods, Protein Sci. 8, 750-759.
94. Di Francesco, V., Munson, P. J., and Garnier, J. (1999) FORESST: Fold recognition from secondary structure predictions of proteins, Bioinformatics 15, 131-140.
95. Kelley, L. A., MacCallum, R. M., and Sternberg, M. J. (2000) Enhanced genome annotation using structural profiles in the program 3D-PSSM, J. Mol. Biol. 299, 499-520.
96. Ayers, D. J., Gooley, P. R., Widmer-Cooper, A., and Torda, A. E. (1999) Enhanced protein fold recognition using secondary structure information from NMR, Protein Sci. 8, 1127-1133.
97. Hargbo, J., and Elofsson, A. (1999) Hidden Markov models that use predicted secondary structures for fold recognition, Proteins 36, 68-76.
98. Jones, D. T. (1999) GenTHREADER: An efficient and reliable protein fold recognition method for genomic sequences, J. Mol. Biol. 287, 797-815.
99. Panchenko, A., Marchler-Bauer, A., and Bryant, S. H. (1999) Threading with explicit models for evolutionary conservation of structure and sequence, Proteins Suppl. 3, 133-140.
100. Ota, M., Kawabata, T., Kinjo, A. R., and Nishikawa, K. (1999) Cooperative approach for the protein fold recognition, Proteins 37, 126-132.
101. Koretke, K. K., Russell, R. B., Copley, R. R., and Lupas, A. N. (1999) Fold recognition using sequence and secondary structure information, Proteins 37, 141-148.
102. Jones, D. T., Tress, M., Bryson, K., and Hadley, C. (1999) Successful recognition of protein folds using threading methods biased by sequence similarity and predicted secondary structure, Proteins 37, 104-111.
103. Heringa, J. (1999) Two strategies for sequence comparison: Profile-preprocessed and secondary structure-induced multiple alignment, Comput. Chem. 23, 341-364. Alignment consistency is checked and alignments are improved through preprocessing the profile and using predicted secondary structure. The resulting method is shown to yield more sensitive database searches for a few examples.
104. Ng, P., Henikoff, J., and Henikoff, S. (2000) PHAT: A trans-membrane-specific substitution matrix, Bioinformatics 16, 760-766. A membrane-specific exchange matrix is collected. Then alignments of membrane proteins are refined by an iterative procedure using and improving predictions of membrane helices.
105. Baldi, P., Pollastri, G., Andersen, C. A., and Brunak, S. (2000) Matching protein beta-sheet partners by feedforward and recurrent neural networks, Ismb 8, 25-36.
106. Hubbard, T. J. P., and Park, J. (1995) Fold recognition and ab initio structure predictions using hidden Markov models and β-strand pair potentials, Proteins 23, 398-402.
107. Ortiz, A. R., Kolinski, A., Rotkiewicz, P., Ilkowski, B., and Skolnick, J. (1999) Ab initio folding of proteins using restraints derived from evolutionary information, Proteins Suppl. 3, 177-185.
108. Eyrich, V. A., Standley, D. M., Felts, A. K., and Friesner, R. A. (1999) Protein tertiary structure prediction using a branch and bound algorithm, Proteins 35, 41-57.
109. Eyrich, V. A., Standley, D. M., and Friesner, R. A. (1999) Prediction of protein tertiary structure to low resolution: Performance for a large and structurally diverse test set, J. Mol. Biol. 288, 725-742.
110. Lomize, A. L., Pogozheva, I. D., and Mosberg, H. I. (1999) Prediction of protein structure: The problem of fold multiplicity, Proteins Suppl., 199-203.
111. Chen, C. C., Singh, J. P., and Altman, R. B. (1999) Using imperfect secondary structure predictions to improve molecular structure computations, Bioinformatics 15, 53-65.
112. Samudrala, R., Xia, Y., Huang, E., and Levitt, M. (1999) Ab initio protein structure prediction using a combined hierarchical approach, Proteins Suppl., 194-198.
113. Samudrala, R., Huang, E. S., Koehl, P., and Levitt, M. (2000) Constructing side chains on near-native main chains for ab initio protein structure prediction, Protein Eng. 13, 453-457.
114. Minor, D. L. J., and Kim, P. S. (1996) Context-dependent secondary structure formation of a designed protein sequence, Nature 380, 730-734.
115. Krittanai, C., and Johnson, W. C. J. (2000) The relative order of helical propensity of amino acids changes with solvent environment, Proteins 39, 132-141.
116. Zhou, X., Alber, F., Folkers, G., Gonnet, G. H., and Chelvanayagam, G. (2000) An analysis of the helix-to-strand transition between peptides with identical sequence, Proteins 41, 248-256.
117. Pan, X. M., Niu, W. D., and Wang, Z. X. (1999) What is the minimum number of residues to determine the secondary structural state? J. Protein Chem. 18, 579-584.
118. Jacoboni, I., Martelli, P. L., Fariselli, P., Compiani, M., and Casadio, R. (2000) Predictions of protein segments with the same amino acid sequence and different secondary structure: A benchmark for predictive methods, Proteins 41, 535-544. Some stretches of up to 11 adjacent residues are known to adopt different secondary structure in different structural contexts (chameleon regions). In this original work, the authors show that, surprisingly, profile-based neural network predictions are almost as accurate for such chameleon regions as they are for regions that are never observed in alternative states.
119. Compiani, M., Fariselli, P., Martelli, P. L., and Casadio, R. (1999) Neural networks to study invariant features of protein folding, Theor. Chem. Accounts 101, 21-26. The authors successfully identify likely nucleation sites, as well as which helix forms first from secondary structure predictions.
120. Abagyan, R. A., and Batalov, S. (1997) Do aligned sequences share the same fold? J. Mol. Biol. 273, 355-368.
121. Park, J., Teichmann, S. A., Hubbard, T., and Chothia, C. (1997) Intermediate sequences increase the detection of distant sequence homologies, J. Mol. Biol. 273, 349-354.
122. Rost, B., and Sander, C. (1994) Combining evolutionary information and neural networks to predict protein secondary structure, Proteins 19, 55-72.
123. Zemla, A., Venclovas, C., Fidelis, K., and Rost, B. (1999) A modified definition of SOV, a segment-based measure for protein secondary structure prediction assessment, Proteins 34, 220-223.
124. Matthews, B. W. (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme, Biochimica et Biophysica Acta 405, 442-451.
125. Kneller, D. G., Cohen, F. E., and Langridge, R. (1990) Improvements in protein secondary structure prediction by an enhanced neural network, J. Mol. Biol. 214, 171-182.
126. Zhang, C.-T., and Chou, K.-C. (1992) An optimization approach to predicting protein structural class from amino acid composition, Protein Sci. 1, 401-408.
127. Defay, T., and Cohen, F. E. (1995) Evaluation of current techniques for ab initio protein structure prediction, Proteins 23, 431-445.
128. Cokol, M., Nair, R., and Rost, B. (2001) Finding nuclear localisation signals, EMBO Rep. 1(5), 411-415.