Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

BMC Bioinformatics

Volumn 7, Issue , 2006, Pages

  Bodén, Mikael ^a   Yuan, Zheng ^a   Bailey, Timothy L ^a  

^a UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; CONFORMATIONAL STATE; INHERENT FLEXIBILITY; PREDICTION METHODS; PROBABILISTIC MACHINES; PROBABILISTIC MODELS; PROBABILISTIC NEURAL NETWORKS; SECONDARY STRUCTURES;

FORECASTING; LEARNING SYSTEMS; NEURAL NETWORKS;

PROTEINS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CONTROLLED STUDY; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEARNING; MOLECULAR MODEL; NUCLEAR MAGNETIC RESONANCE; PREDICTION; PROBABILITY; PROCESS DEVELOPMENT; PROCESS MODEL; PROTEIN CONFORMATION; PROTEIN SECONDARY STRUCTURE; TRAINING; ALGORITHM; CHEMICAL MODEL; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER SIMULATION; CRYSTALLOGRAPHY; EVALUATION STUDY; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PROCEDURES; SEQUENCE ANALYSIS; STATISTICAL MODEL; ULTRASTRUCTURE;

PROTEIN;

ALGORITHMS; COMPUTER SIMULATION; CRYSTALLOGRAPHY; MAGNETIC RESONANCE SPECTROSCOPY; MODELS, CHEMICAL; MODELS, MOLECULAR; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PROTEIN STRUCTURE, SECONDARY; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 33644655386     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-68     Document Type: Article

References (28)

1. Kabsch W, Sander C: Dictionary of protein secondary structure: Pattern recognition of hydrogen bonded and geometrical features. Biopolymers 1983, 22:2577-2637.
2. Andersen CAF, Palmer AG, Brunak S, Rost B: Continuum secondary structure captures protein flexibility. Structure 2002, 10:175-184.
3. Carter P, Andersen CAF, Rost B: DSSP cont: continuous secondary structure assignments for proteins. Nucleic Acids Research 2003, 31(13):3293-3295.
4. Jones DT: Protein secondary structure prediction based on position-specific scoring matrices. Journal of Molecular Biology 1999, 292:195-202.
5. Pollastri G, Przybylski D, Rost B, Baldi P: Improving the Prediction of Protein Secondary Strucure in Three and Eight Classes Using Recurrent Neural Networks and Profiles. Proteins: Structure, Function, and Genetics 2002, 47:228-235.
6. Nordahl-Petersen T, Lundegaard C, Nielsen M, Bohr H, Bohr J, Brunak S, Gippert GP, Lund O: Prediction of protein secondary structure at 80% accuracy. Proteins: Structure, Function and Genetics 2000, 41:17-20.
7. Rost B: Protein Secondary Structure Prediction Continues to Rise. Journal of Structural Biology 2001, 134(2-3):204-218.
8. Hua S, Sun Z: A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. Journal of Molecular Biology 2001. 308(2):397-407.
9. Ward JJ, McGuffin LJ, Buxton BF, Jones DT: Secondary structure prediction with support vector machines. Bioinformatics 2003, 19(13):1650-1655.
10. Solis AD, Rackovsky S: On the use of secondary structure in protein structure prediction: a bioinformatic analysis. Polymer 2004, 45(2):525-546.
11. Guermeur Y, Pollastri G, Elisseeff A, Zelus D, Paugam-Moisy H, Baldi P: Combining protein secondary structure prediction models with ensemble methods of optimal complexity. Neurocomputing 2004, 56:305-327.
12. Frishman D, Argos P: Knowledge-based protein secondary structure assignment. Proteins: Structure, Function, and Genetics 1995, 23(4):566-579.
13. Kihara D: The effect of long-range interactions on the secondary structure formation of proteins. Protein Sci 2005, 14(8):1955-1963.
14. Domingos P, Pazzani M: On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 1997, 29(2-3):103-130.
15. Rost B, Sander C: Prediction of Protein Secondary Structure at Better than 70% Accuracy. Journal of Molecular Biology 1993, 232:584-599.
16. Zemla A, Venclovas C, Fidelis K, Rost B: A modified definition of SOV, a segment-based measure for protein secondary structure prediction assessment. Proteins 1999, 34:220-223.
17. Eyrich VA, Przybylski D, Koh IYY, Grana O, Pazos F, Valencia A, Rost B: CAFASP3 in the spotlight of EVA. Proteins: Structure, Function, and Genetics 2003, 53:548-560.
18. Young M, Kirshenbaum K, Dill K, Highsmith S: Predicting conformational switches in proteins. Protein Sci 1999, 8(9):1752-1764.
19. Yuan Z, Bailey TL, Teasdale R: Prediction of protein B-factor profiles. Proteins: Structure, Function, and Bioinformatics 2005, 58(4):905-912.
20. John GH, Langley P: Estimating continuous distributions in Bayesian classifiers. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence San Mateo: Morgan Kaufmann Publishers; 1995.
21. Jordan MI, Bishop C: Neural networks. In CRC Handbook of Computer Science Edited by: Tucker AB. Boca Raton, FL: CRC Press; 1997.
22. Holm L, Sander C: Touring protein fold space with Dali/FSSP. Nucleic Acids Research 1998, 26:318-321.
23. Hobohm U, Scharf M, Schneider R, Sander C: Selection of representative protein data sets. Protein Science 1992, 1:409-417.
24. Thompson J, Higgins D, Gibson T: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research 1994, 2:4673-4680.
25. Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov I, Bourne P: The Protein Data Bank. Nucleic Acids Research 2000, 28:235-242.
26. DSSPcont [http://cubic.bioc.columbia.edu/services/DSSPcont]
27. Baldi P, Brunak S, Chauvin Y, Andersen CAF, Nielsen H: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000, 16(5):412-424.
28. Durbin R, Eddy SR, Krogh A, Mitchison G: Biological Sequence Analysis Cambridge, England: Cambridge University Press; 1998.