DROP: An SVM domain linker predictor trained with optimal features selected by random forest

Bioinformatics

Volumn 27, Issue 4, 2011, Pages 487-494

  Ebina, Teppei ^a   Toh, Hiroyuki ^b   Kuroda, Yutaka ^a  

^a TOKYO UNIVERSITY OF AGRICULTURE AND TECHNOLOGY   (Japan)

^b NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN;

ALGORITHM; ARTICLE; BIOLOGY; METHODOLOGY; PROTEIN SECONDARY STRUCTURE; PROTEOMICS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; PROTEIN STRUCTURE, SECONDARY; PROTEINS; PROTEOMICS;

EID: 79951527638     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btq700     Document Type: Article

References (39)

1. Bateman, A. et al. (2002) The Pfam protein families database. Nucleic Acids Res., 30, 276-280.
2. Brenner, S.E. (2000) Target selection for structural genomics. Nat. Struct. Biol., 7 (Suppl.), 967-969.
3. Chikayama, E. et al. (2010) Mathematical model for empirically optimizing large scale production of soluble protein domains. BMC Bioinformatics, 11, 113.
4. Chou, P.Y. and Fasman, G.D. (1978) Prediction of the secondary structure of proteins from their amino acid sequence. Adv. Enzymol. Relat. Areas Mol. Biol., 47, 45-148.
5. Christendat, D. et al. (2000) Structural proteomics: prospects for high throughput sample preparation. Prog. Biophys. Mol. Biol., 73, 339-345.
6. Coeytaux, K. and Poupon, A. (2005) Prediction of unfolded segments in a protein sequence based on amino acid composition. Bioinformatics, 21, 1891-1900.
7. Dumontier, M. et al. (2005) Armadillo: domain boundary prediction by amino acid composition. J. Mol. Biol., 350, 1061-1073.
8. Ebina, T. et al. (2009) Loop-length-dependent SVM prediction of domain linkers for high-throughput structural proteomics. Biopolymers, 92, 1-8.
9. Ezkurdia, I. et al. (2009) Assessment of domain boundary predictions and the prediction of intramolecular contacts in CASP8. Proteins, 77 (Suppl. 9), 196-209.
10. Garbuzynskiy, S.O. et al. (2004) To be folded or to be unfolded? Protein Sci., 13, 2871-2877.
11. George, R.A. and Heringa, J. (2002) An analysis of protein domain linkers: their classification and role in protein folding. Protein Eng., 15, 871-879.
12. George, R.A. et al. (2005) Scooby-domain: prediction of globular domains in protein sequence. Nucleic Acids Res., 33, W160-W163.
13. Hirose, S. et al. (2007) POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions. Bioinformatics, 23, 2046-2053.
14. Hondoh, T. et al. (2006) Computer-aided NMR assay for detecting natively folded structural domains. Protein Sci., 15, 871-883.
15. Hulo, N. et al. (2004) Recent improvements to the PROSITE database. Nucleic Acids Res., 32, D134-D137.
16. Joachims, T. (1999) Making large-Scale SVM learning practical. In Schölkopf, B. et al. (eds) Advances in Kernel Methods - Support Vector Learning. The MIT-Press, Cambridge, MA.
17. Jones, D.T. (1999) Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol., 292, 195-202.
18. Jones, S. et al. (1998) Domain assignment for protein structures using a consensus approach: characterization and analysis. Protein Sci., 7, 233-242.
19. Joshi, R.R. (2007) A decade of computing to traverse the labyrinth of protein domains. Curr. Bioinfo., 2, 113.
20. Kawashima, S. et al. (2008) AAindex: amino acid index database, progress report 2008. Nucleic Acids Res., 36, D202-D205.
21. Kernytsky, A. and Rost, B. (2009) Using genetic algorithms to select most predictive protein features. Proteins, 75, 75-88.
22. Kuroda, Y. et al. (2000) Automated search of natively folded protein fragments for high-throughput structure determination in structural genomics. Protein Sci., 9, 2313-2321.
23. Liaw, A. andWiener, M. (2002) Classification and regression by randomForest. R news, 2, 18-22.
24. Liu, J. and Rost, B. (2004) Sequence-based prediction of protein domains. Nucleic Acids Res., 32, 3522-3530.
25. Miyazaki, S. et al. (2002) Characterization and prediction of linker sequences of multidomain proteins by a neural network. J. Struct. Funct. Genomics, 2, 37-51.
26. Miyazaki, S. et al. (2006) Identification of putative domain linkers by a neural network - application to a large sequence database. BMC Bioinformatics, 7, 323.
27. Nagarajan, N. and Yona, G. (2004) Automatic prediction of protein domains from sequence information using a hybrid learning system. Bioinformatics, 20, 1335-1360.
28. Saeys, Y. et al. (2007) A review of feature selection techniques in bioinformatics. Bioinformatics, 23, 2507-2517.
29. Shenkin, P.S. et al. (1991) Information-theoretical entropy as a measure of sequence variability. Proteins, 11, 297-313.
30. Sim, J. et al. (2005) PPRODO: prediction of protein domain boundaries using neural networks. Proteins, 59, 627-632.
31. Suyama, M. and Ohara, O. (2003) DomCut: prediction of inter-domain linker regions in amino acid sequences. Bioinformatics, 19, 673-674.
32. Tai, C.H. et al. (2005) Evaluation of domain prediction in CASP6. Proteins, 61 (Suppl. 7), 183-192.
33. Tanaka, T. et al. (2003) Characteristics and prediction of domain linker sequences in multi-domain proteins. J. Struct. Funct. Genomics, 4, 79-85.
34. Tanaka, T. et al. (2006) Improvement of domain linker prediction by incorporating looplength-dependent characteristics. Biopolymers, 84, 161-168.
35. Udwary, D.W. et al. (2002) A method for prediction of the locations of linker regions within large multifunctional proteins, and application to a type I polyketide synthase. J. Mol. Biol., 323, 585-598.
36. Yeats, C. et al. (2010) A fast and automated solution for accurately resolving protein domain architectures. Bioinformatics, 26, 745-751.
37. Ye, L. et al. (2008) Sequence-based protein domain boundary prediction using BP neural network with various property profiles. Proteins, 71, 300-307.
38. Yokoyama, S. et al. (2000) Structural genomics projects in Japan. Nat. Struct. Biol., 7 (Suppl.), 943-945.
39. Zhang, Y. (2009) I-TASSER: fully automated protein structure prediction in CASP8. Proteins, 77 (Suppl. 9), 100-113.