HYPROSP: A hybrid protein secondary structure prediction algorithm-a knowledge-based approach

Nucleic Acids Research

Volumn 32, Issue 17, 2004, Pages 5059-5065

  Wu, Kuen Pin ^a   Lin, Hsin Nan ^a   Chang, Jia Ming ^a   Sung, Ting Yi ^a   Hsu, Wen Lian ^a  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

HYBRID PROTEIN; PEPTIDE FRAGMENT;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; BIOINFORMATICS; COMPUTER PROGRAM; CONTROLLED STUDY; CORRELATION FUNCTION; GENETIC ALGORITHM; INFORMATION PROCESSING; INTERMETHOD COMPARISON; PREDICTION; PRIORITY JOURNAL; PROTEIN SECONDARY STRUCTURE; QUANTITATIVE ANALYSIS; ALGORITHM; ARTIFICIAL INTELLIGENCE; CHEMISTRY; EVALUATION; INTERNET; REPRODUCIBILITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; INTERNET; PEPTIDE FRAGMENTS; PROTEIN STRUCTURE, SECONDARY; REPRODUCIBILITY OF RESULTS;

EID: 4644354297     PISSN: 03051048     EISSN: None     Source Type: Journal    
DOI: 10.1093/nar/gkh836     Document Type: Article

References (17)

1. McGuffin,L.J. and Jones,D.T. (2003) Benchmarking secondary structure prediction for fold recognition. Proteins, 52, 166-175.
2. Chandonia,J.M. and Karplus,M. (1999) New methods for accurate prediction of protein secondary structure. Proteins, 35, 293-306.
3. Cuff,J.A. and Barton,G.J. (1999) Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins, 34, 508-519.
4. Cuff,J.A. and Barton,G.J. (1999) Application of enhanced multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins, 40, 502-511.
5. 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.
6. Hua,S. and Sun,Z. (2001) A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J. Mol. Biol., 308, 397-407.
7. Jones,D.T. (1999) Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol., 292 195-202.
8. Karplus,K., Barrett,C., Cline,M., Diekhans,M., Grate,L. and Hughey,R. (1999) Predicting protein structure using only sequence information. Proteins, 3(Suppl.), 121-125.
9. Kim,H. and Park,H. (2003) Protein secondary structure prediction by support vector machines and position-specific scoring matrices. Protein Eng., 16, 553-560.
10. McGuffin,L.J., Bryson,K. and Jones,D.T. (2000) The PSIPRED protein structure prediction server. Bioinformatics, 16, 404-405.
11. Qian,N. and Sejnowski,T.J. (1988) Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol., 202, 865-884.
12. Riis,S.K. and Krogh,A. (1996) Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments. J. Comput. Biol., 3, 163-183.
13. Rost,B. and Sander,C. (1993) Prediction of secondary structure at better than 70% accuracy. J. Mol. Biol., 232, 584-599.
14. Rost,B. and Sander,C. (1994) Combining evolutionary information and neural networks to predict protein secondary structure. Proteins, 19, 55-72.
15. Rost,B. (2001) Review: protein secondary structure prediction continues to rise. J. Struct. Biol., 134, 204-218.
16. Frishman,D. and Argos,P. (1997) Seventy-five percent accuracy in protein secondary structure prediction. Proteins, 27, 329-335.
17. Altschul,S.F., Madden,T.L., Schaffer,A.A., Zhang,J., Zhang,Z., Miller,W. and Lipman,D.J. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389-3402.