Hyplosp: A knowledge-based approach to protien local structure prediction

Journal of Bioinformatics and Computational Biology

Volumn 4, Issue 6, 2006, Pages 1287-1307

  Chen, Ching Tai ^a   Lin, Hsin Nan ^a   Sung, Ting Yi ^a   Hsu, Wen Lian ^a  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

Author keywords

Knowledge based prediction method; Local structure prediction; Neural networks; Secondary structure; Structural alphabet

Indexed keywords

ACCURACY; ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOINFORMATICS; INTERMETHOD COMPARISON; KNOWLEDGE BASE; PREDICTION; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; VALIDATION PROCESS;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; MODELS, CHEMICAL; MODELS, MOLECULAR; MOLECULAR SEQUENCE DATA; PATTERN RECOGNITION, AUTOMATED; PROTEINS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SEQUENCE HOMOLOGY, AMINO ACID; SOFTWARE;

EID: 33846525649     PISSN: 02197200     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219720006002466     Document Type: Article

References (27)

1. Karchin R, Cline M, Karplus K, Evaluation of local structure alphabets based on residue burial, Proteins 55:508-518, 2004.
2. Karchin R, Cline M, Mandel-Gutfreund Y, Karplus K, Hidden Markov models that use predicted local structure for fold recognition: Alphabets of backbone geometry, Proteins 51:504-514, 2003.
3. Bystroff C, Shao Y, Fully automated ab initio protein structure prediction using I-Sites, HMMSTR and Rosetta, Bioinformatics 18:54-61, 2002.
4. Karplus K, Karchin R, Draper J, Casper J, Mandel-Gutfreund Y, Diekhans M, Hughey R, Combining local-structure, fold-recognition, and new fold methods for protein structure prediction, Proteins 53:491-496, 2003.
5. Simons KT, Kooperberg C, Huang E, Baker D, Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions, J. Mol. Biol. 268:209-225, 1997.
6. Unger R, Harel D, Wherland S, Sussman JL, A 3D building blocks approach to analyzing and predicting structure of proteins, Proteins 5:355-373, 1989.
7. Micheletti C, Seno F, Maritan A, Recurrent Oligomers in proteins: An optimal scheme reconciling accurate and concise backbone representations in automated folding and design studies, Proteins 40:662-674, 2000.
8. Schuchhardt J, Schneider G, Reichelt J, Schomburg D, Wrede P, Local structural motifs of protein backbones are classified by self-organizing neural networks, Protein Eng 9:833-842, 1996.
9. Kolodny R, Koehl P, Guibas L, Levitt M, Small libraries of protein fragments model native protein structures accurately, J Mol Biol 323:297-307 2002.
10. Bystroff C, Baker D, Prediction of local structure in proteins using a library of sequence-structure motifs, J Mol Biol 281:565-577, 1998.
11. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ, Gapped BLAST and PSI-BLAST: A new generation of protein database search programs, Nucleic Acids Res 25(17):3389-3402, 1997.
12. Bystroff C, Thorsson V, Baker D, HMMSTR: A hidden Markov model for local sequence-structure correlations in proteins, J Mol Biol 301:173-190, 2000.
13. de Brevern AG, Etchebest C, Hazout S, Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks, Proteins 41:271-287, 2000.
14. Hughey R, Krogh A, SAM: Sequence alignment and modeling software system, version3. Technical report UCSC-CRL-95-7. Santa Cruz, CA: University of California, Santa Cruz, Computer Engineering, 1995.
15. Hughey R, Karplus K, Krogh A, SAM: Sequence alignment and modeling software system, version 3. Technical report UCSC-CRL-99-11. Santa Cruz, CA: University of California, Santa Cruz, Computer Engineering, 1999. Available from http://www.soe.ucsc.edu/rsearch/compbio/sam.html
16. Yang AS, Wang LY, Local structure prediction with local structure-based sequence profiles, Bioinformatics 19(10):1267-1274, 2003.
17. Kuang R, Leslie CS, Yang AS, Protein backbone angle prediction with machine learning approaches, Bioinformatics 20:1612-1621, 2004.
18. Tang T, Xu J, Li M, Discovering sequence-structure motifs from protein segments and two applications, Pacific Symposium on Biocomputing, 2005.
19. Kabsch W, Sander C, Definition of secondary structure of proteins given a set of 3D coordinates, Biopolymers 22:2577-2637 1983.
20. Pruitt KD, Tatusova T, Maglott DR, NCBI reference sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts, and proteins, Nucleic Acids Res 33:501-504, 2005.
21. Rumelhart D, Hinton G, Williams R, Learning internal representations by error propagation, In Neurocomputing, MIT Press, Cambridge, MA, pp. 675-695, 1988.
22. Lin HN, Chang JM, Wu KP, Sung TY, Hsu WL, A knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence, Bioinformatics 21:3227-3233, 2005.
23. de Brevern AG, Benros C, Gautier R, Valadié H, Hazout H, Etchebest C, Local backbone structure prediction of proteins, In silico Biology 4(3):381-386, 2004.
24. Rost B, Eyrich VA, EVA: Large-scale analysis of secondary structure prediction, Proteins 5:192-199, 2001.
25. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE, The Protein Data Bank, Nucleic Acids Res 28:235-242, 2000.
26. Rost B, Sander C, Prediction of protein secondary structure at better than 70% accuracy, J Mol Biol 232:584-599 1993.
27. Jones DT, Protein secondary structure prediction based on position-specific scoring matrices, J Mol Biol 292:195-202, 1999.