Prediction of plasma membrane spanning region and topology using hidden markov model and neural network

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3215, Issue , 2004, Pages 270-277

  Kim, Min Kyung ^a   Park, Hyun Seok ^b   Park, Seon Hee ^c  

^a Ewha Womans University   (South Korea)

^b Department of Computer Science and Engineering ^*  (South Korea)

^c Electronics and Telecommunications Research Institute (ETRI)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CELL MEMBRANES; FORECASTING; HIDDEN MARKOV MODELS; KNOWLEDGE BASED SYSTEMS; PEPTIDES; SIGNAL DETECTION;

INTRACELLULAR COMPARTMENTS; INTRACELLULAR MEMBRANES; INTRACELLULAR TARGETING; MEMBRANE SPANNING REGIONS; PREDICTION PERFORMANCE; TOPOLOGY PREDICTIONS; TRANSMEMBRANE REGIONS; TRANSMEMBRANE TOPOLOGY;

TOPOLOGY;

EID: 84975487245     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30134-9_37     Document Type: Conference Paper

References (14)

1. Krogh, A., Larsson, B., von Heijne, G., Sonnhammer, E. L.: Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes. J. Mol. Biol. Vol. 305 (2001) 567-580
2. Tusnady, G. E., Simon, I.: The HMMTOP transmembrane topology prediction server. Bioinformatics. Vol. 17 (2001) 849-850
3. Moller, S., Croning, M. D., Apweiler, R.: Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics. Vol. 17 (2001) 646-653
4. Nielsen, H., Engelbrecht J, Brunak S, von Heijne G. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng. Vol. 10 (1997) 1-6
5. Emanuelsson, O., Nielsen, H., Brunak S., von Heijne, G.: Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol. Vol. 300 (2000) 1005-1016
6. Reinhardt A, Hubbard T.: Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res. Vol. 26 (1998) 2230-2236
7. Park, K. J., Kanehisa, M.: Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics. Vol. 19 (2003) 1656-1663
8. Nakashima H, Nishikawa K.: Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol. Vol. 238 (1994) 54-61
9. Lao DM, Arai M, Ikeda M, Shimizu T.: The presence of signal peptide significantly affects transmembrane topology prediction. Bioinformatics. Vol. 18 (2002) 1562-1566
10. Moller, S., Kriventseva, E. V., Apweiler, R. A collection of well characterised integral membrane proteins. Bioinformatics. Vol. 16 (2000) 1159-1160
11. Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M. C., Estreicher, A., Gasteiger, E., Martin, M. J., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S., Schneider, M.: The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. Vol. 31 (2003) 365-370.
12. Ikeda, M., Arai, M., Okuno, T., Shimizu, T., TMPDB: a database of experimentally characterized trans membrane topologies. Nucleic Acids Res. Vol. 31 (2003) 406-409
13. Nilsson, J., Persson, B., von Heijne, G.: Consensus predictions of membrane protein topology. FEBS Lett. Vol. 486 (2000) 267-269
14. Martelli, P. L., Fariselli, P., Krogh, A., Casadio, R.: A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins. Bioinformatics. Suppl 1 (2002) S46-53.