TRANSMEMBRANE STRUCTURE PREDICTIONS WITH HYDROPATHY INDEX/CHARGE TWO-DIMENSIONAL TRAJECTORIES OF STOCHASTIC DYNAMICAL SYSTEMS
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説明
<jats:p> A novel algorithm is proposed for predicting transmembrane protein secondary structure from two-dimensional vector trajectories consisting of a hydropathy index and formal charge of a test amino acid sequence using stochastic dynamical system models. Two prediction problems are discussed. One is the prediction of transmembrane region counts; another is that of transmembrane regions, i.e. predicting whether or not each amino acid belongs to a transmembrane region. The prediction accuracies, using a collection of well-characterized transmembrane protein sequences and benchmarking sequences, suggest that the proposed algorithm performs reasonably well. An experiment was performed with a glutamate transporter homologue from Pyrococcus horikoshii. The predicted transmembrane regions of the five human glutamate transporter sequences and observations based on the computed likelihood are reported. </jats:p>
収録刊行物
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- Journal of Bioinformatics and Computational Biology
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Journal of Bioinformatics and Computational Biology 05 669-692, 2007-06-01
World Scientific Pub Co Pte Lt
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キーワード
- Models, Molecular
- Likelihood Functions
- Stochastic Processes
- Amino Acid Transport System X-AG
- Archaeal Proteins
- Molecular Sequence Data
- Computational Biology
- Membrane Proteins
- Protein Structure, Secondary
- Humans
- Amino Acid Sequence
- Neural Networks, Computer
- Pyrococcus horikoshii
- Databases, Protein
- Algorithms