Clustering of amino acids for protein secondary structure prediction.
Simple hidden Markov models are proposed for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of length distribution, and focus on inclusion of short range correlations of residues and of conformation states in the models. Conformation-independent and -dependent amino acid coarse-graining schemes are designed for the models by means of proper mutual information. We compare models of different level of complexity, and establish a practical model with a high prediction accuracy.
Zheng
Wei-Mou
WM
Institute of Theoretical Physics, Academia Sinica, Beijing 100080, China.
D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D016000:Cluster Analysis; D008390:Markov Chains; D008958:Models, Molecular; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D000596:Amino Acids; D011506:Proteins