title: Characterization and Prediction of Protein Flexibility Based on Structural Alphabets.
abstract: Motivation.To assist efforts in determining and exploring the functional properties of proteins, it is desirable to characterize and predict protein flexibilities.Results.In this study, the conformational entropy is used as an indicator of the protein flexibility. We first explore whether the conformational change can capture the protein flexibility. The well-defined decoy structures are converted into one-dimensional series of letters from a structural alphabet. Four different structure alphabets, including the secondary structure in 3-class and 8-class, the PB structure alphabet (16-letter), and the DW structure alphabet (28-letter), are investigated. The conformational entropy is then calculated from the structure alphabet letters. Some of the proteins show high correlation between the conformation entropy and the protein flexibility. We then predict the protein flexibility from basic amino acid sequence. The local structures are predicted by the dual-layer model and the conformational entropy of the predicted class distribution is then calculated. The results show that the conformational entropy is a good indicator of the protein flexibility, but false positives remain a problem. The DW structure alphabet performs the best, which means that more subtle local structures can be captured by large number of structure alphabet letters. Overall this study provides a simple and efficient method for the characterization and prediction of the protein flexibility.
authors.affiliation: Institute for Data Science and Engineering, East China Normal University, Shanghai 200062, China.
authors.forename: Qiwen
authors.identifier: 0000-0002-3166-0541
authors.initials: Q
authors.lastname: Dong
authors.affiliation: College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China.
authors.forename: Kai
authors.identifier: 0000-0003-3092-8179
authors.initials: K
authors.lastname: Wang
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Liu
authors.affiliation: College of Engineering, Shanghai Ocean University, Shanghai 201303, China.
authors.forename: Xuan
authors.identifier: 0000-0003-4598-4202
authors.initials: X
authors.lastname: Liu