title: Prediction of complex super-secondary structure βαβ motifs based on combined features.
abstract: Prediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs. Therefore, the accurate prediction of βαβ motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the βαβ motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on βαβ motifs and non-βαβ motifs. The target motif was selected, and the length of the loop-α-loop varies from 10 to 26 amino acids. The ideal fixed-length pattern comprised 32 amino acids. A Support Vector Machine algorithm was developed for predicting βαβ motifs by using the sequence information, the predicted structure and function information to express the sequence feature. The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively. The Matthew's correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively. Results demonstrate that the proposed method is an effective approach for predicting βαβ motifs and can be used for structure and function studies of proteins.
authors: College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China.
authors: Lixia
authors: L
authors: Sun
authors: College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China.
authors: Xiuzhen
authors: X
authors: Hu
authors: College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China.
authors: Shaobo
authors: S
authors: Li
authors: College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China.
authors: Zhuo
authors: Z
authors: Jiang
authors: College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China.
authors: Kun
authors: K
authors: Li
keywords: Combined features; Complex super-secondary structure; Structure prediction; Support Vector Machine