Using factor analysis scales of generalized amino acid information for prediction and characteristic analysis of beta-turns in proteins based on a support vector machine model

作者：Liang GuiZhao^*; Zhao Wei

来源：Science China Chemistry, 2010, 53(5): 1161-1167.

DOI：10.1007/s11426-010-0165-1

• beta-turns
• factor analysis scales of generalized amino acid information
• support vector machine

This paper offers a new combined approach to predict and characterize beta-turns in proteins. The approach includes two key steps, i.e., how to represent the features of beta-turns and how to develop a predictor. The first step is to use factor analysis scales of generalized amino acid information (FASGAI), involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, to represent the features of beta-turns in proteins. The second step is to construct a support vector machine (SVM) predictor of beta-turns based on 426 training proteins by a sevenfold cross validation test. The SVM predictor thus predicted beta-turns on 547 and 823 proteins by an external validation test, separately. Our results are compared with the previously best known beta-turn prediction methods and are shown to give comparative performance. Most significantly, the SVM model provides some information related to beta-turn residues in proteins. The results demonstrate that the present combination approach may be used in the prediction of protein structures.

• 出版日期2010-5
• 单位重庆大学