A protein structural classes prediction method based on predicted secondary structure and PSI-BLAST profile.
Knowledge of protein secondary structural classes plays an important role in understanding protein folding patterns. In this paper, 25 features based on position-specific scoring matrices are selected to reflect evolutionary information. In combination with other 11 rational features based on predicted protein secondary structure sequences proposed by the previous researchers, a 36-dimensional representation feature vector is presented to predict protein secondary structural classes for low-similarity sequences. ASTRALtraining dataset is used to train and design our method, other three low-similarity datasets ASTRALtest, 25PDB and 1189 are used to test the proposed method. Comparisons with other methods show that our method is effective to predict protein secondary structural classes. Stand alone version of the proposed method (PSSS-PSSM) is written in MATLAB language and it can be downloaded from http://letsgob.com/bioinfo_PSSS_PSSM/.
Department of Sciences, Dalian Nationalities University, Dalian, Liaoning 116600, PR China. Electronic address: sunnyday1979@163.com.
Shuyan
S
Ding
College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, PR China.
Yan
Y
Li
College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, PR China.
Zhuoxing
Z
Shi
College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, PR China.
Shoujiang
S
Yan
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D030562:Databases, Protein; D008969:Molecular Sequence Data; D056510:Position-Specific Scoring Matrices; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry / Q000145:classification; D016415:Sequence Alignment; D012984:Software
D011506:Proteins
Feature selection; Position-specific scoring matrix; Support vector machine