Prediction of protein structural class for low-similarity sequences using support vector machine and PSI-BLAST profile.
Knowledge of structural class plays an important role in understanding protein folding patterns. In this study, a simple and powerful computational method, which combines support vector machine with PSI-BLAST profile, is proposed to predict protein structural class for low-similarity sequences. The evolution information encoding in the PSI-BLAST profiles is converted into a series of fixed-length feature vectors by extracting amino acid composition and dipeptide composition from the profiles. The resulting vectors are then fed to a support vector machine classifier for the prediction of protein structural class. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark datasets, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence similarity lower than 40% and 25%, respectively. The overall accuracies attain 70.7% and 72.9% for 1189 and 25PDB datasets, respectively. Comparison of our results with other methods shows that our method is very promising to predict protein structural class particularly for low-similarity datasets and may at least play an important complementary role to existing methods.
Liu
Taigang
T
School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.
Zheng
Xiaoqi
X
Wang
Jun
J
D000596:Amino Acids; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004151:Dipeptides; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000145:classification
D000596:Amino Acids; D004151:Dipeptides; D011506:Proteins