Prediction of protein secondary structure using feature selection and analysis approach.
The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80% currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25% was used to train and test the proposed method. The results indicate that overall accuracy of 87.8% was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89% at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures.
College of Science, Inner Mongolia Agriculture University, Hohhot, 010018, People's Republic of China, fengyonge@163.com.
Yonge
Y
Feng
Hao
H
Lin
Liaofu
L
Luo
D000465:Algorithms; D030562:Databases, Protein; D006801:Humans; D008958:Models, Molecular; D010446:Peptide Fragments / Q000737:chemistry*; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein
D010446:Peptide Fragments; D011506:Proteins