Recognition of beta-hairpin motifs in proteins by using the composite vector.

ABSTRACT

A composite vector method for predicting beta-hairpin motifs in proteins is proposed by combining the score of matrix, increment of diversity, the value of distance and auto-correlation information to express the information of sequence. The prediction is based on analysis of data from 3,088 non-homologous protein chains including 6,035 beta-hairpin motifs and 2,738 non-beta-hairpin motifs. The overall accuracy of prediction and Matthew's correlation coefficient are 83.1% and 0.59, respectively. In addition, by using the same methods, the accuracy of 80.7% and Matthew's correlation coefficient of 0.61 are obtained for other dataset with 2,878 non-homologous protein chains, which contains 4,884 beta-hairpin motifs and 4,310 non-beta-hairpin motifs. Better results are also obtained in the prediction of the beta-hairpin motifs of proteins by analysis of the CASP6 dataset.