Recognition of beta-hairpin motifs in proteins by using the composite vector.
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.
Hu
Xiu-Zhen
XZ
College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China.
Li
Qian-Zhong
QZ
Wang
Chun-Lian
CL
D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry / Q000145:classification; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D016384:Consensus Sequence; D030562:Databases, Protein; D004151:Dipeptides / Q000737:chemistry; D006801:Humans; D008958:Models, Molecular; D020543:Proteome / Q000737:chemistry*; D040901:Proteomics / Q000379:methods; D012984:Software; D013499:Surface Properties
D000596:Amino Acids; D004151:Dipeptides; D020543:Proteome