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Updated: Jun 23, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Recognition of beta-hairpin motifs in proteins by using the composite vector
Xiu-Zhen Hu1, Qian-Zhong Li, Chun-Lian Wang
1College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China.
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.
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