College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China.
Amino Acids. 2010 Mar;38(3):915-21. doi: 10.1007/s00726-009-0299-7. Epub 2009 May 6.
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.
提出了一种组合向量方法，用于预测蛋白质中的β发夹基序，该方法将矩阵得分、多样性增量、距离值和自相关信息结合起来，以表达序列信息。预测是基于对包括 6035 个β发夹基序和 2738 个非-β发夹基序的 3088 个非同源蛋白质链的数据分析。预测的整体准确性和马修相关系数分别为 83.1%和 0.59。此外，通过使用相同的方法，对于包含 4884 个β发夹基序和 4310 个非-β发夹基序的另一个包含 2878 个非同源蛋白质链的数据集，获得了 80.7%的准确性和 0.61 的马修相关系数。通过分析 CASP6 数据集，在蛋白质的β发夹基序预测方面也获得了更好的结果。
