Chen Chao, Tian Yuan-Xin, Zou Xiao-Yong, Cai Pei-Xiang, Mo Jin-Yuan
School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
J Theor Biol. 2006 Dec 7;243(3):444-8. doi: 10.1016/j.jtbi.2006.06.025. Epub 2006 Jul 1.
As a result of genome and other sequencing projects, the gap between the number of known protein sequences and the number of known protein structural classes is widening rapidly. In order to narrow this gap, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a novel predictor is developed for predicting protein structural class. It is featured by employing a support vector machine learning system and using a different pseudo-amino acid composition (PseAA), which was introduced to, to some extent, take into account the sequence-order effects to represent protein samples. As a demonstration, the jackknife cross-validation test was performed on a working dataset that contains 204 non-homologous proteins. The predicted results are very encouraging, indicating that the current predictor featured with the PseAA may play an important complementary role to the elegant covariant discriminant predictor and other existing algorithms.
由于基因组及其他测序项目，已知蛋白质序列数量与已知蛋白质结构类别的数量之间的差距正在迅速扩大。为了缩小这一差距，开发一种用于快速准确确定蛋白质结构类别的计算预测方法至关重要。本文开发了一种用于预测蛋白质结构类别的新型预测器。其特点是采用支持向量机学习系统，并使用一种不同的伪氨基酸组成（PseAA），在一定程度上引入该组成是为了考虑序列顺序效应来表征蛋白质样本。作为一个示例，在一个包含204个非同源蛋白质的工作数据集上进行了留一法交叉验证测试。预测结果非常令人鼓舞，表明当前具有PseAA特征的预测器可能对优雅的协变判别预测器和其他现有算法起到重要的补充作用。
