Comparison study on statistical features of predicted secondary structures for protein structural class prediction: From content to position.
Comparison study on statistical features of predicted secondary structures for protein structural class prediction: From content to position.
复制标题
蛋白质结构类别预测的预测二级结构统计特征的比较研究：从内容到位置
DOI：
10.1186/1471-2105-14-152
复制
发表时间：
2013-05-04
影响因子：
3
通讯作者：
He P
中科院分区：
文献类型：
--
作者：
Dai Q;Li Y;Liu X;Yao Y;Cao Y;He P
许多基于内容的二级结构元素统计特征（CBF-PSSE）已被提出并在蛋白质结构类预测中取得了很好的效果，但迄今为止还没有使用预测二级结构序列中元素连续出现的位置分布。为了进行预测，需要提取一些合适的二级结构元素的位置特征。我们提出了一些基于位置的预测二级结构元件（PBF-PSSE）的特征，并评估了它们相对于现有CBF-PSSE的内在能力，这不仅为这些统计特征提供了系统和定量的实验评估，而且自然地补充了CBF-PSSE的现有比较。我们还分析了CBF-PSSE与PBF-PSSE相结合的性能，并进一步构建了一个新的组合特征集PBF 11 CBF-PSSE。基于这些实验，获得了PBF-PSSE和CBF-PSSE的新的有价值的使用指南。PBF-PSSE和CBF-PSSE对蛋白质结构类预测具有令人信服的影响。当与PBF-PSSE相结合时，大多数CBF-PSSE的预测精度得到了很大的提高，因此PBF-PSSE和CBF-PSSE必须紧密合作，才能对蛋白质结构类预测做出重要和互补的贡献。此外，所提出的PBF-PSSE的性能是非常敏感的参数k的选择。总之，我们的定量分析证明，探索预测的二级结构元件的位置信息是一个有前途的方法，以提高蛋白质结构类预测的能力。
Many content-based statistical features of secondary structural elements (CBF-PSSEs) have been proposed and achieved promising results in protein structural class prediction, but until now position distribution of the successive occurrences of an element in predicted secondary structure sequences hasn’t been used. It is necessary to extract some appropriate position-based features of the secondary structural elements for prediction task. We proposed some position-based features of predicted secondary structural elements (PBF-PSSEs) and assessed their intrinsic ability relative to the available CBF-PSSEs, which not only offers a systematic and quantitative experimental assessment of these statistical features, but also naturally complements the available comparison of the CBF-PSSEs. We also analyzed the performance of the CBF-PSSEs combined with the PBF-PSSE and further constructed a new combined feature set, PBF11CBF-PSSE. Based on these experiments, novel valuable guidelines for the use of PBF-PSSEs and CBF-PSSEs were obtained. PBF-PSSEs and CBF-PSSEs have a compelling impact on protein structural class prediction. When combining with the PBF-PSSE, most of the CBF-PSSEs get a great improvement over the prediction accuracies, so the PBF-PSSEs and the CBF-PSSEs have to work closely so as to make significant and complementary contributions to protein structural class prediction. Besides, the proposed PBF-PSSE’s performance is extremely sensitive to the choice of parameter k. In summary, our quantitative analysis verifies that exploring the position information of predicted secondary structural elements is a promising way to improve the abilities of protein structural class prediction.
登录
查看更多内容
影响因子：
3
作者：
Kurgan L;Cios K;Chen K
通讯作者：
Chen K
影响因子：
3
作者：
Ferragina P;Giancarlo R;Greco V;Manzini G;Valiente G
通讯作者：
Valiente G
螺旋和链末端周围的位置特异性残基偏好特征以及预测二级结构的新策略
影响因子：
8
作者：
Duan, Mojie;Huang, Min;Zhou, Yanhong
通讯作者：
Zhou, Yanhong
影响因子：
5.8
作者：
Birzele, Fabian;Kramer, Stefan
通讯作者：
Kramer, Stefan
影响因子：
14.9
作者：
Andreeva, A;Howorth, D;Murzin, AG
通讯作者：
Murzin, AG