Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

Comparison study on statistical features of predicted secondary structures for protein structural class prediction: From content to position

Qi Dai1*, Yan Li1, Xiaoqing Liu2, Yuhua Yao1*, Yunjie Cao1 and Pingan He3

Author affiliations

1 College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China

2 College of Science, Hangzhou Dianzi University, Hangzhou, 310018, China

3 College of Science, Zhejiang Sci-Tech University, Hangzhou, 310018, China

For all author emails, please log on.

Citation and License

BMC Bioinformatics 2013, 14:152  doi:10.1186/1471-2105-14-152

Published: 4 May 2013

Abstract

Background

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.

Results

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.

Conclusions

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.