title: Comparison study on statistical features of predicted secondary structures for protein structural class prediction: From content to position.
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.
authors: College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China. daiailiu2004@yahoo.com.cn
authors: Qi
authors: Q
authors: Dai
authors: Yan
authors: Y
authors: Li
authors: Xiaoqing
authors: X
authors: Liu
authors: Yuhua
authors: Y
authors: Yao
authors: Yunjie
authors: Y
authors: Cao
authors: Pingan
authors: P
authors: He
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D008969:Molecular Sequence Data; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D017386:Sequence Homology, Amino Acid; D060388:Support Vector Machine
chemical_list: D011506:Proteins
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