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has been cited by the following article:

• TITLE: PFP-RFSM: Protein fold prediction by using random forests and sequence motifs

  AUTHORS: Junfei Li, Jigang Wu, Ke Chen

  KEYWORDS: Protein Fold; Structure Analysis; Random Forest; Sequence Motifs

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.6 No.12, December 20, 2013

  ABSTRACT: Protein tertiary structure is indispensible in revealing the biological functions of proteins. De novo perdition of protein tertiary structure is dependent on protein fold recognition. This study proposes a novel method for prediction of protein fold types which takes primary sequence as input. The proposed method, PFP-RFSM, employs a random forest classifier and a comprehensive feature representation, including both sequence and predicted structure descriptors. Particularly, we propose a method for generation of features based on sequence motifs and those features are firstly employed in protein fold prediction. PFP-RFSM and ten representative protein fold predictors are validated in a benchmark dataset consisting of 27 fold types. Experiments demonstrate that PFP-RFSM outperforms all existing protein fold predictors and improves the success rates by 2%-14%. The results suggest sequence motifs are effective in classification and analysis of protein sequences.