Prediction of protein folding rates from primary sequence by fusing multiple sequential features

Hong-Bin Shen, Jiang-Ning Song, Kuo-Chen Chou

Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan;.
Gordon Life Science Institute, 13784 Torrey Del Mar Drive, San Diego, California 92130, USA..
Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200240, China;.

DOI: 10.4236/jbise.2009.23024                

Abstract

We have developed a web-server for predicting the folding rate of a protein based on its amino acid sequence information alone. The web- server is called Pred-PFR (Predicting Protein Folding Rate). Pred-PFR is featured by fusing multiple individual predictors, each of which is established based on one special feature derived from the protein sequence. The ensemble pre-dictor thus formed is superior to the individual ones, as demonstrated by achieving higher correlation coefficient and lower root mean square deviation between the predicted and observed results when examined by the jack-knife cross-validation on a benchmark dataset constructed recently. As a user-friendly web- server, Pred-PFR is freely accessible to the public at www.csbio.sjtu.edu.cn/bioinf/Folding Rate/.

Keywords

Protein Folding Rate; Ensemble Predictor; Fusion Approach; Web-Server; Pred-PFR

Conflicts of Interest

The authors declare no conflicts of interest.

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