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Recognition of 27-class protein folds by adding the interaction of segments and motif information.

Feng Z, Hu X - Biomed Res Int (2014)

Bottom Line: After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved.The overall accuracy of the testing set and structural classification measured up to 78.38% and 92.55%, respectively.In order to compare with the results of previous researchers, the method above was tested on Ding and Dubchak's dataset which has been widely used by many previous researchers, and an improved overall accuracy 70.24% was obtained.

View Article: PubMed Central - PubMed

Affiliation: Department of Sciences, Inner Mongolia University of Technology, Hohhot, China.

ABSTRACT
The recognition of protein folds is an important step for the prediction of protein structure and function. After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved. However, the influences of interactions from predicted secondary structure segments and motif information on protein folding have not been considered. Therefore, the recognition of 27-class protein folds with the interaction of segments and motif information is very important. Based on the 27-class folds dataset built by Liu et al., amino acid composition, the interactions of secondary structure segments, motif frequency, and predicted secondary structure information were extracted. Using the Random Forest algorithm and the ensemble classification strategy, 27-class protein folds and corresponding structural classification were identified by independent test. The overall accuracy of the testing set and structural classification measured up to 78.38% and 92.55%, respectively. When the training set and testing set were combined, the overall accuracy by 5-fold cross validation was 81.16%. In order to compare with the results of previous researchers, the method above was tested on Ding and Dubchak's dataset which has been widely used by many previous researchers, and an improved overall accuracy 70.24% was obtained.

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The architecture of the protein folds identification system.
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fig2: The architecture of the protein folds identification system.

Mentions: For further comparison, identification results from the gradual addition of relevant feature parameters are listed (Table 3). Furthermore, the architecture of the protein folds identification system is shown (Figure 2).


Recognition of 27-class protein folds by adding the interaction of segments and motif information.

Feng Z, Hu X - Biomed Res Int (2014)

The architecture of the protein folds identification system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4127253&req=5

fig2: The architecture of the protein folds identification system.
Mentions: For further comparison, identification results from the gradual addition of relevant feature parameters are listed (Table 3). Furthermore, the architecture of the protein folds identification system is shown (Figure 2).

Bottom Line: After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved.The overall accuracy of the testing set and structural classification measured up to 78.38% and 92.55%, respectively.In order to compare with the results of previous researchers, the method above was tested on Ding and Dubchak's dataset which has been widely used by many previous researchers, and an improved overall accuracy 70.24% was obtained.

View Article: PubMed Central - PubMed

Affiliation: Department of Sciences, Inner Mongolia University of Technology, Hohhot, China.

ABSTRACT
The recognition of protein folds is an important step for the prediction of protein structure and function. After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved. However, the influences of interactions from predicted secondary structure segments and motif information on protein folding have not been considered. Therefore, the recognition of 27-class protein folds with the interaction of segments and motif information is very important. Based on the 27-class folds dataset built by Liu et al., amino acid composition, the interactions of secondary structure segments, motif frequency, and predicted secondary structure information were extracted. Using the Random Forest algorithm and the ensemble classification strategy, 27-class protein folds and corresponding structural classification were identified by independent test. The overall accuracy of the testing set and structural classification measured up to 78.38% and 92.55%, respectively. When the training set and testing set were combined, the overall accuracy by 5-fold cross validation was 81.16%. In order to compare with the results of previous researchers, the method above was tested on Ding and Dubchak's dataset which has been widely used by many previous researchers, and an improved overall accuracy 70.24% was obtained.

Show MeSH
Related in: MedlinePlus