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Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical Shifts.

YongE F, GaoShan K - PLoS ONE (2015)

Bottom Line: Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs.Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure.Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew's correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation.

View Article: PubMed Central - PubMed

Affiliation: College of Science, Inner Mongolia Agriculture University, Hohhot, PR China.

ABSTRACT
Successful prediction of the beta-hairpin motif will be helpful for understanding the of the fold recognition. Some algorithms have been proposed for the prediction of beta-hairpin motifs. However, the parameters used by these methods were primarily based on the amino acid sequences. Here, we proposed a novel model for predicting beta-hairpin structure based on the chemical shift. Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs. Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure. Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew's correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation. Our finding showed that the chemical shift is an effective parameter for beta-hairpin prediction, suggesting the quadratic discriminant analysis is a powerful algorithm for the prediction of beta-hairpin.

No MeSH data available.


Related in: MedlinePlus

Distribution chart of six-nuclei CSs in beta-hairpin and not beta-hairpin motifs.
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pone.0139280.g001: Distribution chart of six-nuclei CSs in beta-hairpin and not beta-hairpin motifs.

Mentions: We analyzed the average chemical shifts of six nuclei in beta-hairpin and not beta-hairpin dataset. As showed in Fig 1, we found that the different distribution of the CSs six nuclei in beta-hairpin and not beta-hairpin dataset. The average chemical shift values of C,Cα,Cβ,Hα,N nuclei are higher in not beta-hairpin dataset than beta-hairpin dataset. However, the average chemical shift value of HN nuclei is lower in not beta-hairpin dataset than beta-hairpin dataset.


Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical Shifts.

YongE F, GaoShan K - PLoS ONE (2015)

Distribution chart of six-nuclei CSs in beta-hairpin and not beta-hairpin motifs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4589334&req=5

pone.0139280.g001: Distribution chart of six-nuclei CSs in beta-hairpin and not beta-hairpin motifs.
Mentions: We analyzed the average chemical shifts of six nuclei in beta-hairpin and not beta-hairpin dataset. As showed in Fig 1, we found that the different distribution of the CSs six nuclei in beta-hairpin and not beta-hairpin dataset. The average chemical shift values of C,Cα,Cβ,Hα,N nuclei are higher in not beta-hairpin dataset than beta-hairpin dataset. However, the average chemical shift value of HN nuclei is lower in not beta-hairpin dataset than beta-hairpin dataset.

Bottom Line: Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs.Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure.Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew's correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation.

View Article: PubMed Central - PubMed

Affiliation: College of Science, Inner Mongolia Agriculture University, Hohhot, PR China.

ABSTRACT
Successful prediction of the beta-hairpin motif will be helpful for understanding the of the fold recognition. Some algorithms have been proposed for the prediction of beta-hairpin motifs. However, the parameters used by these methods were primarily based on the amino acid sequences. Here, we proposed a novel model for predicting beta-hairpin structure based on the chemical shift. Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs. Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure. Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew's correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation. Our finding showed that the chemical shift is an effective parameter for beta-hairpin prediction, suggesting the quadratic discriminant analysis is a powerful algorithm for the prediction of beta-hairpin.

No MeSH data available.


Related in: MedlinePlus