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.
Highlights
Protein function is inherently correlated with its structure
The results showed that the performance of chemical shifts (CSs) outperform that of 20 amino acid compositions (AAC) in the prediction of beta-hairpin
For further investigating whether the distribution of average CSs of six nuclei in two datasets are independent of one another, the analysis of variance (ANOVA) [19, 31] can be used for the
Summary
Protein function is inherently correlated with its structure. So, the prediction of protein structure is an active research field in bioinformatics. Protein super-secondary-structure motifs are composed of a few regular secondary structural elements connected by loops. These structural motifs play an important role in protein folding and stability because a large number of motifs exist in protein spatial structure. The empirical prediction of protein super-secondary structure essentially consists of two parts: one is the prediction of different structural types from amino acid sequences [1,2,3]; another is the prediction of structural motifs [4,5,6,7]. The prediction of beta-hairpin motif will be helpful to identify fold in the unknown structure. The features of these studies were mainly derived from the amino acid
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