Improving the performance of β-turn prediction using predicted shape strings and a two-layer support vector machine model

Abstract

Background: The β-turn is a secondary protein structure type that plays an important role in protein configuration and function. Development of accurate prediction methods to identify β-turns in protein sequences is valuable. Several methods for β-turn prediction have been developed; however, the prediction quality is still a challenge and there is substantial room for improvement. Innovations of the proposed method focus on discovering effective features, and constructing a new architectural model.

Results: We utilized predicted secondary structures, predicted shape strings and the position-specific scoring matrix (PSSM) as input features, and proposed a novel two-layer model to enhance the prediction. We achieved the highest values according to four evaluation measures, i.e. Q(total) = 87.2%, MCC = 0.66, Q(observed) = 75.9%, and Q(predicted) = 73.8% on the BT426 dataset. The results show that our proposed two-layer model discriminates better between β-turns and non-β-turns than the single model due to obtaining higher Q(predicted). Moreover, the predicted shape strings based on the structural alignment approach greatly improve the performance, and the same improvements were observed on BT547 and BT823 datasets as well.

Conclusion: In this article, we present a comprehensive method for the prediction of β-turns. Experiments show that the proposed method constitutes a great improvement over the competing prediction methods.

Figure 1

The flow chart of shape string prediction.

Figure 2

The architecture of the proposed prediction method. N denotes the window size.

Figure 3

The distribution of two clusters. The axes correspond to top 3 PCs (×100) of PCA (principal component analysis) of positive samples with β-turns. Red dots denote samples in cluster 1. Blue dots denote samples in cluster 2.

Figure 4

ROC curves for the prediction on the BT426 dataset. Green curve corresponds to the prediction using predicted secondary structures from PROTEUS, PSSMs and predicted shape strings as input features, while the blue curve corresponds to the prediction using predicted secondary structures from PROTEUS and PSSMs.

Figure 5

The distribution of shape strings in sliding-window fragments of β-turns and non-β-turns. (a) denotes the distribution in β-turns, while (b) denotes that in non-β-turns. The height of symbols indicates the relative frequency of that type of shape string at that position. Both were created by WebLogo [53].

Figure 6

The proportion ratio of G (Glycine), C (coil) and T (turns) existing in β-turns and non-β-turns. (a) denotes the proportion of each type existing in β-turns and non-β-turns. (b) denotes the proportion ratio of β-turns to non-β-turns.

Figure 7

ROC curves for the prediction using predicted shape string and real shape string. Green curve corresponds to the prediction using real shape string, while the blue curve corresponds to the prediction using predicted shape string.