A novel sequence-based prediction method for ATP-binding sites using fusion of SMOTE algorithm and random forests classifier

Figures & data

Figure 1. MCC values for different sizes of sliding window on ATP-168 and ATP-227.

Figure 2. Architecture of the proposed method.

Figure 3. Framework of the proposed prediction procedure.

Table 1. Prediction performance based on the under-sampled dataset, original dataset and SMOTE dataset over five-fold cross-validation.

Dataset	Method	ACC	Sensitivity	Specificity	MCC	AUC
ATP-168	Under-sampling	0.949	0.349	0.981	0.386	0.841
	Original data	0.959	0.255	0.997	0.434	0.847
	SMOTE	0.959	0.383	0.989	0.480	0.873
ATP-227	Under-sampling	0.958	0.397	0.983	0.412	0.848
	Original data	0.967	0.308	0.996	0.476	0.856
	SMOTE	0.969	0.439	0.992	0.535	0.896

Figure 4. Prediction ROC curves of datasets with SMOTE algorithm, original datasets without under-sampling and datasets with under-sampling on ATP-168 (a) and ATP-227 (b).

Table 2. Comparison of prediction performance with and without the template-based method on ATP-168 and ATP-227 over five-fold cross-validation.

Dataset	Method	ACC	Sensitivity	Specificity	MCC	AUC
ATP-168	With template	0.959	0.383	0.989	0.480	0.873
	Without template	0.958	0.341	0.990	0.451	0.865
ATP-227	With template	0.969	0.439	0.992	0.535	0.896
	Without template	0.968	0.395	0.993	0.511	0.892

Figure 5. ROC curves of prediction result with and without template-based method on ATP-168 (a) and ATP-227 (b).

Table 3. Performance comparison of our proposed method with ATPint on ATP168 over five-fold cross-validation.

Method	ACC	Sensitivity	Specificity	MCC	AUC
ATPint	0.751	0.744	0.758	0.249	0.823
Proposed method	0.790	0.777	0.785	0.291	0.873

Table 4. Prediction results comparison of our proposed method with other sequence-based methods on ATP-227 over five-fold cross-validation.

Method	ACC	Sensitivity	Specificity	MCC	AUC
ATPint	0.648	0.539	0.651	0.078	0.627
ATPsite	0.962	0.361	0.988	0.433	0.854
NsitePred	0.960	0.444	0.982	0.460	0.861
TargetATP	0.966	0.412	0.990	0.501	0.895
TargetATPsite	0.966	0.445	0.989	0.520	0.881
TCS_ATP	0.962	0.419	0.985	0.460	0.860
Proposed method	0.969	0.439	0.992	0.535	0.896

Table 5. Performance comparison of our proposed method with other sequence-based methods on an independent testing set.

Method	ACC	Sensitivity	Specificity	MCC	AUC
ATPint	0.655	0.512	0.660	0.066	0.606
ATPsite	0.969	0.367	0.991	0.451	0.868
NsitePred	0.967	0.460	0.985	0.476	0.875
TargetATP	0.969	0.489	0.989	0.542	0.912
TargetATPsite	0.972	0.458	0.991	0.530	0.882
Proposed method	0.973	0.504	0.990	0.553	0.912

Figure 6. Binding sites predicted by the proposed method, predictor constructed on under-sampled dataset, predictor constructed on original dataset and TargetATP for protein 1O93_A.

Note: Black dots indicate the true binding sites in the protein sequence; blue dots indicate the binding residues predicted by the proposed method; red, orange and green dots represent the result of TargetATP, predictor constructed on under-sampled dataset and predictor constructed on original dataset, respectively. The probability curves of involved predictors are drawn in corresponding colors.

Data availability statement

The data that support the findings of this study are openly available in BMC Bioinformatics at https://doi.org/10.1186/1471-2105-10-434, reference [17], and in Proteome Science at https://doi.org/10.1186/1477-5956-9-s1-s4 [18].