PsePSSM-based prediction for the protein-ATP binding sites
L Qian, Y Jiang, YY Xuan, C Yuan… - Current …, 2021 - ingentaconnect.com
L Qian, Y Jiang, YY Xuan, C Yuan, T SiQiao
Current Bioinformatics, 2021•ingentaconnect.comBackground: Predicting the protein-ATP binding sites is a highly unbalanced binary
classification problem, and higher precision prediction through the machine learning
methods is of great significance to the researches on proteins' functions and the design of
drugs. Objective: Most existing researches typically select 17aa as the length of window by
experience, and extract features by the Position-specific Scoring Matrix (PSSM), and then
construct models predicting with SVC. However, the independent prediction values obtained …
classification problem, and higher precision prediction through the machine learning
methods is of great significance to the researches on proteins' functions and the design of
drugs. Objective: Most existing researches typically select 17aa as the length of window by
experience, and extract features by the Position-specific Scoring Matrix (PSSM), and then
construct models predicting with SVC. However, the independent prediction values obtained …
Background
Predicting the protein-ATP binding sites is a highly unbalanced binary classification problem, and higher precision prediction through the machine learning methods is of great significance to the researches on proteins’ functions and the design of drugs.
Objective
Most existing researches typically select 17aa as the length of window by experience, and extract features by the Position-specific Scoring Matrix (PSSM), and then construct models predicting with SVC. However, the independent prediction values obtained in these researches are either over-high (ACC) or lower (MCC), and there is therefore a larger improvement room in the prediction precision.
Methods
This paper utilizes the mutual information, I, to define the window length of 15aa, and the Pseudo Position Specific Scoring Matrix (PsePSSM), which is more fault-tolerance, to extract the features, and then train multiple 1:1 SVC classifiers to model, and finally perform the simple votings.
Results
The prediction results over two protein-ATP binding site datasets, the ATP168 and the ATP227, are totally superior to the independent prediction results obtained in the Reference Feature Extraction Approach. And in our approach, the MCC values are respectively improved, from the range of 0.3110 ∼ 0.5360 and the range of 0.3060 ∼ 0.553, to 0.7512 and 0.7106.
Conclusion
Further, we explain why the PsePSSM approach is more fault-tolerance. This approach has a promising application prospect in the feature-extraction of protein sequences.
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