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 larger
improving 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 trains multiple 1:1
SVC classifiers to model, and finally performs 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.
A Tri-Gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition
