An Improved Deep Neural Network Algorithm for the Prediction of Limited Proteolysis in Native Protein

Abstract

Protease is a proteolytic enzyme that hydrolyzes the amino acid where the cleavage only occurs at specific sites of the amino acid substrate.  By discovering the nick site, the prediction on the function of proteases can be identified and enable humans to control the protein's hydrolysis by their corresponding protease. It is very contributed to controlling protein production especially viral protein. The experts may alter the production of viral protein by reducing the viral proteases to undergo proteolysis. With the rise of computational methods in this era, deep learning is becoming more famous and applied in every field of study, including the biological area. Conventional techniques such as mass spectrometry and two-dimensional gel electrophoresis are being replaced by computational methods due to time-consuming. Thus, this study improves the deep learning algorithm by proposing the Hybrid model of Random Forest + Deep Neural Network (Hybrid RF+DNN) to classify nick sites. The classification in this study is compared with the other machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM),  and Deep Neural Network (DNN). The proposed method is believed to enhance the classification results in identifying the positive and negative nick sites. The RF is a feature-selector that gathers the most important feature before entering the DNN classifier. This approach reduces the data dimensionality and speeds up the execution time of the training process. The performance of the models was measured by confusion matrix, specificity, sensitivity, etc. However, the proposed method is not the best performer among the mentioned classifiers from the result. The proposed method may become the best performer as the parameter tuning is done more precisely, even after the feature selection by the RF algorithm. Thus, the proposed method with the enhancement appears to be an alternative to the researcher discovering nick site.