Abstract
Protein-protein interactions (PPIs) play an important role in the life activities of organisms. With the availability of large amounts of protein sequence data, PPIs prediction methods have attracted increasing attention. A variety of protein sequence coding methods have emerged, but the training of these methods is particularly time consuming. To solve this issue, we have proposed a novel matrix sequence coding method. Based on deep neural network (DNN) and a novel matrix protein sequence descriptor, we constructed a protein interaction prediction model for predicting PPIs. When performed on human PPIs data, the method achieved an accuracy of 94.34%, a recall of 98.28%, an area under the curve (AUC) of 97.79% and a loss of 23.25%. A non-redundant dataset was used to evaluate this prediction model, and the prediction accuracy is 88.29%. These results indicate that the matrix of sequence (MOS) descriptor can enhance the predictive power of PPIs and reduce training time, which can be a useful complement for future proteomics research. The experimental code and experimental results can be found at https://github.com/smalltalkman/hppi-tensorflow.
Highlights
Protein-protein interactions (PPIs) are useful for elucidating the changing mechanisms of organisms in physiological or pathological conditions and are important for disease prevention and drug development
The cross-entropy cost function can measure the predicted and actual values in a deep neural network, and it can compensate for the defects caused by the easy saturation of the sigmoid function, causing the training set to converge faster
To investigate the contribution of the novel matrix of sequence (MOS) descriptor, we separately trained deep neural network (DNN) based on conjoint triad method (CT), auto covariance method (AC), local descriptor (LD), and MOS
Summary
Protein-protein interactions (PPIs) are useful for elucidating the changing mechanisms of organisms in physiological or pathological conditions and are important for disease prevention and drug development. Numerous methods for studying protein-protein interactions, such as yeast two-hybrid screens [1], hybrid approaches [2] and protein chips [3], have emerged. All of these experimental methods have the disadvantage of being time-consuming and costly. Using computational approaches to predict unknown PPIs has become an important research topic in bioinformatics. Many computer prediction methods have been proposed to predict PPIs based on a phylogenetic profile method [4], amino acid index distribution [5] and gene fusion events [6, 7].
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