A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences

Xue Wang, Yuejin Wu, Rujing Wang, Yuanyuan Wei and Yuanmiao Gui

PLOS ONE, 2019, vol. 14, issue 6, 1-12

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0217312

DOI: 10.1371/journal.pone.0217312

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