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Prediction of Protein–Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets

Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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Int. J. Mol. Sci. 2020, 21(2), 467; https://doi.org/10.3390/ijms21020467
Received: 24 November 2019 / Revised: 23 December 2019 / Accepted: 8 January 2020 / Published: 11 January 2020
(This article belongs to the Section Molecular Biology)
Protein–protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms. View Full-Text
Keywords: protein–protein interaction sites; convolutional neural network; residue binding propensity protein–protein interaction sites; convolutional neural network; residue binding propensity
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Xie, Z.; Deng, X.; Shu, K. Prediction of Protein–Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets. Int. J. Mol. Sci. 2020, 21, 467.

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