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ConvsPPIS: Identifying Protein-protein Interaction Sites by an Ensemble Convolutional Neural Network with Feature Graph

Author(s):

Huaixu Zhu, Xiuquan Du* and Yu Yao   Pages 1 - 11 ( 11 )

Abstract:


Background/Objective: Protein-protein interactions are essentials for most cellular processes and thus, unveiling how proteins interact with is a crucial question that can be better understood by recognizing which residues participate in the interaction. Although many computational approaches have been proposed to predict interfacial residues, their feature perspective and model learning ability are not enough to achieve ideal results.

Method: In this study, we proposed an ensemble deep convolutional neural network, which explores the context and positional context of consecutive residues within a protein sub-sequence. Specifically, unlike the feature view of previous methods, ConvsPPIS used evolutionary, physicochemical, and structural protein characteristics to form their own feature graph respectively. After that, three independent deep convolutional neural networks are trained on each type of feature graph for learning the pattern among residues of the protein sequence. Finally, we integrated these three deep networks into an ensemble predictor with leveraging complementary information of these features to predict potential interface residues.

Results: Some comparative experiments have conducted through 10-fold cross-validation. The results indicated that ConvsPPIS achieved superior performance on DBv5-Sel dataset with an Accuracy of 88%. Additional experiments on CAPRI-Alone dataset demonstrated ConvsPPIS also has better prediction performance.

Conclusion: The ConvsPPIS method provided a new perspective to capture protein feature expression for identifying protein-protein interaction sites. The experiments proved the superiority of this method. The code is available at https://github.com/zhx-main/ConvsPPIS.

Keywords:

feature graph, positional context, protein complex, interface prediction, convolution neural network, ensemble learning

Affiliation:

Computer Science and Technology, Anhui University, Hefei, Computer Science and Technology, Anhui University, Hefei, Computer Science and Technology, Anhui University, Hefei



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