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A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation

Wang, Feiqi Chen, Yun-Ti Yang, Jinn-Moon Akutsu, Tatsuya 京都大学 DOI:10.1038/s41598-021-04230-7

2022

概要

Protein kinase-inhibitor interactions are key to the phosphorylation of proteins involved in cell proliferation, differentiation, and apoptosis, which shows the importance of binding mechanism research and kinase inhibitor design. In this study, a novel machine learning module (i.e., the WL Box) was designed and assembled to the Prediction of Interaction Sites of Protein Kinase Inhibitors (PISPKI) model, which is a graph convolutional neural network (GCN) to predict the interaction sites of protein kinase inhibitors. The WL Box is a novel module based on the well-known Weisfeiler-Lehman algorithm, which assembles multiple switch weights to effectively compute graph features. The PISPKI model was evaluated by testing with shuffled datasets and ablation analysis using 11 kinase classes. The accuracy of the PISPKI model with the shuffled datasets varied from 83 to 86%, demonstrating superior performance compared to two baseline models. The effectiveness of the model was confirmed by testing with shuffled datasets. Furthermore, the performance of each component of the model was analyzed via the ablation study, which demonstrated that the WL Box module was critical. The code is available at https://github.com/feiqiwang/PISPKI.

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Acknowledgements

T.A. was partially supported by JSPS KAKENHI (grant no. 18H04113). This work was also supported in part by

Research Collaboration Projects of the Institute for Chemical Research, Kyoto University, Kyoto, Japan.

Author contributions

F. W. designed the method, conducted the computational experiments, and wrote the draft of the manuscript.

Y.-T. C and J.-M. Y. gave important suggestions on computational experiments. T. A. gave the problem setting

and supervised the research. Y.-T. C., J.-M. Y., and T. A. improved the manuscript. All authors have reviewed

and approved the content of this article.

Competing interests The authors declare no competing interests.

Additional information

Supplementary Information The online version contains supplementary material available at https://​doi.​org/​

10.​1038/​s41598-​021-​04230-7.

Correspondence and requests for materials should be addressed to F.W.

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