Abstract
Protein interface prediction is fundamental to understand the hidden principles of many living activities. Although many approaches to the task of protein interface prediction have been proposed, most of existing methods fail to make full use of the available sequence information and structure information. To address the challenge, we propose a deep learning-based end-to-end framework for protein interface prediction, in which a hybrid attention mechanism is utilized to take into account the semantic associations and complementary effect between both sequence and structure information. More specifically, a cross-modal attention is built to capture the semantic associations between sequence representations and structure representations for proteins. In addition, a type-level attention is introduced to model the different contributions of sequence and structure information for predicting protein interaction interface. Experimental results on three commonly used datasets demonstrate the effectiveness of the proposed method.
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Acknowledgment
The work is partially supported by the National Natural Science Foundation of China (No. 61532008, No. 61872157, and No. 61932008), the Wuhan Science and Technology Program (2019010701011392), the Key Research and Development Program of Hubei Province (2020BAB017), the Fundamental Research Funds for the Central Universities (CCNU19TD004), the Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS19-02) and the Guangxi Key Laboratory of Trusted Software (kx201905). Authors are grateful to the anonymous reviewers for helpful comments.
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Wu, H., Luo, S., Zhao, W., Jiang, X., He, T. (2022). A Novel Protein Interface Prediction Framework via Hybrid Attention Mechanism. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13282. Springer, Cham. https://doi.org/10.1007/978-3-031-05981-0_29
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