Abstract
Proteins have significant biological effects when they bind to other substances, with binding to DNA being particularly crucial. Therefore, accurate identification of protein-DNA binding residues is important for further understanding of the protein-DNA interaction mechanism. Most current state-of-the-art methods are two-step approaches: the first step uses a sliding window technique to extract residue features; the second step uses each residue as an input to the model for prediction. This has a negative impact on the efficiency of prediction and ease of use. In this study, we propose a sequence-to-sequence (seq2seq) model that can input the entire protein sequence of variable length and use multiple modules including Transformer Encoder Module, Feature Fusion Module, and Feature Extraction Module for multi-layer feature processing. The Transformer Encoder Module is used to extract global features while the Feature Extraction Module is used to extract local features, further improving the recognition capability of the model. Comparison results on two benchmark datasets PDNA-543 and PDNA-41 demonstrate the effectiveness of our method in identifying protein-DNA binding residues. The code is available at https://github.com/HaipengZZhao/Prediction-of-Residues.
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Acknowledgement
This paper is supported by the National Natural Science Foundation of China (62073231, 62176175, 61902271), National Research Project (2020YFC2006602), Provincial Key Laboratory for Computer Information Processing Technology, Soochow University (KJS2166), Opening Topic Fund of Big Data Intelligent Engineering Laboratory of Jiangsu Province (SDGC2157).
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Zhao, H., Zhu, B., Jiang, T., Cui, Z., Wu, H. (2023). A Transformer-Based Deep Learning Approach with Multi-layer Feature Processing for Accurate Prediction of Protein-DNA Binding Residues. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_47
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