Identification of DNA-protein binding residues through integration of Transformer encoder and Bi-directional Long Short-Term Memory

Haipeng Zhao; Baozhong Zhu; Tengsheng Jiang; Zhiming Cui; Hongjie Wu; Haipeng Zhao; Baozhong Zhu; Tengsheng Jiang; Zhiming Cui; Hongjie Wu

doi:10.3934/mbe.2024008

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 1: 170-185. doi: 10.3934/mbe.2024008

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Research article

Identification of DNA-protein binding residues through integration of Transformer encoder and Bi-directional Long Short-Term Memory

1.
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
2.
Gusu School, Nanjing Medical University, Suzhou, China

Academic Editor: Yang Kuang

Received: 12 October 2023 Revised: 18 November 2023 Accepted: 28 November 2023 Published: 11 December 2023

DNA-protein binding is crucial for the normal development and function of organisms. The significance of accurately identifying DNA-protein binding sites lies in its role in disease prevention and the development of innovative approaches to disease treatment. In the present study, we introduce a precise and robust identifier for DNA-protein binding residues. In the context of protein representation, we combine the evolutionary information of the protein, represented by its position-specific scoring matrix, with the spatial information of the protein's secondary structure, enriching the overall informational content. This approach initially employs a combination of Bi-directional Long Short-Term Memory and Transformer encoder to jointly extract the interdependencies among residues within the protein sequence. Subsequently, convolutional operations are applied to the resulting feature matrix to capture local features of the residues. Experimental results on the benchmark dataset demonstrate that our method exhibits a higher level of competitiveness when compared to contemporary classifiers. Specifically, our method achieved an MCC of 0.349, SP of 96.50%, SN of 44.03% and ACC of 94.59% on the PDNA-41 dataset.
- DNA-protein binding residues identification,
- Transformer encoder,
- BiLSTM,
- deep learning
Citation: Haipeng Zhao, Baozhong Zhu, Tengsheng Jiang, Zhiming Cui, Hongjie Wu. Identification of DNA-protein binding residues through integration of Transformer encoder and Bi-directional Long Short-Term Memory[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 170-185. doi: 10.3934/mbe.2024008

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Abstract

DNA-protein binding is crucial for the normal development and function of organisms. The significance of accurately identifying DNA-protein binding sites lies in its role in disease prevention and the development of innovative approaches to disease treatment. In the present study, we introduce a precise and robust identifier for DNA-protein binding residues. In the context of protein representation, we combine the evolutionary information of the protein, represented by its position-specific scoring matrix, with the spatial information of the protein's secondary structure, enriching the overall informational content. This approach initially employs a combination of Bi-directional Long Short-Term Memory and Transformer encoder to jointly extract the interdependencies among residues within the protein sequence. Subsequently, convolutional operations are applied to the resulting feature matrix to capture local features of the residues. Experimental results on the benchmark dataset demonstrate that our method exhibits a higher level of competitiveness when compared to contemporary classifiers. Specifically, our method achieved an MCC of 0.349, SP of 96.50%, SN of 44.03% and ACC of 94.59% on the PDNA-41 dataset.

References

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