MTNA: A deep learning based predictor for identifying multiple types of N-terminal protein acetylated sites

Yongbing Chen; Wenyuan Qin; Tong Liu; Ruikun Li; Fei He; Ye Han; Zhiqiang Ma; Zilin Ren; Yongbing Chen; Wenyuan Qin; Tong Liu; Ruikun Li; Fei He; Ye Han; Zhiqiang Ma; Zilin Ren

doi:10.3934/era.2023276

Electronic Research Archive

2023, Volume 31, Issue 9: 5442-5456. doi: 10.3934/era.2023276

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MTNA: A deep learning based predictor for identifying multiple types of N-terminal protein acetylated sites

Yongbing Chen ¹,
Wenyuan Qin ¹,
Tong Liu ¹,
Ruikun Li ¹,
Fei He ^{1
,
,},
Ye Han ^{2
,
,},
Zhiqiang Ma ^{3
,
,},
Zilin Ren ^{4
,
,}

1.
School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
2.
School of Information Technology, Jilin Agricultural University, Changchun 130118, China
3.
Department of Computer Science, College of Humanities & Sciences of Northeast Normal University, Changchun 130119, China
4.
Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun 130122, China

Received: 27 May 2023 Revised: 18 July 2023 Accepted: 23 July 2023 Published: 03 August 2023

N-terminal acetylation is a specific protein modification that occurs only at the N-terminus but plays a significant role in protein stability, folding, subcellular localization and protein-protein interactions. Computational methods enable finding N-terminal acetylated sites from large-scale proteins efficiently. However, limited by the number of the labeled proteins, existing tools only focus on certain subtypes of N-terminal acetylated sites on frequently detected amino acids. For example, NetAcet focuses on alanine, glycine, serine and threonine only, and N-Ace predicts on alanine, glycine, methionine, serine and threonine. With the growth of experimental N-terminal acetylated site data, it is observed that N-terminal protein acetylation occurs on nearly ten types of amino acids. To facilitate comprehensive analysis, we have developed MTNA (Multiple Types of N-terminal Acetylation), a deep learning network capable of accurately predicting N-terminal protein acetylation sites for various amino acids at the N-terminus. MTNA not only outperforms existing tools but also has the capability to identify rare types of N-terminal protein acetylated sites occurring on less studied amino acids.
- protein translational modification,
- protein acetylation,
- N-terminal acetylated sites,
- deep learning
Citation: Yongbing Chen, Wenyuan Qin, Tong Liu, Ruikun Li, Fei He, Ye Han, Zhiqiang Ma, Zilin Ren. MTNA: A deep learning based predictor for identifying multiple types of N-terminal protein acetylated sites[J]. Electronic Research Archive, 2023, 31(9): 5442-5456. doi: 10.3934/era.2023276

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Abstract

N-terminal acetylation is a specific protein modification that occurs only at the N-terminus but plays a significant role in protein stability, folding, subcellular localization and protein-protein interactions. Computational methods enable finding N-terminal acetylated sites from large-scale proteins efficiently. However, limited by the number of the labeled proteins, existing tools only focus on certain subtypes of N-terminal acetylated sites on frequently detected amino acids. For example, NetAcet focuses on alanine, glycine, serine and threonine only, and N-Ace predicts on alanine, glycine, methionine, serine and threonine. With the growth of experimental N-terminal acetylated site data, it is observed that N-terminal protein acetylation occurs on nearly ten types of amino acids. To facilitate comprehensive analysis, we have developed MTNA (Multiple Types of N-terminal Acetylation), a deep learning network capable of accurately predicting N-terminal protein acetylation sites for various amino acids at the N-terminus. MTNA not only outperforms existing tools but also has the capability to identify rare types of N-terminal protein acetylated sites occurring on less studied amino acids.

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