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Deng Q, Zhang J, Liu J, Liu Y, Dai Z, Zou X, Li Z. Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network. Interdiscip Sci 2024; 16:649-664. [PMID: 38457108 DOI: 10.1007/s12539-024-00615-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 03/09/2024]
Abstract
As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. Therefore, identifying protein phosphorylation site-disease associations can help to elucidate the pathogenesis of disease and discover new drug targets. Networks of sequence similarity and Gaussian interaction profile kernel similarity were constructed for phosphorylation sites, as well as networks of disease semantic similarity, disease symptom similarity and Gaussian interaction profile kernel similarity were constructed for diseases. To effectively combine different phosphorylation sites and disease similarity information, random walk with restart algorithm was used to obtain the topology information of the network. Then, the diffusion component analysis method was utilized to obtain the comprehensive phosphorylation site similarity and disease similarity. Meanwhile, the reliable negative samples were screened based on the Euclidean distance method. Finally, a convolutional neural network (CNN) model was constructed to identify potential associations between phosphorylation sites and diseases. Based on tenfold cross-validation, the evaluation indicators were obtained including accuracy of 93.48%, specificity of 96.82%, sensitivity of 90.15%, precision of 96.62%, Matthew's correlation coefficient of 0.8719, area under the receiver operating characteristic curve of 0.9786 and area under the precision-recall curve of 0.9836. Additionally, most of the top 20 predicted disease-related phosphorylation sites (19/20 for Alzheimer's disease; 20/16 for neuroblastoma) were verified by literatures and databases. These results show that the proposed method has an outstanding prediction performance and a high practical value.
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Affiliation(s)
- Qian Deng
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Jing Zhang
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Jie Liu
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Yuqi Liu
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Zong Dai
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Xiaoyong Zou
- School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, China.
| | - Zhanchao Li
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China.
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Wu D, Fang X, Luan K, Xu Q, Lin S, Sun S, Yang J, Dong B, Manavalan B, Liao Z. Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method. Comput Biol Med 2023; 162:107065. [PMID: 37267826 DOI: 10.1016/j.compbiomed.2023.107065] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/30/2023] [Accepted: 05/27/2023] [Indexed: 06/04/2023]
Abstract
The Src Homology 2 (SH2) domain plays an important role in the signal transmission mechanism in organisms. It mediates the protein-protein interactions based on the combination between phosphotyrosine and motifs in SH2 domain. In this study, we designed a method to identify SH2 domain-containing proteins and non-SH2 domain-containing proteins through deep learning technology. Firstly, we collected SH2 and non-SH2 domain-containing protein sequences including multiple species. We built six deep learning models through DeepBIO after data preprocessing and compared their performance. Secondly, we selected the model with the strongest comprehensive ability to conduct training and test separately again, and analyze the results visually. It was found that 288-dimensional (288D) feature could effectively identify two types of proteins. Finally, motifs analysis discovered the specific motif YKIR and revealed its function in signal transduction. In summary, we successfully identified SH2 domain and non-SH2 domain proteins through deep learning method, and obtained 288D features that perform best. In addition, we found a new motif YKIR in SH2 domain, and analyzed its function which helps to further understand the signaling mechanisms within the organism.
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Affiliation(s)
- Duanzhi Wu
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Xin Fang
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China; Laboratory of Non-communicable Chronic Disease Control, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, 350012, China
| | - Kai Luan
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Qijin Xu
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Shiqi Lin
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Shiying Sun
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Jiaying Yang
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Bingying Dong
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
| | - Zhijun Liao
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
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Rescifina A. Progress of the "Molecular Informatics" Section in 2022. Int J Mol Sci 2023; 24:ijms24119442. [PMID: 37298393 DOI: 10.3390/ijms24119442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023] Open
Abstract
This is the first Editorial of the "Molecular Informatics" Section (MIS) of the International Journal of Molecular Sciences (IJMS), which was created towards the end of 2018 (the first article was submitted on 27 September 2018) and has experienced significant growth from 2018 to now [...].
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Affiliation(s)
- Antonio Rescifina
- Department of Drug and Health Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
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Zeng Y, Liu D, Wang Y. Identification of phosphorylation site using S-padding strategy based convolutional neural network. Health Inf Sci Syst 2022; 10:29. [PMID: 36124094 PMCID: PMC9481819 DOI: 10.1007/s13755-022-00196-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 08/25/2022] [Indexed: 10/14/2022] Open
Abstract
Purpose Abnormal phosphorylation has been proved to associate with a variety of human diseases, and the identification of phosphorylation sites is one of the research hotspots in healthcare. The study of phosphorylation site prediction in deep learning models often introduces a variety of information, and the utilization of complex models limits the usage scenarios of the models. Methods An enhanced deep learning method with S-padding strategy based on convolutional neural network is proposed in this paper. The S-padding strategy forms a three-dimensional matrix with extension information from original amino acid sequences, and a corresponding 2D-CNN model is designed to abstract the comprehensive features of phosphorylation site area in protein sequences. Results The fivefold cross-validation experiments are conducted, and the results show the performance of the proposed method on human dataset can achieve an accuracy of 89.68 % on serine/threonine sites and 88.16 % on tyrosine sites, respectively. Furthermore, phosphorylation site prediction on different organisms obtains the accuracy, sensitivity, and specificity of over 0.85, indicating a potential capability on phosphorylation site prediction task. Comparison result with existing models shows that the proposed method obtains better performance on both accuracy and AUC value, and the proposed method can further improve performance with sufficient training data. Conclusion This method enables proteome-wide predictions via models trained on a large amount of phosphorylation data, further exploiting the potential of protein phosphorylation site identification, and helping to provide insights into phosphorylation mechanisms.
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Affiliation(s)
- Yanjiao Zeng
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Dongning Liu
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Yang Wang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
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