GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks Article Swipe
Zeyu Wang
,
Xiaoli Yang
,
Shang Gao
,
Yanchun Liang
,
Xiaohu Shi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/ijms26030941
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/ijms26030941
Phosphorylation is one of the most common protein post-translational modifications. The identification of phosphorylation sites serves as the cornerstone for protein-phosphorylation-related research. This paper proposes a protein-phosphorylation site-prediction model based on graph neural networks named GraphPhos, which combines sequence features with structure features. Sequence features are derived from manual extraction and the calculation of protein pre-trained language models, and the structure feature is the secondary structure contact map calculated from protein tertiary structure. These features are then innovatively applied to graph neural networks. By inputting the features of the entire protein sequence and its contact graph, GraphPhos achieves the goal of predicting phosphorylation sites along the entire protein. Experimental results indicate that GraphPhos improves the accuracy of serine, threonine, and tyrosine site prediction by at least 8%, 15%, and 12%, respectively, exhibiting an average 7% improvement in accuracy compared to individual amino acid category prediction models.
Related Topics
Concepts
Phosphorylation
Protein phosphorylation
Threonine
Serine
Graph
Computational biology
Artificial neural network
Computer science
Artificial intelligence
Biology
Biochemistry
Protein kinase A
Theoretical computer science
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijms26030941
- https://www.mdpi.com/1422-0067/26/3/941/pdf?version=1737629568
- OA Status
- gold
- Cited By
- 1
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406761782
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406761782Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijms26030941Digital Object Identifier
- Title
-
GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-23Full publication date if available
- Authors
-
Zeyu Wang, Xiaoli Yang, Shang Gao, Yanchun Liang, Xiaohu ShiList of authors in order
- Landing page
-
https://doi.org/10.3390/ijms26030941Publisher landing page
- PDF URL
-
https://www.mdpi.com/1422-0067/26/3/941/pdf?version=1737629568Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1422-0067/26/3/941/pdf?version=1737629568Direct OA link when available
- Concepts
-
Phosphorylation, Protein phosphorylation, Threonine, Serine, Graph, Computational biology, Artificial neural network, Computer science, Artificial intelligence, Biology, Biochemistry, Protein kinase A, Theoretical computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
-
52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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