GGN-GO: geometric graph networks for predicting protein function by multi-scale structure features Article Swipe
Jia Mi
,
Han Wang
,
Jing Li
,
Jinghong Sun
,
Chang Li
,
Jing Wan
,
Yuan Zeng
,
Jingyang Gao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbae559
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbae559
Recent advances in high-throughput sequencing have led to an explosion of genomic and transcriptomic data, offering a wealth of protein sequence information. However, the functions of most proteins remain unannotated. Traditional experimental methods for annotation of protein functions are costly and time-consuming. Current deep learning methods typically rely on Graph Convolutional Networks to propagate features between protein residues. However, these methods fail to capture fine atomic-level geometric structural features and cannot directly compute or propagate structural features (such as distances, directions, and angles) when transmitting features, often simplifying them to scalars. Additionally, difficulties in capturing long-range dependencies limit the model’s ability to identify key nodes (residues). To address these challenges, we propose a geometric graph network (GGN-GO) for predicting protein function that enriches feature extraction by capturing multi-scale geometric structural features at the atomic and residue levels. We use a geometric vector perceptron to convert these features into vector representations and aggregate them with node features for better understanding and propagation in the network. Moreover, we introduce a graph attention pooling layer captures key node information by adaptively aggregating local functional motifs, while contrastive learning enhances graph representation discriminability through random noise and different views. The experimental results show that GGN-GO outperforms six comparative methods in tasks with the most labels for both experimentally validated and predicted protein structures. Furthermore, GGN-GO identifies functional residues corresponding to those experimentally confirmed, showcasing its interpretability and the ability to pinpoint key protein regions. The code and data are available at: https://github.com/MiJia-ID/GGN-GO
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbae559
- OA Status
- gold
- Cited By
- 4
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403972634
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403972634Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/bib/bbae559Digital Object Identifier
- Title
-
GGN-GO: geometric graph networks for predicting protein function by multi-scale structure featuresWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-09-23Full publication date if available
- Authors
-
Jia Mi, Han Wang, Jing Li, Jinghong Sun, Chang Li, Jing Wan, Yuan Zeng, Jingyang GaoList of authors in order
- Landing page
-
https://doi.org/10.1093/bib/bbae559Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1093/bib/bbae559Direct OA link when available
- Concepts
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Graph, Computer science, Scale (ratio), Protein function prediction, Function (biology), Artificial intelligence, Pattern recognition (psychology), Protein function, Theoretical computer science, Biology, Cartography, Geography, Biochemistry, Gene, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
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-
2025: 3, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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