Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model Article Swipe
Yuansong Zeng
,
Zhuoyi Wei
,
Qianmu Yuan
,
Sheng Chen
,
Weijiang Yu
,
Yutong Lu
,
Jianzhao Gao
,
Yuedong Yang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btad187
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btad187
Motivation Identifying the B-cell epitopes is an essential step for guiding rational vaccine development and immunotherapies. Since experimental approaches are expensive and time-consuming, many computational methods have been designed to assist B-cell epitope prediction. However, existing sequence-based methods have limited performance since they only use contextual features of the sequential neighbors while neglecting structural information. Results Based on the recent breakthrough of AlphaFold2 in protein structure prediction, we propose GraphBepi, a novel graph-based model for accurate B-cell epitope prediction. For one protein, the predicted structure from AlphaFold2 is used to construct the protein graph, where the nodes/residues are encoded by ESM-2 learning representations. The graph is input into the edge-enhanced deep graph neural network (EGNN) to capture the spatial information in the predicted 3D structures. In parallel, a bidirectional long short-term memory neural networks (BiLSTM) are employed to capture long-range dependencies in the sequence. The learned low-dimensional representations by EGNN and BiLSTM are then combined into a multilayer perceptron for predicting B-cell epitopes. Through comprehensive tests on the curated epitope dataset, GraphBepi was shown to outperform the state-of-the-art methods by more than 5.5% and 44.0% in terms of AUC and AUPR, respectively. A web server is freely available at http://bio-web1.nscc-gz.cn/app/graphbepi. Availability and implementation The datasets, pre-computed features, source codes, and the trained model are available at https://github.com/biomed-AI/GraphBepi.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btad187
- https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad187/49841456/btad187.pdf
- OA Status
- gold
- Cited By
- 48
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4364353366
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4364353366Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioinformatics/btad187Digital Object Identifier
- Title
-
Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language modelWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-04-01Full publication date if available
- Authors
-
Yuansong Zeng, Zhuoyi Wei, Qianmu Yuan, Sheng Chen, Weijiang Yu, Yutong Lu, Jianzhao Gao, Yuedong YangList of authors in order
- Landing page
-
https://doi.org/10.1093/bioinformatics/btad187Publisher landing page
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad187/49841456/btad187.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad187/49841456/btad187.pdfDirect OA link when available
- Concepts
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Epitope, Computer science, Artificial intelligence, Perceptron, Graph, Construct (python library), Artificial neural network, Machine learning, Sequence (biology), Computational biology, Theoretical computer science, Programming language, Biology, Antibody, Genetics, ImmunologyTop concepts (fields/topics) attached by OpenAlex
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48Total citation count in OpenAlex
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2025: 28, 2024: 17, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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