AcrNET: predicting anti-CRISPR with deep learning Article Swipe
Yunxiang Li
,
Yumeng Wei
,
Sheng Xu
,
Qingxiong Tan
,
Licheng Zong
,
Jiuming Wang
,
Yixuan Wang
,
Jiayang Chen
,
Liang Hong
,
Yu Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btad259
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btad259
Motivation As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance. Results On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly. Availability and implementation Web server: https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/. Training code and pre-trained model are available at.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btad259
- https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad259/50061469/btad259.pdf
- OA Status
- gold
- Cited By
- 16
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366603469
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4366603469Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioinformatics/btad259Digital Object Identifier
- Title
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AcrNET: predicting anti-CRISPR with deep learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-21Full publication date if available
- Authors
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Yunxiang Li, Yumeng Wei, Sheng Xu, Qingxiong Tan, Licheng Zong, Jiuming Wang, Yixuan Wang, Jiayang Chen, Liang Hong, Yu LiList of authors in order
- Landing page
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https://doi.org/10.1093/bioinformatics/btad259Publisher landing page
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad259/50061469/btad259.pdfDirect link to full text PDF
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YesWhether a free full text is available
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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/btad259/50061469/btad259.pdfDirect OA link when available
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CRISPR, Computer science, Artificial intelligence, Deep learning, Computational biology, Biology, Genetics, GeneTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2025: 10, 2024: 6Per-year citation counts (last 5 years)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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