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PreprintCited by 137Machine Learning in BioinformaticsProtein Structure and DynamicsComputational Drug Discovery Methods

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AlphaFold2-aware protein-DNA binding site prediction using graph transformer

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Yuan, Qianmu, Chen, Sheng, Rao, Jiahua, Zheng, Shuangjia, Zhao, Huiying, Yang, Yuedong·2021·Briefings in Bioinformatics
DOI 10.1101/2021.08.25.457661

Preprint — published as 10.1093/bib/bbab564 · Paper page

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Abstract

Protein-DNA interactions play crucial roles in the biological systems, and identifying protein-DNA binding sites is the first step for mechanistic understanding of various biological activities (such as transcription and repair) and designing novel drugs. How to accurately identify DNA-binding residues from only protein sequence remains a challenging task. Currently, most existing sequence-based methods only consider contextual features of the sequential neighbors, which are limited to capture spatial information. Based on the recent breakthrough in protein structure prediction by AlphaFold2, we propose an accurate predictor, GraphSite, for identifying DNA-binding residues based on the structural models predicted by AlphaFold2. Here, we convert the binding site prediction problem into a graph node classification task and employ a transformer-based variant model to take the protein structural information into account. By leveraging predicted protein structures and graph transformer, GraphSite substantially improves over the latest sequence-based and structure-based methods. The algorithm is further confirmed on the independent test set of 181 proteins, where GraphSite surpasses the state-of-the-art structure-based method by 16.4% in AUPR and 11.2% in MCC, respectively. We provide the datasets, the predicted structures, and the source codes along with the pre-trained models of GraphSite at https://github.com/biomed-AI/GraphSite . The GraphSite web server is freely available at https://biomed.nscc-gz.cn/apps/GraphSite .

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Metadata

Authors
Yuan, Q., Chen, S., Rao, J., Zheng, S., Zhao, H., & Yang, Y.
Affiliations
Sun Yat-sen University (CN)
Corresponding author
Yang, Yuedong
ORCID
Venue
Briefings in Bioinformatics
Publisher
openRxiv
Published
2021-08-26
Volume
23
Issue
2
ISSN
1467-5463 (ISSN-L)
DOI
10.1101/2021.08.25.457661
OpenAlex
W4205989901
Cited by
137
References
1
Citations by year
2026: 22 · 2025: 37 · 2024: 45 · 2023: 22 · 2022: 11
FWCI
6.12
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Briefings in Bioinformatics (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Griffith Research Online (submittedVersion, other-oa)
Funders
National Natural Science Foundation of China (61,772,566, 2020YFB0204803, 2019B020228001, 62041209)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords
DNA-binding residues, AlphaFold2, graph transformer, node classification, protein structure-based prediction, protein-DNA interactions, web server
MeSH terms
Protein Domains, Algorithms, Binding Sites, DNA, Protein Binding, Proteins

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