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

By Qianmu Yuan, Sheng Chen, Jiahua Rao, Shuangjia Zheng, Huiying Zhao, Yuedong Yang. 2022

109 citations recorded by the source corpus.

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 area under the precision-recall curve and 11.2% in Matthews correlation coefficient, 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.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jianzhu Ma
2. Jian Peng
3. Bonnie Berger
4. Shitong Luo
5. Sheng Wang
6. Jinbo Xu

In Molecular Biology

1. Jianzhu Ma
2. Jian Peng
3. Jinbo Xu
4. Bonnie Berger
5. Shitong Luo
6. Sheng Wang
7. Shiwang Liu
8. Siqi Sun

Citations in the corpus

• High-resolution de novo structure prediction from primary sequence (2022, background): “One of the successful pre-trained models in biology is the pre-trained protein language models, which has been demonstrated to be able to extract biologically meaningful representations for various tasks such as protein contact prediction (11–14), protein function prediction (15,16), zero-shot mutat”