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

计算机科学 图形 变压器 马修斯相关系数 DNA结合位点 计算生物学 机器学习 人工智能 数据挖掘 理论计算机科学 生物 基因 遗传学 工程类 电压 基因表达 电气工程 发起人 支持向量机
作者
Qianmu Yuan,Sheng Chen,Jiahua Rao,Shuangjia Zheng,Huiying Zhao,Yuedong Yang
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:23 (2) 被引量:43
标识
DOI:10.1093/bib/bbab564
摘要

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.
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