Geometric deep learning of protein–DNA binding specificity

• 全文链接

蛋白质-DNA结合特异性的几何深度学习

关键词：

来源：

Nature Methods

全文链接：

//agri.nais.net.cn/topic/downloadFile/a8c5f054-d113-42f6-8a84-8de74856b088

来源地址：

https://www.nature.com/articles/s41592-024-02372-w

资源所属：

饲料用酶工程

类型：

学术文献

语种：

英语

原文发布日期：

2024-08-05

摘要：

Predicting protein–DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein–DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding specificity, to a wide range of DNA sequences. This information is not directly accessible in a single structure. Here, to access this information, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity from protein–DNA structure. DeepPBS can be applied to experimental or predicted structures. Interpretable protein heavy atom importance scores for interface residues can be extracted. When aggregated at the protein residue level, these scores are validated through mutagenesis experiments. Applied to designed proteins targeting specific DNA sequences, DeepPBS was demonstrated to predict experimentally measured binding specificity. DeepPBS offers a foundation for machine-aided studies that advance our understanding of molecular interactions and guide experimental designs and synthetic biology.