Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

• 全文链接

整合蛋白质结构、序列和动力学的机器学习来预测牛肠激酶变异体的酶活性

关键词：

来源：

Journal of Chemical Information and Modeling

全文链接：

//agri.nais.net.cn/topic/downloadFile/ea17c888-fc0f-4020-a780-718bd35d51de

来源地址：

https://doi.org/10.1021/acs.jcim.3c00999

资源所属：

饲料用酶工程

类型：

学术文献

语种：

中文

原文发布日期：

2024-02-22

摘要：

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.