AUTHOR=Chen Xu , Chen Zhidong , Xu Daiyun , Lyu Yonghui , Li Yongxiao , Li Shengbin , Wang Junqing , Wang Zhe 

TITLE=De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2021.694100

DOI=10.3389/fbioe.2021.694100

ISSN=2296-4185

ABSTRACT=GPR40, one of the G protein-coupled receptors that are available to sense glucose metabolism, is an attractive target for the treatment of type II diabetes mellitus (T2DM). Despite many efforts having been made to discover small-molecule agonists, there is limited research focus on developing peptides acting as GPR40 agonists to treat T2DM. Here, we propose a novel strategy for peptide design to efficiently generate and determine potential peptide agonists against GPR40. A molecular fingerprint similarity model combined with a deep neural network and convolutional neural network was applied to predict the activity of peptides constructed by unnatural amino acids. Site-Directed Mutagenesis (SDM) further optimized the peptides to form specific favorable interactions and subsequent flexible docking showed the details of the binding mechanism between peptide and GPR40. The R-square of the machine learning model on the training set and the test set reached 0.87 and 0.75, respectively, and the five candidate peptides showed excellent performance. The strategy based on machine learning and SDM successfully searched for an optimal design with desirable activity comparable to the model agonist in phase III clinical.