Deep-learning based bioactive peptides generation and screening against Xanthine oxidase

Abstract

AbstractIn our previous work, we have developed LSTM_Pep to generatede novopotential active peptides by finetuning with known active peptides and developed DeepPep to effectively identify protein-peptide interaction. Here, we have combined LSTM_Pep and DeepPep to successfully obtained an activede novopeptide (ARG-ALA-PRO-GLU) of Xanthine oxidase (XOD) with IC50 value of 3.76mg/mL, and XOD inhibitory activity of 64.32%. Consistent with the experiment result, the peptide ARG-ALA-PRO-GLU has the highest DeepPep score, this strongly supports that we can generatede novopotential active peptides by finetune training LSTM_Pep over some known active peptides and identify those active peptides by DeepPep effectively. Our work sheds light on the development of deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discoverde novobioactive peptides that can bind to a particular target.