De novo antioxidant peptide design via machine learning and DFT studies.

Antioxidant peptides (AOPs) are highly valued in food and pharmaceutical industries due to their significant role in human function. This study introduces a novel approach to identifying robust AOPs using a deep generative model based on sequence representation. Through filtration with a deep-learning classification model and subsequent clustering via the Butina cluster algorithm, twelve peptides (GP1-GP12) with potential antioxidant capacity were predicted.

Peptiligase, an enzyme for efficient chemo-enzymatic synthesis of aviptadil.

Increasing attention to peptides as prospective therapeutics has created a renaissance in searching for new alternatives to the current peptide synthetic approaches as well as their modification. In this context, it is necessary to develop different approaches for peptide ligation. Using enzymes as a novel strategy and powerful tool for the peptide and protein ligation has recently received a lot of attention.