Generative β-hairpin design using a residue-based physicochemical property landscape.

ABSTRACT: De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach base

SUBMITTER: Satalkar V 

PROVIDER: S-EPMC11393682 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature