HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

ABSTRACT:

Motivation

Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioactive peptides. Precise design of helices for binding or biological activity is still a challenging problem.

Results

Here, we present HelixGAN, the first generative adversarial network method to generate de novo left-handed and right-handed alpha-helix structures from scratch at an atomic level. We developed a gradient-based search approach in latent space to optimize the generation of novel α-helical structures by matching the exact conformations of selected hotspot residues. The designed α-helical structures can bind specific tar

SUBMITTER: Xie X 

PROVIDER: S-EPMC9887083 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature