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

Bioinformatics. 2023 Jan 1;39(1):btad036. doi: 10.1093/bioinformatics/btad036.


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 targets or activate cellular receptors. There is a significant agreement between the helix structures generated with HelixGAN and PEP-FOLD, a well-known de novo approach for predicting peptide structures from amino acid sequences. HelixGAN outperformed RosettaDesign, and our previously developed structural similarity method to generate D-peptides matching a set of given hotspots in a known L-peptide. As proof of concept, we designed a novel D-GLP1_1 analog that matches the conformations of critical hotspots for the GLP1 function. MD simulations revealed a stable binding mode of the D-GLP1_1 analog coupled to the GLP1 receptor. This novel D-peptide analog is more stable than our previous D-GLP1 design along the MD simulations. We envision HelixGAN as a critical tool for designing novel bioactive peptides with specific properties in the early stages of drug discovery.

Availability and implementation: https://github.com/xxiexuezhi/helix_gan.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Deep Learning*
  • Peptides / chemistry
  • Protein Conformation, alpha-Helical
  • Protein Structure, Secondary
  • Proteins


  • Peptides
  • Proteins