A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy

While large-scale artificial intelligence
(AI) models for protein
structure prediction and design are advancing rapidly, the translation
of deep learning models for practical macromolecular drug development
remains limited. This investigation aims to bridge this gap by combining
cutting-edge methodologies to create a novel peptide-based PROTAC
drug development paradigm. Using ProteinMPNN and RFdiffusion, we identified
binding peptides for androgen receptor (AR) and Von Hippel-Lindau
(VHL), followed by computational modeling with Alphafold2-multimer
and ZDOCK to predict spatial interrelationships. Experimental validation
confirmed the designed peptide’s binding ability to AR and
VHL. Transdermal microneedle patching technology was seamlessly integrated
for the peptide PROTAC drug delivery in androgenic alopecia treatment.
In summary, our approach provides a generic method for generating
peptide PROTACs and offers a practical application for designing potential
therapeutic drugs for androgenetic alopecia. This showcases the potential
of interdisciplinary approaches in advancing drug development and
personalized medicine.