bioRxiv (May 1): Generative AI Designs Peptides with Custom Secondary Structure Motifs Using Reduced Amino-Acid Alphabets
A May 1 bioRxiv preprint introduces a generative AI protein-design model trained on hundreds of thousands of structures from the RCSB PDB to produce peptides with custom secondary structure motifs while operating on reduced amino-acid alphabets. The work targets a real bottleneck in cyclic peptide drug development — generating sequences that fold into specified secondary-structure scaffolds without exhausting the full 20-letter design space, which lowers the barrier for synthesis and downstream maturation. It joins the recent University of Utah PapB enzymatic cyclization paper, the Nature Communications few-shot AI Acinetobacter pipeline, and Profluent's recombinase work as part of the broader AI-peptide-design wave through April–May 2026.