PISAD: De novo peptide design for target protein with iterative stochastic searching algorithm and docking assessment

Rapid identification of peptides that bind specifically to a target protein is essential for disease diagnostics and drug development. However, de novo design of peptides without any prior structural knowledge remains a long-standing challenge. Herein, we present Peptide Iterative design with Stochastic Algorithm and Docking (PISAD), a de novo peptide design method, which combines an iterative stochastic searching algorithm with docking assessment. The searching algorithm simulates the evolution of peptide sequences by introducing mutations and crossovers iteratively. After every round of evolution, the peptide sequences undergo docking assessments with the target protein based on the structural prediction of AlphaFold2. We demonstrated PISAD's efficacy by designing peptides targeting four proteins, namely ARF6, ARF1, TGF-β1, and IL-6. For each target, PISAD managed to output peptides with ideal binding affinity within only four iterations of evolutions, and no more than 1250 sequences were assessed. Particularly, the best-performing peptide achieved a K_D value of 3.4 nM with ARF6, which has been further experimentally validated. These results demonstrate the efficiency and accuracy of PISAD, which may serve as a universal tool for rapid de novo design of peptides targeting specific proteins.