Despite the exciting progress in
target-specific de novo protein binder design, peptide
binder design remains
challenging
due to the flexibility of peptide structures and the scarcity of protein-peptide
complex structure data. In this study, we curated a large synthetic
data set, referred to as PepPC-F, from the abundant protein–protein
interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an
SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide
sequences and structures. DiffPepBuilder also introduces disulfide
bonds to stabilize the generated peptide structures. We tested DiffPepBuilder
on 30 experimentally verified strong peptide binders with available
protein–peptide complex structures. DiffPepBuilder was able
to effectively recall the native structures and sequences of the peptide
ligands and to generate novel peptide binders with improved binding
free energy. We subsequently conducted de novo generation
case studies on three targets. In both the regeneration test and case
studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled
with ProteinMPNN, in terms of sequence and structure recall, interface
quality, and structural diversity. Molecular dynamics simulations
confirmed that the introduction of disulfide bonds enhanced the structural
rigidity and binding performance of the generated peptides. As a general
peptide binder de novo design tool, DiffPepBuilder
can be used to design peptide binders for given protein targets with
three-dimensional and binding site information.