  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 dataset, 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 co-design 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.
