PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design

• URL: http://arxiv.org/abs/2506.11420v2
• Date: Fri, 31 Oct 2025 00:03:51 GMT
• Title: PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design
• Authors: Zhenqiao Song, Tiaoxiao Li, Lei Li, Martin Renqiang Min,
• Abstract summary: PPDiff is a diffusion model to jointly design the sequence and structure of binders for arbitrary protein targets.<n> PPDiff builds upon our developed Sequence Structure Inter Network with Causal attention layers.<n>The model is pretrained on PPBench and finetuned on two real-world applications.
• Score: 20.033392739225658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing protein-binding proteins with high affinity is critical in biomedical research and biotechnology. Despite recent advancements targeting specific proteins, the ability to create high-affinity binders for arbitrary protein targets on demand, without extensive rounds of wet-lab testing, remains a significant challenge. Here, we introduce PPDiff, a diffusion model to jointly design the sequence and structure of binders for arbitrary protein targets in a non-autoregressive manner. PPDiffbuilds upon our developed Sequence Structure Interleaving Network with Causal attention layers (SSINC), which integrates interleaved self-attention layers to capture global amino acid correlations, k-nearest neighbor (kNN) equivariant graph layers to model local interactions in three-dimensional (3D) space, and causal attention layers to simplify the intricate interdependencies within the protein sequence. To assess PPDiff, we curate PPBench, a general protein-protein complex dataset comprising 706,360 complexes from the Protein Data Bank (PDB). The model is pretrained on PPBenchand finetuned on two real-world applications: target-protein mini-binder complex design and antigen-antibody complex design. PPDiffconsistently surpasses baseline methods, achieving success rates of 50.00%, 23.16%, and 16.89% for the pretraining task and the two downstream applications, respectively. The code, data and models are available at https://github.com/JocelynSong/PPDiff.

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