Multimodal Diffusion over Sequence and SE(3) Manifolds for Protein-Protein Complex Design

Designing high-affinity target-specific protein binders is critical to modern therapeutics. Existing protein generation methods are unsatisfactory on this task because many of their designs fail to achieve joint geometric and physicochemical complementarity at the binding interfaces. To address this, we propose PPFrame, a physicochemically enhanced multimodal diffusion method for protein-protein complex design. The core idea is to co-design a target-conditional protein sequence and structure by jointly diffusing over the discrete sequence space and the continuous $SE(3)$ structural manifold. We also introduce IPAFormer to serve as the core network inside the diffusion. IPAFormer's architectural insight is its interleaving of geometry-based invariant point attention and global sequence self-attention. We curate PPIBench, a dataset of 602,364 protein-protein complexes across diverse target scaffolds from the Protein Data Bank (PDB). Our PPFrame is first pretrained on PPIBench and then finetuned on two important real-world tasks: mini-binder design and antibody design. PPFrame consistently outperforms the best baselines across the three tasks, improving design success rates by 17.8%, 8.0%, and 1.9%, respectively. Notably, PPFrame generates a candidate broadly neutralizing antibody (bNAb) against HIV-1 with an ipTM of 0.81 and a binding energy of -52.1, highlighting its strong utility for real-world biotherapeutic discovery.