PLDesign: Unifies All-Atom Complex Representation and Geometry-Guided Protein Design

Modeling protein complexes requires coordinating atomic detail with cross-chain geometry, placing substantial demands on generative models of interacting systems. Autoencoder-based structural compression offers a way to separate atomic reconstruction from distribution learning, opening the possibility of modeling these coupled relationships in a compact space. We introduce PLDesign to investigate this direction through joint encoding of complete all-atom complexes. An Atom14 autoencoder learns a shared representation that preserves intrachain structure and interchain organization, while latent perturbations reveal a correspondence with decoded geometric variation. A downstream latent flow model uses this representation for unconditional backbone generation and target-conditioned binder design. Geometry-guided trajectory search further makes the latent information actionable during sampling, ranking intermediate states before coordinate decoding or external structure prediction. This search improves unconditional designability and enriches candidates in a matched computational binder screen, whose retained complexes contain locally packed interfaces with potential value for interface seed development. PLDesign connects joint complex encoding with latent-guided exploration, opening a route to studying and designing interacting molecular systems through compressed structural representations.