Epitope-Conditioned Nanobody CDR Design via Retrieval-Augmented Protein Language Models

De novo design of nanobody complementarity-determining regions (CDRs) targeting user-specified epitopes is crucial yet remains computationally challenging. Current approaches rely on either (i) diffusion-based structural sampling that requires thousands of designs per target, (ii) gradient-based hallucination through frozen structure-prediction networks, or (iii) all-atom generative systems requiring 3D structural input and high computational cost. A fundamental limitation of these methods is their dependence on high-quality antigen structures, either experimental or computationally predicted, restricting applicability to the small fraction of therapeutic targets with available structural data. We present EpiRAG-PBind42 (Epitope-conditioned Retrieval-Augmented Generation with PBind42), a sequence-only framework for epitope-conditioned VHH/nanobody CDR generation. Our approach builds on PBind42, a Prot42-derived autoregressive binder generator instruction-tuned on DIPS-Plus protein–protein interaction pairs. EpiRAG-PBind42 adds three key innovations: (1) target-conditioned binder generation from sequence prompts, (2) retrieval-augmented latent-space decoding using hidden-state transition datastores derived from strict VHH/nanobody–antigen donor pairs, and (3) multi-agent filtering via four specialized evaluators (Structural, BLI, Interface, and Expression) that assess structural confidence, sequence naturalness, developability-adjacent liabilities, and interface energetics. Because EpiRAG-PBind42 operates on sequence input alone, it enables design on targets lacking high-quality structural models, including intrinsically disordered regions, flexible multi-domain proteins, and membrane-bound targets inaccessible to structure-dependent methods. We evaluate EpiRAG-PBind42 on five therapeutic antigen targets spanning diverse therapeutic areas: infectious disease, oncology, inflammation, and immunology.