Constrained Generative Design Frameworks For Computational Discovery of Target-Specific DARPin Candidates

Applying unconstrained generative protein models to fixed structural scaffolds can produce systematic design artifacts, including a "Glycine Trap" characterized by the enrichment of glycine at structurally incompatible positions. Furthermore, optimizing sequences against artificial rigid-body docking geometries induces reward-hacking and severe geometric hallucinations. In addition, the highly conserved designed ankyrin repeat protein, or DARPin, scaffold can obscure defects at the engineered binding interface, causing AlphaFold2-Multimer (AF2) to predict nonfunctional protein-target interactions with high confidence. To overcome these limitations, we developed DARPinMPNN, a scaffold-constrained computational pipeline for DARPin candidate discovery. Restricting sequence generation to a validated DARPin design space eliminated these failure modes. A state-aware chimeric multiple sequence alignment strategy was engineered and enabled AlphaFold2-Multimer (AF2) to serve as a high-throughput structural sieve, while AlphaFold 3 (AF3) provided independent structural validation of candidate binders. Using this framework, we identified mesothelin-targeting DARPin candidates with predicted structural confidences (champion ipTM = 0.83) approaching those of a structurally validated picomolar-affinity binder (G3 control, ipTM = 0.89). By revealing extensive discordance between AF2 and AF3 predictions, this work establishes a robust framework for identifying and prioritizing high-confidence DARPin candidates for experimental validation.