EvoTilt-MPNN: Distributional Tilting for Variation-Aware Binder Design

Designed protein binders can fail as their targets evolve: a single interface point mutation can abolish binding, making escape a major failure mode in therapeutic and diagnostic applications. However, current binder-design methods condition on a single target, making their designs vulnerable to mutations. To address this, we introduce a structure-based binder-design approach in which each target-interface residue is specified by a distribution $\pi$ over likely mutations. During autoregressive design, we sample each binder residue from an inverse-folding model's conditional distribution, tilted toward residues that reduce the KL divergence between $\pi$ and the model's predicted distribution at the target interface. Memoization computes this tilt at a small additional cost. Tilting produces a consistent distributional response that transfers to held-out inverse-folding models across twenty-one targets. In silico co-folding on fifteen of these shows a small, heterogeneous, tail-concentrated improvement in worst-tail confidence across sampled target mutations, relative to an untilted ProteinMPNN baseline. Our method provides an efficient way to incorporate plausible target variation into sequence design. Because it accepts any per-residue distribution, $\pi$ can be drawn from deep mutational scanning, phylogenetic frequencies, or escape predictors, making the approach a general route to variation-aware binder design.