Structure-Contrastive Reveal: Guiding Generation Order in Protein Inverse Folding

Protein inverse folding aims to design amino-acid sequences compatible with a given protein backbone structure. Many recent models generate sequences iteratively, predicting amino acids at unresolved positions and filling only a subset of these positions at each decoding step. Decoded residues become context for later predictions. Thus, the order in which positions are selected can affect the final generation trajectory. Existing position-selection policies usually prioritize positions where the model is most confident. However, high confidence does not necessarily mean that a prediction is supported by the target structure. Since inverse folding is conditioned on a target structure, identifying predictions that actually rely on structural information is pivotal. Intuitively, if the predicted amino acid at a position is supported by the structure, weakening the structural information should reduce the probability assigned to that prediction. Motivated by this intuition, we explore comparing the probability of the same predicted amino acid under the original and weakened structural conditions, using the probability decrease as a proxy for structural dependence. In this work, we introduce Structure-Contrastive Reveal (SCR), which combines this probability decrease with predictive confidence to decide which residue positions to decode first, while using the predictions under the original structural condition for the selected positions. Extensive experiments show that SCR consistently improves structural quality across different inverse-folding models and further enhances the state-of-the-art baseline under matched search configurations. An extension to trajectory-conditioned motion generation further suggests that condition-dependent reveal selection can generalize beyond protein inverse folding.