TwinMP: Adapting Inverse-Folding Models for Multi-Conformation Design and Mutation-Effect Prediction

Protein inverse folding models learn strong priors from individual protein backbones, but typically overlook the conformational variability that is often essential to protein function. Existing multi-conformation adaptations either pool structural representations, potentially obscuring conformation-specific constraints, or process each conformation independently and combine their logits only during decoding, without information exchange between structural states. We introduce **Twin**-Stream with **M**essage **P**assing **(TwinMP)**, a model-agnostic post-training framework that adapts pretrained single-conformation inverse folding models to dual-conformation learning. TwinMP maintains two streams that separately preserve the structure-to-sequence mapping of each conformation while introducing bidirectional message passing between their hidden representations before sequence prediction. This design retains the single-state knowledge acquired during pretraining while enabling explicit exchange and reconciliation of shared and state-specific structural constraints. We instantiate TwinMP on ProteinReasoner, ESM-IF, and ProteinMPNN. Across all three model families, TwinMP improves mean amino-acid recovery over pooling and Logit mean, with backbone-dependent gains from message passing. Moreover, TwinMP with AF2 and MD-derived structural inputs achieves an official ProteinGym score of 0.486, compared with 0.473 for the single-conformation baseline. These results support TwinMP as a practical framework for adapting pretrained inverse-folding models to multi-conformation design and mutation-effect prediction.