ConforFlux: Particle-Guided Trunk Repulsion for Diverse Protein Conformations

Deep-learning protein structure predictors achieve near-experimental accuracy on individual folds, yet their default inference samples concentrate around a single dominant conformation. We introduce ConforFlux, an inference-time procedure for Boltz-2 that couples $M$ structure-prediction trajectories through a pairwise C$\alpha$-RMSD repulsion gradient on the trunk's single and pair embeddings. Because the trunk conditions every block of the diffusion module, this update propagates to every subsequent denoising step. On four conformational-change categories, ConforFlux improves the per-state success rate over Default Boltz-2 by 3–17 percentage points while preserving Default-comparable physical quality. On twelve transporter pairs with at least one alternate-state reference released after the Boltz-2 cutoff, the $2$ Å success rate rises from $4/12$ to $9/12$. On the human dopamine transporter, ConforFlux additionally reaches the post-cutoff inward state, which $500$ Default samples never attain.