Ensemble-IF: Towards Protein Design for Target Conformational Populations

Designing amino acid sequences that adopt one or more target conformations is an important problem in protein engineering, with applications in antibody and GPCR design. However, many use cases require controlling not only which conformational states are accessible, but also their equilibrium populations, a property that existing inverse folding-based design methods leave unconstrained. In this work, we introduce Ensemble-IF, an inverse folding-based pipeline for designing protein sequences that exhibit target equilibrium populations. Ensemble-IF combines pretrained inverse folding models with machine-learned conformational ensemble generation, scoring sequence-structure compatibility across sampled conformations to identify mutations that directionally reshape the conformational landscape. We validate designs with molecular dynamics simulations on events defined from reference trajectories, achieving an over 90% reduction in mean target-band distance error relative to baselines while maintaining structural integrity.