Exploring the Conformational Landscape of Adenylate Kinase and Beyond: A Benchmark of Protein Folding Models

Abstract

Protein folding models have revolutionized structure prediction but struggle to capture conformational flexibility. Recent studies perturb inputs or parameters to sample alternative conformations, while diffusion-based approaches generate conformational ensembles directly. Although the former have been benchmarked to some extent, the latter have yet to be evaluated, and sub-domain dynamics validation remains limited. Here, we present a systematic benchmark of nine methods across 20 monomeric proteins with active and inactive states. We extend the pairwise aligned error metric to ensembles and reveal that protein identity exerts a non-negligible influence on model performance. Focusing on Adenylate Kinase, a well-studied enzyme with extensive molecular dynamics (MD) data, we find that Chai-1 performs the best in recovering known conformations, identifying mobile regions, and capturing transition trajectories. These results highlight the potential of generative models as efficient alternatives to MD for exploring protein conformational dynamics and provide a rigorous benchmark for dynamic structure prediction.