Rethinking Self-Consistency in Protein Generative Models

Self-consistency is the standard metric for evaluating structural plausibility in protein generative models: a generated sequence–structure pair is accepted if an independent folding model refolds the sequence to the generated structure within a fixed RMSD threshold. However, self-consistency rejects 44% of native sequence–structure pairs, a gap previously overlooked as a folding model limitation. In this paper, we connect self-consistency to the biophysical ideal of structural plausibility–sufficiently low free energy in that protein's equilibrium ensemble–which makes two failure modes visible: *flexibility failures*, where a broad ensemble cannot be summarized by a single refold, and *folder failures* . We use ATLAS molecular dynamics trajectories to empirically analyze native structure rejections through this lens, and find that both failure modes are enriched among flexible proteins. We propose ensemble self-consistency using conformational ensemble predictors, improving native protein recall across the flexibility spectrum and rescuing co-design generations previously rejected, without compromising specificity on a Rosetta decoy dataset. Since flexibility is often central to protein function, a metric that penalizes it misdirects generative model development; ensemble self-consistency offers a more faithful framework, and our formalization and diagnostics let it evolve as structure prediction models improve.