Rethinking Self-Consistency in Protein Generative Models

Minji Lee, Jaeyeon Kim, Yeqing Lin, Mohammed AlQuraishi

Keywords: protein sequence-structure co-design, protein generative models, protein conformations

TL;DR: Self-consistency, the standard metric for protein generative models, systematically rejects flexible proteins.

Abstract: 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: \emph{flexibility failures}, where a broad ensemble cannot be summarized by a single refold, and \emph{folder failures}. We use ATLAS molecular dynamics trajectories to empirically analyze native structure rejections under 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.

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Submission Number: 81