Spectral Graph Features for Reference-free RNA 3D Quality Assessment.

MOTIVATION: Existing RNA 3D structure quality assessment (QA) methods rely on local geometric descriptors or statistical potentials that evaluate atomic-level contacts but are blind to global topological coherence. This creates a critical failure mode-structures that are "locally correct but globally wrong"-where well-formed local helices mask misplaced domains and incorrect overall packing.

RESULTS

We introduce SpecRNA-QA, a lightweight RNA QA method based on multiscale graph-Laplacian features of inter-nucleotide contact networks. In CASP16 leave-one-out cross-validation, it achieves median per-target Spearman ρ = 0.69 (target-clustered bootstrap 95% CI [0.64,0.73]) versus 0.47 for an internal geometry baseline-a +0.22 gap that is significant at p = 1.2 × 10-10 (one-sided Wilcoxon signed-rank) and reflects a per-target win rate of 93%. The gain is concentrated on large, multi-domain RNAs, where global coherence is poorly captured by local descriptors. In a contextual comparison with established statistical potentials, local energy-based scores remain strongest on compact RNAs, while SpecRNA-QA yields the strongest signal we observed on targets longer than 200 nt; within the single-threaded runtime budget used here, the strongest local-energy comparator, rsRNASP, timed out on 22 of 26 large targets, and we report an explicit paired head-to-head on the four commonly scored targets in Section 4.2. A training-free heuristic variant further shows that the spectral prior carries intrinsic quality information even in the absence of labeled QA data.