Geometric Intuition Enables Resource-Efficient Screening for β-Barrel-Like Folds in Viral Structural Dark Matter

Predicted protein structures now enable fold discovery beyond the reach of sequence homology, but scalable and interpretable methods remain limited. We present Cooper-Beta, a resource-efficient, chain-resolved framework for detecting β-barrel-like folds from strand connectivity. Cooper-Beta converts DSSP-assigned physical β-strands into an adjacency graph and evaluates four measurements using calibrated direct rules or logistic regression. On a grouped held-out benchmark of 466 positive and 1063 negative chains, the direct classifier achieved 87.77% recall and 97.65% specificity, while the learned classifier reached 94.42% recall and 97.08% specificity. A separate evaluation of 114 manually classified CATH Class II non-barrels yielded specificities of 95.61% and 92.98%, respectively. Both classifiers retained prediction-flip rates below 3% through 1.0 Å Gaussian Cα perturbation, with stronger learned-model sensitivity at higher noise. Screening 351 242 structures from the Big Fantastic Virus Database yielded 5803 joint-positive candidates; sequential Swiss-Prot and UniRef50 searches identified 1568 sequence-available candidates without a passing Swiss-Prot or UniRef50 match. Cooper-Beta provides an interpretable route from predicted structural repositories to β-barrel-like candidates for experimental follow-up.