Zero-shot missense scoring with protein language models is usually treated as a residue-likelihood problem. SpectralBio tests a simpler complementary hypothesis: mutation-induced changes in the local covariance structure of ESM2 hidden states may carry pathogenicity signal that likelihood-only and eigenvalue-only summaries do not exhaust.
Zero-shot missense scoring with protein language models is usually framed as a sequence-likelihood problem. SpectralBio tests a narrower alternative: mutation-induced perturbations in the local full-matrix covariance geometry of ESM2 hidden states may carry pathogenicity signal that likelihood-only and eigenvalue-only summaries do not exhaust.
Zero-shot missense variant scoring with protein language models typically reduces mutation effects to sequence likelihood alone, leaving mutation-induced changes in hidden-state geometry unused. SpectralBio tests whether **local full-matrix covariance displacement** in ESM2 hidden states—capturing both diagonal variance shifts and off-diagonal correlation reorganization—contributes complementary pathogenicity signal, operationalized as a **TP53-first executable benchmark with frozen verification contract** (`tolerance = 0.