Does Direct Coupling Analysis of epistasis in allosteric mechanical materials (arXiv:1811.10480) bridge physical-learning networks to protein-sequence coevolution?

A saved-but-not-followed hook from the physical-learning / allostery hop capture (2026-07-11): "Direct Coupling Analysis of epistasis in allosteric materials" (arXiv:1811.10480). Direct Coupling Analysis (DCA) is a statistical method used on real protein families to infer contacting/coevolving residue pairs from sequence alignments. Applying it to engineered allosteric mechanical materials would connect the vault's physical-learning cluster to protein biology from the sequence side, complementing the mechanics side already captured.

Why it matters. The existing physics↔biology bridge in the vault runs through mechanics: allostery is cheap to engineer in disordered networks (claim-removing-one-percent-of-bonds-makes-a-random-network-allosteric) and a trained network's function lives in its soft modes (claim-physical-networks-become-what-they-learn-soft-modes). A DCA/epistasis result would close the loop from the coevolution/statistical-genetics direction — showing that the same networks exhibit epistatic coupling structure of the kind DCA was built to read in proteins. That is a genuinely new thread, not a verification of an existing note.

What I'd need to do. Read arXiv:1811.10480; establish what "epistasis" means for a mechanical network, whether DCA recovers the designed allosteric couplings, and whether the authors claim any correspondence to protein-family coevolution.

Related saved hook (parked here, not its own question yet): Kuramoto-oscillator implementations of equilibrium propagation — a hardware/mechanism zoom-in kept for a future hardware-focused chain, not this biology bridge.

Candidate next move. A dedicated hop chain rather than a promotion follow-up.

Progress log

• 2026-07-18 — Answered by the promotion of the 2026-07-15 DCA-epistasis capture. claim-bravi-2020-applies-dca-to-evolved-allosteric-networks-as-synthetic-msa establishes that the paper builds the bridge itself (evolved allosteric networks cast as a synthetic protein MSA, DCA run directly on it); claim-dca-underestimates-long-range-epistasis-in-allosteric-materials and claim-dca-predicts-mutation-costs-but-poor-generative-model-of-allostery settle what DCA recovers and where it fails; claim-pdz-dca-couplings-track-short-range-epistasis-more-than-long-range is the explicit correspondence to real protein-family coevolution (PDZ deep-mutational scan). claim-bravi-allosteric-networks-are-evolved-not-coupled-learning settles the scope caveat: these are Monte-Carlo-evolved networks, so the bridge holds for 'physical-learning' read broadly (evolved/optimized mechanical networks) but not for the narrow coupled-learning sense. What settled it: the paper does both halves the question asked — DCA-on-networks and a real-protein correspondence — with Tier-1 quotes throughout.