One model has only ever read DNA, the other only proteins. Train a translator between them and they turn out to describe the same biology — but only their chemistry can be pushed.
A gene is a recipe and a protein is the dish. Two of today's large biological models each learned one half of that relationship and never saw the other: Evo 2 reads raw genome sequence, ESM-C reads amino-acid chains. This work tested whether they nevertheless built the same internal picture of protein biology, and whether that picture can be used to control them.
Start with the sharpest version of the question. Pick one residue in one gene and change the DNA underneath it, in one of two ways. A silent edit swaps a DNA letter but leaves the amino acid identical — the recipe is rewritten, the dish is unchanged. A meaningful edit changes the amino acid too. Then watch where each model's internal representation of that residue moves.
The asymmetry is the point. For a silent edit the protein model does not move at all, because its input genuinely has not changed — that zero is structural, not a discovery. The DNA model does move, since its input changed; but 85.60% of that motion runs sideways to the direction that carries protein meaning. It notices the edit and files it somewhere that does not disturb the protein interpretation.
The two models agree about protein biology in a way that goes beyond coincidence: their shared features fire on the same annotations, and the shared space behaves like a coordinate system for protein meaning. But that agreement is recognition, not control. Of everything tested, only continuous chemistry — how oily or how charged a residue is — moved both models when it was pushed. The rest are read-outs.
Any single model's internal structure might be an artifact of how it was built: its architecture, its training data, its tokenizer. When two models trained on opposite sides of the same biological relationship converge on the same organization, that organization becomes a candidate for a property of the biology rather than a quirk of one network. It also has a practical edge: a structure that survives translation between a genome model and a protein model is a structure you can look for in the next model, or use to compare them.
Both models were read out through a shared dictionary of features — think of it as a vocabulary of recurring patterns, learned so that the same word can describe activity in either model. The design choice that mattered was the training objective. A dictionary trained only to compress each model's activity faithfully learns two vocabularies that happen to sit side by side. So the objective here paid off only when one model's feature activity genuinely predicted the other's.
That distinction is not subtle. Scored on 431,802 held-out residues from genes that never appeared in training, the cross-prediction dictionary predicts protein-model activity from DNA-model activity at 0.3177, where a reconstruction-only dictionary manages 0.0145 — essentially nothing.
The harder reverse direction makes sense. DNA carries information the protein simply does not have — which of several synonymous codons was used, where the exon boundaries fall — so predicting genome-model activity from protein-model activity has a lower ceiling built into it.
A feature counting as "shared" by the objective above could still be shared for uninteresting reasons. The check is semantic: take a feature, find what it fires on inside the DNA model, find what it fires on inside the protein model, and ask whether those are the same annotation — a transmembrane helix, a disulfide-rich domain, a signal peptide, a beta strand.
The shared vocabulary also names biology the reference labels had missed. Of 100 shared features with no matching annotation in the reference set, 42 turned out to correspond to a real, externally documented protein concept — collagen's Gly-X-Y triple-helix repeat, the glycine hinge between ankyrin repeats, the coenzyme-binding fold of a dehydrogenase family — when checked against outside databases such as Pfam and InterPro. 30 of those fire across several unrelated genes, so they describe a concept rather than memorizing one protein. The same pipeline run on shuffled labels validates nothing at all.
Reading a representation and steering it are different capabilities, and the difference decides whether a shared feature is a scientific instrument or just a nice description. The test pushes directly on a shared feature inside one model to see whether the behavior it supposedly encodes changes, then does the same in the other model. A shared feature that is really a shared control should move both.
Six shared amino-acid features were pushed this way, each with a matched random direction as the control. None of them raised its own amino acid in both models. The two that clearly moved the protein model, features for glycine and proline, moved the DNA model in the opposite direction.
Chemistry behaves differently, and the transfer is what makes the case. A hydrophobicity direction fitted inside one model, then carried over and applied unchanged inside the other, still steers it.
That carried-over direction is cosine 0.9945 and 0.9946 to the direction each model would have produced natively. The two models are not merely correlated about oiliness; they use very nearly the same vector for it.
One caveat keeps that from being eight separate control knobs. Eight biochemical properties were mapped this way, and they are not independent axes: pushing any one of them measurably shifts 54 of the other 56 property readings. Increasing hydrophobicity alone shifts polarity by -1.039, sheet propensity by 1.49 and flexibility by -0.9835. What transfers is one correlated chemical subspace, not a mixing desk.
The natural application is clinical: deciding whether a mutation is harmful. Using only the shared features as input, a simple classifier was asked to separate 43,258 human variants labeled pathogenic or benign in ClinVar, spanning 6,434 genes.
The shared features are genuinely useful here, and not the best tool available. Two things temper that further. This measurement is taken near the protein model's output, where features are closest to its own pathogenicity prediction, and the shared bank there is the smaller partition — 2,709 features shared against 2,023 private to the protein model, 1,764 private to the DNA model and 9,888 assigned to neither. And much of what the shared disease features read is evolutionary conservation: their scores track a standard conservation score at correlation 0.735, and when the top shared features were described one by one, 6 of eighteen were labeled as reading conservation rather than a specific mechanism.
Return to the explorer at the top, and ask what the movement means rather than how big it is. Three properties, each measured against shuffled controls, say the shared space behaves like a coordinate system for protein meaning.
First, size tracks consequence: a meaningful edit displaces the representation 2.758 times as far as a silent one (11.52 against 4.175 in median). Second, the two models move the same way.
Third, and most tellingly, the movement points somewhere specific. Each of the twenty amino acids occupies its own region of the shared space. When a residue is mutated, the direction it travels lands nearest the region of the amino acid it became.
That is a strong statement about the geometry, and it is also the most conservative of the findings: the amino-acid organization it describes follows largely from structure established earlier in this work. It confirms the picture rather than revealing a new mechanism.
A DNA model and a protein model, trained on opposite halves of the relationship between genes and proteins, can be shown to hold the same internal picture of protein biology — legible in the same annotations, organized as a usable coordinate system, and rich enough to name real biology the reference labels had missed. That agreement does not come with a control panel. Where it was tested directly, only chemistry moved both models.
The honest limits:
The next question this raises is a comparison, not a refinement. If a shared chemical subspace is what genuinely transfers between a genome model and a protein model, then it is the thing to look for first in the next pair — and the honest test of the shared picture is whether a third model, trained differently again, lands in the same place.