Designed Proteins Are Leaving the Lab. The Validation Gap Is Widening.

Sofia Reyes — Tue, 04 Aug 2026 00:00:00 GMT

The design half of protein engineering has become extraordinarily fast. A group with a target structure and reasonable compute can generate thousands of candidate binders in a week, and a meaningful fraction of them will fold as predicted. The characterisation half has not sped up at all. Expressing, purifying, and measuring a candidate still takes weeks per construct, and the good measurements take longer than that.

That asymmetry is not a temporary inconvenience. It is starting to shape what the field publishes and what it believes.

Where the ratio actually sits

Groups we spoke to described design-to-validation ratios that would have been unthinkable five years ago. One lab generated roughly 4,000 candidate designs against a single target over two months and experimentally characterised 47 of them.

The 47 were not a random sample. They were selected by computational filters, which is reasonable practice and also the source of the problem. The published result describes the performance of the selected subset. It does not describe the performance of the design method, because the selection step is doing work that is not being measured.

Filtering is a hidden model. Predicted binding affinity, predicted solubility, and structural plausibility scores all encode assumptions. Reporting success only among survivors measures the filters as much as the generator.
Negative results stay unpublished. A design campaign that yields nothing is rarely written up, so the field’s estimate of base rates is drawn from campaigns that worked.
Characterisation depth varies enormously. A binding assay is not a functional assay, and a functional assay in vitro is not activity in a cell.

We can now design faster than we can be wrong at a measurable rate. That should worry people more than it does.

The measurements that are being skipped

The specific gap most often cited by structural biologists is not affinity. It is specificity and stability under realistic conditions.

Affinity for the intended target is comparatively easy to measure and is almost always reported. Off-target binding across a realistic proteome is expensive and is usually not. Thermal and proteolytic stability get reported inconsistently. Aggregation behaviour at concentration, which determines whether a molecule is developable at all, appears in a minority of papers.

This produces a literature in which designs look excellent on the axis that is cheap to measure. Groups working on therapeutic applications are blunt about the consequence: a substantial share of published designed binders fail on properties that were never characterised in the original report.

What would close the gap

Three interventions came up repeatedly, and none of them requires a methodological breakthrough.

The first is reporting the denominator. State how many designs were generated, what filters were applied, and how many survived each stage. This is a change in convention rather than in capability, and it would immediately make published success rates interpretable.

The second is standardised minimum characterisation. A short, agreed panel covering specificity, stability, and aggregation, reported for every candidate that gets published, would eliminate most of the current inconsistency. Several groups are pushing for this through journal policy rather than waiting for consensus.

The third is investment in throughput on the wet side. Automated expression and purification exists and works. It is unglamorous, it does not produce papers on its own, and it is chronically underfunded relative to the compute budgets on the design side.

Why this is a familiar failure

The pattern here is not specific to protein design. It is the standard signature of a field where one half of the loop got cheap and the other did not: apparent progress accelerates, published success rates rise, and the base rate quietly becomes unknowable.

Medicine has run this experiment already, which is why trial registration exists at all. As we reported on the trial reporting gap, even mandatory registration only partly solved it. The design field has the advantage of being able to adopt the convention before the credibility problem becomes acute rather than after.

A Decade of Sky Surveys Just Rewrote the Supernova Rate

Sofia Reyes — Thu, 16 Jul 2026 00:00:00 GMT

Ten years of automated transient surveys have produced a catalogue large enough to do something the field could not previously do well: measure how often stars actually explode, rather than inferring it from a handful of nearby events and a lot of modelling. The measured rate of core-collapse supernovae has come out higher than the standard prediction, and the excess is concentrated in dusty, star-forming galaxies.

That last detail is what makes the result interesting rather than merely surprising. The discrepancy appears where the observational bias was always expected to be worst.

What changed methodologically

Earlier rate estimates were built from small samples with heterogeneous selection. An event was found because someone was looking at that galaxy, which makes the sample a description of observing habits as much as of the universe.

Automated wide-field surveys with consistent cadence removed most of that problem. The survey observes the same footprint on a fixed schedule regardless of what is interesting, so the selection function is computable rather than anecdotal.

Uniform cadence means the probability of catching an event of known duration can be calculated, not estimated.
Consistent depth allows a completeness correction that is a function of distance and brightness rather than of who was on shift.
Sample size finally permits splitting by host galaxy type, which is where the signal turned out to live.

The dust problem, stated plainly

Core-collapse supernovae come from short-lived massive stars, so they occur in regions that are actively forming stars. Those regions are dusty. Dust absorbs and reddens optical light, so a fraction of these events have always been expected to be missed or misclassified in optical surveys.

The size of that fraction was the open question. The new catalogues, combined with infrared follow-up on a subsample, put it substantially higher than most models assumed. In the dustiest host galaxies the inferred correction is large enough to account for most of the gap between predicted and observed rates.

The universe was not producing more supernovae than we thought. We were failing to see them in exactly the places our own models told us we would.

Why the star-formation rate is implicated

The supernova rate and the cosmic star-formation history are tied together. Massive stars form, live briefly, and explode, so a measured explosion rate constrains how many massive stars formed a few million years earlier.

If the supernova rate is higher than predicted, either more massive stars are forming than the star-formation rate implies, or the relationship between the two is not what the standard assumptions encode. Both possibilities have consequences well beyond supernova statistics, because star-formation history feeds into estimates of metal enrichment, dust production, and the ultraviolet background.

The dust-obscuration explanation is the least disruptive of the available options, and it currently has the best support. It also has a testable prediction: infrared and radio surveys, which are far less affected by dust, should recover the missing events. Early results from radio follow-up are consistent with that, though the samples remain small.

What the result does not say

It does not indicate a problem with stellar evolution theory. The models of how massive stars end are not in question here; what is in question is the completeness of optical censuses of them.

It also does not resolve the related question of how many core-collapse events fail to produce a bright explosion at all. Failed supernovae, where a massive star collapses without a luminous transient, remain difficult to constrain and would push in the opposite direction. The current work brackets the problem better than before without closing it.

The broader methodological lesson is the one worth carrying forward, and it echoes what we found reporting on the validation gap in protein design: when a field’s measurements are drawn from a selected sample, the first thing to characterise is the selection.

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