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Yes—cysteine proteases can now be designed from scratch, but reliably producing a highly active enzyme remains difficult. A version-of-record study published in Nature on 14 October 2026 tested 69 computationally designed proteins: 13 were active, a 19% success rate, and the best reached a rate enhancement of up to 3 × 107 over the uncatalyzed reaction. The proteins used newly generated folds rather than modified natural protease scaffolds, and selected designs cleaved intended targets in mammalian cells.
What the study designed
The work by Hojae Choi, Magnus S. Bauer, Brian Coventry and co-authors addresses a long-standing protein-engineering challenge: making an enzyme that breaks peptide-backbone amide bonds without starting from a natural protease structure. Those bonds are unusually resistant to hydrolysis in neutral water, so a useful catalyst must both activate a nucleophile and hold the substrate in a reactive arrangement.
The reported proteins use an activated cysteine nucleophile. Supporting catalytic residues, including a histidine system, and a sequence-dependent substrate-binding environment were placed into computationally generated scaffolds. Their folds are substantially different from natural proteases; the reported TM scores were below 0.50, indicating that the designs are not simply remodeled versions of familiar protease architectures.
Results at a glance
| Measure | Result | What it means |
|---|---|---|
| Designs experimentally tested | 69 | The complete tested set reported in the study |
| Active designs | 13 (19%) | Activity was measurable for fewer than one in five tested proteins |
| Best rate enhancement | Up to 3 × 107 | Improvement relative to the uncatalyzed background reaction |
| Structures solved | 6 | Crystal structures were compared directly with computational models |
| Structural agreement | Cα RMSD below 1.2 Å | The six solved structures were close to their intended designs |
| Fold similarity to natural proteases | TM score below 0.50 | The active proteins use distinctly different overall folds |
How the de novo design pipeline works
The workflow repeatedly proposes, sequences, predicts and filters proteins before any laboratory test. Its purpose is to make the catalytic geometry plausible while avoiding dependence on an existing protease scaffold.
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1. Generate a catalytic backbone
RFD2-MI was used to generate protein backbones and active-site geometries. The design objective was not merely to make a stable globular protein, but to create a pocket that places an activated cysteine and its supporting residues in positions suitable for attacking a peptide bond.
2. Design amino-acid sequences
ProteinMPNN assigned sequences to the generated backbones. Sequence design must satisfy two competing requirements: preserve the intended active-site arrangement and provide enough overall stability for the new fold to form in solution.
3. Preserve geometry during relaxation
Rosetta FastRelax constraints were applied during sequence optimization. These restraints help prevent relaxation from moving catalytic side chains away from the geometry specified by the design.
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4. Predict and filter structures
AlphaFold 3 (AF3) predictions were used to evaluate whether each designed sequence was likely to adopt the intended structure. Designs whose predicted folds or active sites did not meet the computational criteria were removed before experimental testing.
5. Test the surviving proteins
The remaining candidates were produced and assayed. This final step is essential: computational confidence does not guarantee folding, catalytic activity or the desired substrate specificity. The 13 active proteins among 69 tested candidates quantify that gap.
Why an activated cysteine can cut a peptide bond
A cysteine side chain can act as a nucleophile, attacking the carbonyl carbon of a peptide bond. In a free protein or in water, the reaction is slow because the nucleophile is not necessarily in the right chemical state or position, and the peptide substrate is poorly aligned for attack.
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The designed enzymes address both problems. Their active sites preorganize an activated cysteine and position other catalytic residues to support the reaction. A binding cleft then holds a target sequence in a geometry that makes productive attack more likely. The result is sequence-dependent cleavage rather than indiscriminate breakdown of every nearby peptide bond.
This specificity principle is consistent with the established behavior of papain-like cysteine proteases: the shape and chemistry of the substrate-binding cleft strongly influence which sequences are recognized. In a de novo protein, that cleft has to be created at the same time as the catalytic machinery.
How the designs were validated
Biochemical activity
Thirteen of the 69 tested designs showed activity. The strongest catalysts accelerated the reaction by as much as 3 × 107 relative to the uncatalyzed background. That is a large gain over spontaneous hydrolysis, but the most active designs still do not match the best native cysteine proteases.
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Structural validation
Six crystal structures were determined. Each had a Cα root-mean-square deviation below 1.2 Å from its computational model, supporting the claim that the pipeline can realize newly generated catalytic folds rather than only predictably remodel known ones. Six structures provide strong direct checks for those proteins, but they do not establish that every inactive or uncrystallized design adopted its intended model.
Activity in mammalian cells
Selected designed proteins cleaved their intended targets in mammalian cells. This shows that the catalytic concept can function in a cellular environment, where proteins encounter crowding, competing interactions and cellular clearance mechanisms absent from a purified assay. It is evidence of cellular operation, not evidence of therapeutic efficacy or safety.
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The structural comparisons indicate high accuracy for the six designs that were crystallized: all six had Cα RMSD values below 1.2 Å. That agreement is important because a catalytic design can fail even when its overall fold looks plausible if a cysteine, histidine or substrate-contacting residue shifts by only a few angstroms.
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The 19% experimental hit rate gives a complementary measure. AF3, RFD2-MI and sequence-design filters narrowed the search to candidates worth testing, but most tested proteins were not active. The evidence therefore supports a useful and increasingly reliable design workflow, not a push-button method that predicts activity for every sequence.
What still limits performance
The authors attribute the remaining gap to highly active native proteases to imperfect active-site preorganization and substrate positioning. In practical terms, the cysteine and histidine chemistry may not be held in an optimal geometry throughout the reaction, or the binding cleft may allow the peptide to sample poses that are unreactive.
- Improve active-site geometry: refine distances, angles and electrostatics around the catalytic cysteine and histidine system.
- Improve substrate positioning: shape the cleft so the target peptide spends more time in a reactive pose.
- Preserve both properties during folding: catalytic precision must survive sequence optimization, relaxation and expression.
These priorities also explain why a high structural match alone is not enough. A protein can resemble its model closely and still be a weak catalyst if the transition-state environment or substrate contacts are slightly misconfigured.
Reproducibility and public data
Atomic coordinates and structure factors for the six solved structures are deposited in the RCSB Protein Data Bank under accession codes 9YNL, 9YNM, 9YOX, 9YOY, 9YOZ and 9YP0. Protein sequences are listed in Supplementary Table 1, and complete plasmid DNA sequences are provided in the study’s Supplementary Information files. Those resources allow other laboratories to inspect the structures, reproduce expression constructs and test alternative substrates or mutations.
What this means for protein design
The central advance is not that every designed protease works. It is that catalytic activity was obtained from proteins with genuinely new folds, and that some of those proteins functioned inside mammalian cells. The combination of a measurable 19% success rate, very large rate enhancements and close structural agreement demonstrates that computational methods can generalize beyond familiar enzyme scaffolds.
Future designs will be judged on more than activity alone: catalytic rate enhancement, sequence specificity, active-site preorganization, agreement between predicted and solved structures, stability, cellular delivery and function are all relevant. The present study establishes a foundation for those improvements while leaving clinical performance and safety unestablished.
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