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AI can now help design proteins that do more than bind a target: they can be shaped to recognize a chosen protein surface and, in some cases, cut the target’s peptide backbone or send it to a cell’s disposal machinery. The process is an iterative design-and-test loop. Generative models propose a structure, sequence models assign amino acids, structure predictors check whether the design is plausible, and laboratory experiments determine whether it actually works.
Two capabilities are often confused. A designed protease directly hydrolyzes a peptide bond. A bifunctional degrader binds the target and recruits an E3 ubiquitin-ligase adapter, causing the cell to ubiquitinate and destroy the target in the proteasome. Both approaches have reached cell-culture demonstrations, but neither has yet established an approved human therapy.
What AI is actually designing
A protein-design system does not simply type a target name and receive a finished medicine. It receives a structural or functional objective: a surface patch to bind, a peptide sequence to recognize, or a catalytic arrangement that should place reactive atoms near a bond. The software then searches an enormous space of possible three-dimensional backbones and amino-acid sequences.
The output is a set of candidates that satisfy computational constraints. Each candidate still has to be synthesized, purified, and tested. Folding, solubility, stability, catalytic turnover, cellular delivery, and unintended interactions are physical properties; they cannot be confirmed from a model score alone.
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How the propose–predict–filter loop works
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Define the target and the desired action
Researchers begin with a target structure, a modeled structure, or a functional specification. For a binder, the objective may be a particular epitope or surface patch. For a protease, the objective includes the substrate region and the geometry needed to position a reactive residue at the peptide bond. For a degrader, the design must include two binding surfaces: one for the target protein and one for a cellular disposal component.
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Generate candidate backbones
Diffusion and hallucination methods create new protein backbones around the chosen interface or active-site geometry. The 2026 cysteine-protease work used RFD2-MI in this stage. These methods are proposing physical shapes, not proving that a sequence will fold or function.
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Assign amino-acid sequences
ProteinMPNN and related sequence-design models choose amino acids that are compatible with each proposed backbone. The model balances core packing, surface chemistry, local geometry, and the residues needed for binding or catalysis.
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Predict the folded structure and complex
AlphaFold-family systems, including AlphaFold3 in the 2026 protease workflow, check whether the sequence is likely to adopt the intended fold and whether the target complex is geometrically plausible. These predictions are filters for prioritizing experiments, not substitutes for affinity or activity measurements.
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Screen experimentally and iterate
Selected genes are synthesized and expressed. Researchers measure folding, solubility, binding, cleavage or degradation, selectivity, and cellular behavior. Failed designs inform a new round of modeling. This loop is why a high-quality computational pipeline can still require many physical candidates.
How a designed protein recognizes its target
Recognition comes from complementary shape and chemistry. The designed surface is arranged to fit a selected patch, epitope, or substrate sequence. Contacts can include hydrogen bonds, electrostatic attractions, hydrophobic packing, and close shape complementarity. A good design also limits clashes with neighboring regions of the target and leaves enough flexibility for the complex to form.
Recognition can be based on a three-dimensional surface rather than a short linear sequence. That distinction matters: a protein may expose the same sequence in one conformation but hide it in another. Designers therefore specify the structural context when the target’s shape is important.
Small binders are possible. A 2022 study based only on a target structure reported designed proteins shorter than 65 amino acids. After experimental optimization, some reached nanomolar-to-picomolar binding affinities. Those figures describe the reported optimized binders, not a guaranteed result for every new target.
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What must be added to make a protein cut another protein
Binding alone does not create an enzyme. Cleavage requires an active site that accelerates peptide-bond hydrolysis and positions the substrate correctly. In the 2026 de novo cysteine-protease study, an activated cysteine nucleophile was placed with supporting catalytic residues so the reactive atom could attack the target backbone in a sequence-dependent context.
The design challenge has two parts:
- Correct chemistry: catalytic residues must have the right identities, protonation environment, distances, and orientations.
- Correct positioning: the substrate must sit in a productive pose long enough for the reaction to occur, while non-target proteins should be excluded or bind unproductively.
Native proteases usually benefit from finely preorganized active sites evolved over many generations. AI-designed proteases often lose efficiency because the active site is not perfectly rigid or the substrate is not held in the best orientation. The 2026 authors identified this imperfect preorganization and substrate positioning as key reasons their designs still lag native cysteine proteases.
Two different meanings of “break down”
Researchers use the phrase for two mechanisms with different requirements and risks.
| Approach | What the designed protein does | What happens to the target | Evidence reported | Main bottleneck |
|---|---|---|---|---|
| Direct enzymatic cleavage | Recognizes a sequence or structural context and positions a catalytic nucleophile at a peptide bond. | The bond is hydrolyzed; one protein becomes fragments. | The 2026 Nature study reported activity in vitro and cleavage of targets in mammalian cells. | Active-site preorganization, productive substrate positioning, selectivity, stability, and delivery. |
| Targeted cellular degradation | Uses one binding surface for the target and another to recruit an E3-ubiquitin-ligase adapter. | The cell ubiquitinates the target and sends it to the proteasome. | A 2026 JACS study reported degradation of BCL-xL linked to apoptosis in cells. | Getting both interactions to form in cells, controlling off-target effects, and achieving suitable exposure and pharmacology. |
A protease can, in principle, act catalytically on multiple target molecules. A degrader is not an enzyme that cuts the target; it works by bringing the target and disposal machinery together. The distinction affects dosing, durability, cellular localization, and the experiments needed to establish specificity.
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What the published results show
| Study and year | Reported result | How to interpret it |
|---|---|---|
| De novo cysteine proteases, Nature, 2026 | 13 of 69 screened designs were active (19%); activity reached up to a 3 × 107-fold rate enhancement over background hydrolysis. | This is the success rate among the screened designs and the maximum catalytic acceleration reported by the authors, not a universal hit rate for protein design. |
| Target-structure-only binders, 2022 | Designed proteins shorter than 65 amino acids were reported; experimentally optimized examples reached nanomolar-to-picomolar affinity. | Very small binders can be made, but the affinity figures apply to the optimized examples that were tested. |
| Deep-learning binder workflow, Nature Communications, 2023 | Adding deep-learning structure checks and ProteinMPNN produced a nearly ten-fold increase in experimental success. | The improvement concerns that workflow and its tested targets; it does not eliminate the need to screen candidates. |
| Bifunctional targeted degraders, JACS, 2026 | 75% submicromolar-binder success against selected BCL-xL and MCL-1 designs. | The percentage applies to the selected targets and the study’s binder-creation experiments, not to all proteins. |
Computational confidence remains an imperfect guide. In the targeted-degradation work, several AI confidence or physical-scoring metrics did not correlate well with measured binding affinities. Laboratory binding and activity assays therefore remain decisive.
What happens when the designs enter cells
The mammalian-cell result for designed proteases is important because a protein that works only in a purified tube may fail in the crowded, regulated environment of a cell. Inside cells, a candidate must fold, avoid rapid destruction, reach the compartment containing its target, and retain activity at the available concentration. It must also avoid damaging unrelated proteins.
The degrader route adds another dependency: the target-binding and E3-adapter-binding surfaces must create a productive complex in the cell. Successful ubiquitination depends on geometry, local concentrations, and access to the proteasome. Degradation can trigger downstream effects, as shown by the reported BCL-xL degradation linked to apoptosis, but that cellular outcome does not by itself establish therapeutic safety.
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A practical laboratory workflow
- Choose a structurally defined target region. Map an accessible epitope, substrate segment, or catalytic pose. Check whether the region is exposed in the cellular state you care about.
- Generate a diverse design set. Use a diffusion or hallucination model to propose multiple backbones rather than trusting one high-scoring structure.
- Design sequences. Run ProteinMPNN or an equivalent model, retaining variants that preserve the interface and active-site geometry.
- Predict monomers and complexes. Use AlphaFold-family predictions to remove candidates that misfold or place the target incorrectly. Treat confidence as triage, not proof.
- Express and purify. Measure soluble yield, monodispersity, and structural integrity before interpreting a weak functional result.
- Measure binding independently. Use an assay that reports affinity or occupancy, and include an unrelated-protein control to reveal nonspecific sticking.
- Measure the intended breakdown mechanism. For proteases, identify cleavage products and quantify turnover against background hydrolysis. For degraders, measure target abundance, ubiquitination or proteasome dependence, and cellular viability separately.
- Test selectivity and durability. Challenge close homologs, vary substrate concentration, monitor loss of activity over time, and repeat in the relevant cellular compartment.
Controls should distinguish true catalysis or degradation from simple destabilization, nonspecific aggregation, expression differences, or general cell toxicity. A design that binds tightly but does not produce the intended downstream event is a binder, not a successful breakdown system.
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Current limitations and failure modes
- Low conversion from computation to function: many plausible structures fail during expression, folding, binding, or catalysis, which is why experimental screening remains necessary.
- Active-site flexibility: small positional errors can leave a nucleophile near the bond but unable to react efficiently.
- Substrate access: a target region may be buried, transiently exposed, or blocked by other cellular proteins.
- Scoring mismatch: predicted confidence and physical scores may not track measured affinity or catalytic rate.
- Off-target activity: a surface that recognizes shared structural motifs can bind or cleave related proteins.
- Cellular delivery: getting a designed protein into the correct cell type and compartment remains a separate engineering problem.
- Safety uncertainty: immunogenicity, pharmacology, dose control, and long-term effects require animal and human studies that these reports did not provide.
Where the technology is most useful next
Near-term uses are strongest where researchers can control the design environment and measure outcomes directly. Synthetic-biology circuits could use a cleavage event as a signal or switch. Intracellular removal tools could eliminate a selected protein in cell culture. Designed binders can support molecular detection, imaging, and mechanistic studies. Specialist protein-engineering and structural-biology organizations can also use these workflows to create custom binders or protease assays.
Therapeutic applications remain conditional on delivery, specificity, immunogenicity, exposure, and safety. The current evidence demonstrates in-vitro and cell-culture behavior, not an approved treatment or a proven ability to destroy a harmful protein safely throughout a human body.
Bottom line
AI designs these molecules by combining generative backbone creation, sequence design, structure prediction, and laboratory iteration. Recognition comes from complementary shape and chemistry; breakdown requires either a precisely organized catalytic site or recruitment of the cell’s ubiquitin–proteasome machinery. The field has progressed from designed binders to active de novo proteases and cellular degraders, but experimental screening and biological validation still determine whether a design works.
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