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AI can propose proteins that perform a requested task, but a high model score or convincing structure prediction does not prove that the molecule is specific, non-toxic, stable, or safe in a living system. Unintended binding, immune reactions, degradation products, delivery to the wrong tissue, and deliberate misuse are all credible failure modes. Safe development therefore requires layered experimental testing, manufacturing controls, and governance rather than reliance on a single computational confidence score.
Why an apparently successful design can still be unsafe
Protein-design systems optimize what they are asked to optimize: a predicted fold, a binding interaction, catalytic activity, expression level, or another measurable objective. Biology is broader than that objective. A design can achieve the requested activity while also binding a second protein, activating a receptor, sticking to a membrane, or changing a signaling pathway that was never included in the design brief.
Machine-learning systems work from sequence, structure, and functional data. Those datasets are incomplete representations of biology and may contain measurement gaps and biases. A predicted structure describes a likely conformation; it does not establish how the protein behaves in a crowded cell, how it changes over time, or what happens after the immune system encounters it. The European Food Safety Authority (EFSA) has noted that toxicity is often connected to biological function that is not obvious from primary sequence alone, and that biases in structure-training data can affect AI-derived conclusions.
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What can go wrong with an AI-designed protein?
1. It can bind the wrong molecule
A design optimized to bind one target may also recognize related proteins, receptors, lipids, nucleic acids, or components of the extracellular matrix. Similar surface shapes, charge patterns, or short sequence motifs can create unintended interactions. These may be weak in a test tube but significant when the protein reaches a high local concentration in a tissue.
Off-target binding can neutralize the intended effect, alter the distribution of the protein, or create a new pharmacological effect. For example, an engineered binder that reaches a receptor family may activate or block a related receptor even though the design objective named only one member. Computational docking or affinity estimates can prioritize such possibilities, but they cannot enumerate every biologically available partner.
2. It can activate an unexpected cellular pathway
Binding is not the same as a harmless physical contact. A protein may cluster receptors, cross-link cell-surface molecules, alter membrane behavior, or enter a cell and interact with intracellular machinery. Those events can trigger inflammation, apoptosis, proliferation, coagulation, or other pathway changes that were not visible in the original design metric.
Functional labels used in training data are also imperfect proxies. A label such as “agonist,” “inhibitor,” or “enzyme active” may summarize a particular assay under particular conditions. It does not guarantee the same outcome in a different cell type, species, concentration range, or disease state.
3. It can be directly toxic, or toxic only in a particular context
A novel sequence may perturb cells or tissues in ways that cannot be inferred from sequence similarity. Toxicity can depend on biological function, dose, exposure duration, metabolism, and the condition of the receiving tissue. A protein that appears benign in one cell line may damage primary human cells, stressed cells, or a tissue with a different receptor profile.
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Context can also change exposure. A design intended for local delivery may enter the circulation; a protein cleared rapidly in one species may persist in another; and an inflammatory response can change permeability and biodistribution. These are reasons to test the molecule in models that reflect the proposed route and patient population, not only in a convenient production cell line.
4. It can provoke anti-drug antibodies or other immune reactions
Engineered therapeutic proteins can elicit anti-drug antibodies (ADAs), T-cell responses, complement activation, or innate immune reactions. Antibodies may neutralize the treatment, accelerate its clearance, or form immune complexes. In rare and unpredictable cases, anti-drug antibodies can seriously compromise safety or efficacy, as described by Carter and Quarmby.
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Immunogenicity is not determined solely by whether a sequence is “human-like.” Aggregates, impurities, chemical modifications, unusual glycosylation, dosing frequency, and the route of administration can all influence immune recognition. Preclinical immunogenicity assessments can reduce the incidence or severity of problems, but current methods have important limitations and cannot predict every human response.
5. Manufacturing and storage can create different, riskier molecules
The intended sequence is only one part of the final product. Truncated forms, misfolded species, aggregates, precursors, oxidation products, and other degradation products can arise during expression, purification, shipping, or storage. Each variant may have different potency, tissue distribution, or immunogenicity.
Aggregation is especially important because it can change both pharmacology and immune risk. A design that is monomeric in a short laboratory experiment may oligomerize at the concentration needed for treatment or after repeated temperature changes. Stability studies therefore need to cover the proposed formulation, container, storage period, transport conditions, and in-use handling.
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6. Delivery can expose the wrong tissues
Even a specific protein can be unsafe if it reaches tissues where the target is absent or where the same interaction has a different consequence. Injection route, formulation, half-life, tissue penetration, and clearance determine exposure. A protein designed for a tumor, for example, may also reach liver, kidney, vascular, or immune compartments.
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7. Degradation can produce active fragments
Proteases can cut a designed protein into fragments with new binding surfaces or activities. A fragment might be inactive, or it might bind a different receptor, cross a biological barrier, or persist longer than the parent molecule. Testing only the intact protein can miss this risk. Analytical methods should identify major breakdown products under relevant physiological and storage conditions.
How to evaluate the safety of a candidate design
A defensible evaluation moves from a precise definition of the intended use to progressively more realistic tests. Each stage should have predefined failure criteria and a documented decision about whether to continue, redesign, or stop.
- Define the intended function and exposure. Specify the molecular target, desired activity, dose range, route, schedule, duration, receiving tissue, and the outcomes that would be unacceptable. A vague goal such as “bind the disease protein” is not sufficient for a safety plan.
- Map plausible off-targets. Use sequence, structure, and biophysical analyses to identify related proteins, receptors, membranes, enzymes, and abundant molecules that could interact with the design. Treat these analyses as hypotheses for testing, not as proof of specificity.
- Run an off-target binding panel. Measure binding against the plausible alternatives under conditions that cover relevant concentrations. Include close homologues and biologically important proteins that were not present in the design dataset. Confirm hits with an orthogonal method because one assay format can create artefacts.
- Test cellular function. Expose multiple relevant cell types and measure pathway-specific endpoints, viability, cytokines, receptor activation or blockade, and changes in gene expression where justified. Include positive controls, untreated controls, and concentration ranges that exceed the intended exposure.
- Use tissue-relevant models. Organoids, primary cells, ex-vivo tissues, or other models can reveal effects hidden by a single immortalized cell line. Select models that represent the proposed route, target tissue, and likely off-target organs.
- Characterize the molecule as manufactured. Confirm identity, purity, potency, folding state, aggregation, charge variants, glycosylation where applicable, and degradation products. Repeat key assays with material from the intended manufacturing process rather than only an early laboratory preparation.
- Measure pharmacokinetics and biodistribution. Establish absorption, half-life, clearance, tissue exposure, active metabolites or fragments, and dose proportionality. Connect these measurements to the concentrations used in cellular safety assays.
- Assess immunogenicity. Screen for sequence and structural features that may contribute to immune recognition, evaluate innate immune activation, and monitor anti-drug antibodies in appropriate preclinical studies. Interpret negative results cautiously because no preclinical assay reproduces every human immune response.
- Progress to governed in-vivo studies only when justified. Choose species and endpoints that address identified risks. Include clinical pathology, histopathology, immune markers, and recovery observations where appropriate. In-vivo work should answer specific unresolved questions rather than serve as a substitute for basic characterization.
- Update the risk assessment after every new result. A new off-target interaction, aggregate, or unexpected pathway signal should trigger a documented review of dose, formulation, sequence, delivery, and monitoring plans.
The National Institute of Standards and Technology (NIST) describes testing, evaluation, verification, and validation (TEVV) approaches that use safer proteins as proxies for AI-assisted protein-design risks. Proxy studies can improve methods and controls, but they do not remove the need to test the actual candidate.
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How to compare candidates without confusing prediction with evidence
Teams often rank designs by a confidence score. A safer comparison uses several axes and records the strongest evidence actually available for each one.
| Comparison axis | Questions to ask | Evidence that carries more weight |
|---|---|---|
| Intended function | Does the protein perform the requested activity at the proposed concentration and in the relevant system? | Reproduced functional assays in relevant cells or tissues; computational scores alone are preliminary. |
| Target specificity | Does it avoid related proteins, receptors, membranes, and abundant off-target molecules? | Quantitative panels with orthogonal confirmation and cellular pathway tests. |
| Toxicity | Does it harm cells or tissues directly, or only under a particular dose or disease context? | Dose-response data from relevant primary cells, tissues, and appropriately governed animal studies. |
| Immune risk | Could the sequence, structure, aggregates, or impurities provoke an unwanted immune response? | Characterization of the final product plus preclinical immunogenicity and ADA monitoring; no single assay is definitive. |
| Manufacturing stability | Does the product remain folded, potent, and free of problematic variants during storage and use? | Validated analytical methods and stability studies under proposed conditions. |
| Delivery and reversibility | Where does it go, how long does it stay, and can exposure be stopped if harm appears? | Pharmacokinetic, biodistribution, and clearance data tied to a practical mitigation plan. |
| Detectability by screening | Would existing sequence or synthesis screening recognize a high-risk design? | Combined sequence, structural, functional, and synthesis-provider review rather than sequence similarity alone. |
| Overall evidence strength | What has actually been observed, and what remains a prediction? | Transparent separation of computational prediction, in-vitro testing, animal testing, and clinical observation. |
Could AI-designed proteins be misused?
Yes. The same systems that help optimize a therapeutic protein could be directed toward known toxins or pathogen traits, immune escape, altered host range, or entirely novel toxic activities. A particularly difficult problem is that a dangerous design may have little sequence similarity to known hazards. Philip Hunter reported the security concern that screening based on sequence similarity “could be undermined by the ability to create novel sequences with similar functions to known proteins but with undetectable sequence homology.”
Accidental release is another pathway. A high-risk sequence can appear in a public model output, an unsecured notebook, a shared repository, or a synthesis request made without adequate review. Controls should therefore cover the complete workflow rather than only the model.
- Screen sequences and synthesis requests with methods that combine similarity, structure, function, and contextual risk signals.
- Restrict access to high-risk design capabilities and require authenticated users, logging, and review of unusual requests or outputs.
- Separate exploratory model work from systems that can order DNA or release material, with human approval gates between them.
- Retain audit records so investigators can reconstruct who generated, changed, approved, or ordered a design.
- Prepare incident and emergency-governance procedures before deployment, including escalation contacts and authority to pause access.
In a 2024 Science article, David Baker and George Church argued that synthetic gene sequences and synthesis data should be collected in repositories that are queried only in emergencies, so protein design can proceed in a safe, secure, and trustworthy manner. The World Health Organization likewise emphasizes that AI in medicine must deliver public-health benefit within appropriate governance.
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Common evaluation mistakes and how to correct them
Treating a structure prediction as a safety certificate
Why it fails: A plausible fold does not reveal all binding partners, pathway effects, aggregates, or immune responses.
Correction: Use structure predictions to select experiments, then verify specificity, function, stability, and toxicity experimentally.
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Testing only the intended target
Why it fails: The most consequential interaction may involve a related receptor, membrane component, or immune sensor.
Correction: Build an off-target panel from biological context, not just sequence similarity.
Using one cell line and one concentration
Why it fails: Cell lines differ in receptors, metabolism, stress responses, and innate immune signaling; a single concentration cannot establish a dose-response relationship.
Correction: Test multiple relevant cell types across a concentration range that includes and exceeds expected exposure.
Evaluating an early laboratory sample instead of the intended product
Why it fails: Expression system, purification, formulation, storage, and shipping can create aggregates or variants absent from the first sample.
Correction: Repeat critical safety and potency tests with material made by the proposed process and characterize its impurities and degradation products.
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Why it fails: Human immune responses are variable, and preclinical assays have limited predictive power.
Correction: Combine sequence and product characterization with several complementary assays and continued monitoring.
What is established—and what remains uncertain
It is established that AI-designed proteins can be useful and that computational methods can narrow the design space. It is also established that sequence, structure, and functional labels do not provide a complete model of biology; unintended binding, pathway effects, toxicity, product variants, delivery effects, and immunogenicity remain plausible risks.
What is not established is one universal failure rate for AI-designed proteins. No single percentage can cover a short-lived research reagent, a long-acting therapeutic, an industrial enzyme, and a protein intended for environmental release. Claims about safety should therefore identify the protein class, assay, exposure, and evidence tier instead of presenting a model score as a population-wide guarantee.
The practical standard is layered evidence: define the intended use, challenge the design with plausible off-targets, measure cellular and tissue effects, characterize the manufactured product, establish exposure and clearance, evaluate immune risk, and maintain governance against misuse. A candidate that cannot meet those requirements is not demonstrated safe merely because an algorithm generated it successfully.
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