Share the exact amino-acid sequence alongside a stable identifier, a versioned snapshot, and machine-readable provenance that connects it to the design activity, tools, inputs, responsible people, and later changes. Deposit the record somewhere with stable, citable releases. Provenance makes a sequence’s history interpretable; it does not establish that the sequence works, is safe, or has been experimentally validated.
What provenance should preserve
A sequence label alone cannot tell another researcher how a protein design came to exist or which version they are looking at. ISO 23494-2:2026 describes provenance as relations between objects, activities, persons, and organizations that account for an object’s current state. In practice, that means connecting the amino-acid sequence to the computational steps and people behind it, and recording how it changes over time.
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For an AI-designed protein, the sequence is one object in a broader record. The model and software, design settings, shareable constraints or prompts, analyses, and any experimental work are related objects or activities. This field list is a practical implementation, not a protein-specific checklist mandated by ISO. The standard provides a general provenance framework rather than prescribing exactly which protein-design details to capture.
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Build a versioned record
1. Freeze the exact sequence
Preserve the precise amino-acid string described by the record. Give it a stable identifier and a release or version identifier, and make clear which sequence the identifier refers to. When the sequence changes, create a new version and link it to the previous state rather than replacing the original record. ISO’s common provenance model treats states at different times as distinct entities and includes versioning.
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2. Describe how the design was generated
Record the date of generation, the model and software names and versions, relevant settings or parameter specifications, and the design activity’s responsible people or organizations. Include constraints or prompts when they can be shared. If an input is sensitive or restricted, identify that limitation rather than exposing the input or implying that the record is complete for reproduction.
NHGRI’s guidance on resource-sharing plans identifies AI/ML models, parameter specifications, and training protocols as resources that may need to be addressed. These details help readers interpret the design and assess whether they can reproduce or extend it; they do not substitute for the exact sequence snapshot.
3. Link later transformations and evidence
Record subsequent filtering, edits, structure predictions, and computational analyses as dated activities linked to their inputs, outputs, and tool versions. If a sequence is synthesized or assayed, identify the experiment and report its result separately. A computational history is not experimental validation, and the presence of experimental work does not by itself mean the sequence performed as intended.
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4. Deposit and cite the exact release
Choose a repository that provides stable records and a credible long-term maintenance plan. Link the sequence version to its machine-readable provenance metadata, available code or model release, and a human-readable methods description. Cite the persistent identifier and exact release—not just a project homepage or a repository’s latest version.
NHGRI recommends stable public repositories and persistent identifiers, such as a DOI or a citation.cff file for software. It does not endorse one repository for every use case. Compare candidates by whether they support persistent identifiers, immutable releases, machine-readable metadata, suitable access controls, long-term maintenance, and relevant community, funder, and journal requirements.
5. Check permissions before sharing
Review the terms governing inputs before uploading them to a repository or an AI service. In particular, NIH’s NOT-OD-25-081 says that sending controlled-access human genomic data to public generative-AI tools through prompts or interfaces violates the non-transferability provision in the applicable Data Use Certification. The notice also limits sharing and retention of models developed with those data pending further guidance. Do not assume that removing direct identifiers makes a restricted input permissible to upload.
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Other institutional, funder, repository, journal, privacy, or biosafety rules may also apply. ISO 23494-1:2026 expressly excludes biological material and data used for medical diagnosis, treatment, or therapy; following the standard does not settle those separate obligations.
What ISO 23494:2026 does—and does not—require
ISO 23494-1:2026, Biotechnology — Provenance information model for biological material and data — Part 1: Design concepts and general requirements, was published in June 2026 as the first edition, replacing ISO/TS 23494-1:2023. Its scope includes organizations generating or processing data and digital objects in biotechnology and biomedicine, including in-silico contexts. It addresses provenance management across an object’s life cycle in support of traceability, quality, and fitness for purpose.
ISO 23494-2:2026, Part 2: Common provenance model, specifies a common model and serialization requirements intended to support interoperability. It builds on W3C PROV-DM. The common model is a foundation for domain-specific implementations: it does not prescribe the actual provenance content or the recording methods. Treat the protein-design fields in this article as recommended practice, not a mandatory ISO template.
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ISO 23494-1:2026 says in its Introduction: “The resulting provenance information can serve as a quality indicator and can provide evidence of the reliability of the data, thus enabling transparency and comparability of research results.” That is a reason to keep a clear history, not a guarantee that any particular sequence is reliable, functional, safe, or fit for a specific use.
A practical release checklist
- Sequence: exact amino-acid string, stable record identifier, and version or release identifier.
- Design activity: date, model and software versions, relevant settings, shareable constraints, and responsible people or organizations.
- History: linked records of edits, filtering, analyses, and any synthesis or assay, with dates, tools, outputs, and results where available.
- Access and rights: permissions for inputs and derived materials, including any controlled-access human genomic data.
- Deposit and citation: machine-readable metadata, a human-readable methods account, a maintained repository record, and a citation to the precise release.
For each item, distinguish what is recorded from what is withheld or unavailable. That distinction lets readers judge the record’s limits without mistaking a provenance chain for a claim of reproducibility or validation.
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