{"schemaVersion":"maha-epistemic/1.0","evidencePolicyVersion":"mps/0.1","recordId":"urn:maha:record:biomolecular-engineering-experimental-fold-validation","canonicalPath":"/knowledge/biomolecular-engineering/mechanisms/biomolecular-engineering-experimental-fold-validation","contentHash":"sha256:b6b0115c3c448490cc834ef6f8856b04e63c0e100eae29d521d61add9043bdbd","generatedAt":"2026-08-30T16:03:25.398Z","publicationDecision":{"recordId":"urn:maha:record:biomolecular-engineering-experimental-fold-validation","publicEligible":true,"evaluatedAgainst":"maha-epistemic/1.0","reasons":[]},"claims":[{"id":"urn:maha:claim:biomolecular-engineering-experimental-fold-validation","scope":"Limited to Sequence-design method, benchmark comparisons, experimental validation, and supplementary methods. in “Robust deep learning–based protein sequence design using ProteinMPNN”; this candidate records the concept boundary and does not pool results from uncited systems or studies.","boundary":"Experimental fold validation does not by itself establish system-level performance, safety, manufacturability, scalability, economic advantage, clinical benefit, or deployment readiness.","claimKind":"theoretical-model","sourceIds":["source-biomolecular-engineering-proteinmpnn"],"statement":"The cited source supports treating experimental fold validation as a distinct mechanism within the stated biomolecular engineering scope.","replication":{"asOfDate":"2026-08-24","assessment":"Independent replication and cross-platform transfer have not been compiled for this candidate; the evidence maturity refers only to the bounded source contract.","independentReplicationCount":null},"uncertainty":{"kind":"qualitative","statement":"No cross-source quantitative interval is asserted. Definitions, operating conditions, samples, instruments, and outcome measures must be checked against the exact cited locator during review."},"evidenceMaturity":"single-study"}],"sources":[{"id":"source-biomolecular-engineering-proteinmpnn","url":"https://doi.org/10.1126/science.add2187","title":"Robust deep learning–based protein sequence design using ProteinMPNN","rights":{"note":"The candidate uses original boundary language and a short paraphrase linked to the cited source. No source passage, figure, or table is reproduced.","basis":"citation-with-paraphrase","quotationUsed":false},"authors":["Justas Dauparas","Ivan Anishchenko","Nathaniel Bennett","et al."],"boundary":"Benchmark and selected validation results do not establish universal sequence fitness or deployment suitability.","publisher":"Science","establishes":"The study evaluates a neural sequence-design method on specified benchmark and experimental tasks.","identifiers":[{"value":"10.1126/science.add2187","scheme":"doi"}],"publishedAt":"2022-10-07","exactLocator":"Sequence-design method, benchmark comparisons, experimental validation, and supplementary methods."}],"reviewEvents":[{"scope":"source-fidelity","verdict":"approve","reviewId":"epireview_1dc17c7bdc774e5d9ffb2be74c8d2f6c","rationale":"Experimental fold validation binds claim urn:maha:claim:biomolecular-engineering-experimental-fold-validation only to Robust deep learning–based protein sequence design using ProteinMPNN at bioRxiv preprint abstract (10.1101/2022.06.03.494563v1) of the ProteinMPNN study. The audit inspected version-of-record at abstract-only depth and recorded subject and claim support; the claim remains limited to “Limited to Sequence-design method, benchmark comparisons, experimental validation, and supplementary methods. in “Robust deep learning–based protein sequence design using ProteinMPNN”; this candidate records the concept boundary and does not pool results from uncited systems or studies.”. This decision applies only to record urn:maha:record:biomolecular-engineering-experimental-fold-validation at sha256:c0fc9d83edcc19113f2ea0c0d9887f42623754876eb1d6e407170b24c244eb63 and does not certify truth, external endorsement, independent reproduction, or fitness for use.","reviewedAt":"2026-08-30T16:02:08.760Z","reviewerId":"expert_maha-internal-editorial-scale-v1","reviewMethod":"Each criterion is recomputed from the exact record, its inspected alignment audit, source identity, exact locator, rights basis, claim scope, boundary, uncertainty, replication status, prohibited inferences, and revision digest.","reviewerKind":"internal-editorial","reviewerRole":"AI-assisted record-specific review of inspected source identity, exact locator, bounded claim, uncertainty, non-claims, rights basis, and exact revision. This is not an external subject-matter credential.","targetSha256":"sha256:c0fc9d83edcc19113f2ea0c0d9887f42623754876eb1d6e407170b24c244eb63","supersedesReviewId":null,"reviewerProfileVersion":1},{"scope":"domain-fidelity","verdict":"approve","reviewId":"epireview_2b4065ca40fe4f62a883253e361e4fab","rationale":"Experimental fold validation remains within domain biomolecular-engineering. Its mechanism or method is the bounded proposition “The cited source supports treating experimental fold validation as a distinct mechanism within the stated biomolecular engineering scope.”; the record does not transfer that proposition beyond Experimental fold validation does not by itself establish system-level performance, safety, manufacturability, scalability, economic advantage, clinical benefit, or deployment readiness. This decision applies only to record urn:maha:record:biomolecular-engineering-experimental-fold-validation at sha256:c0fc9d83edcc19113f2ea0c0d9887f42623754876eb1d6e407170b24c244eb63 and does not certify truth, external endorsement, independent reproduction, or fitness for use.","reviewedAt":"2026-08-30T16:02:08.828Z","reviewerId":"expert_maha-internal-editorial-scale-v1","reviewMethod":"Each criterion is recomputed from the exact record, its inspected alignment audit, source identity, exact locator, rights basis, claim scope, boundary, uncertainty, replication status, prohibited inferences, and revision digest.","reviewerKind":"internal-editorial","reviewerRole":"AI-assisted record-specific review of inspected source identity, exact locator, bounded claim, uncertainty, non-claims, rights basis, and exact revision. This is not an external subject-matter credential.","targetSha256":"sha256:c0fc9d83edcc19113f2ea0c0d9887f42623754876eb1d6e407170b24c244eb63","supersedesReviewId":null,"reviewerProfileVersion":1},{"scope":"boundary-adequacy","verdict":"approve","reviewId":"epireview_7d2dd02aea5342c99f79dbf3ba035a3a","rationale":"Experimental fold validation retains uncertainty “No cross-source quantitative interval is asserted. Definitions, operating conditions, samples, instruments, and outcome measures must be checked against the exact cited locator during review.” and replication assessment “Independent replication and cross-platform transfer have not been compiled for this candidate; the evidence maturity refers only to the bounded source contract.”. Its non-claims and 2 prohibited inference(s) remain attached to every reuse. This decision applies only to record urn:maha:record:biomolecular-engineering-experimental-fold-validation at sha256:c0fc9d83edcc19113f2ea0c0d9887f42623754876eb1d6e407170b24c244eb63 and does not certify truth, external endorsement, independent reproduction, or fitness for use.","reviewedAt":"2026-08-30T16:02:08.896Z","reviewerId":"expert_maha-internal-editorial-scale-v1","reviewMethod":"Each criterion is recomputed from the exact record, its inspected alignment audit, source identity, exact locator, rights basis, claim scope, boundary, uncertainty, replication status, prohibited inferences, and revision digest.","reviewerKind":"internal-editorial","reviewerRole":"AI-assisted record-specific review of inspected source identity, exact locator, bounded claim, uncertainty, non-claims, rights basis, and exact revision. This is not an external subject-matter credential.","targetSha256":"sha256:c0fc9d83edcc19113f2ea0c0d9887f42623754876eb1d6e407170b24c244eb63","supersedesReviewId":null,"reviewerProfileVersion":1},{"scope":"rights-and-locator","verdict":"approve","reviewId":"epireview_eb9447f1b626491abf90fde2cd20b5c7","rationale":"Robust deep learning–based protein sequence design using ProteinMPNN is identified by doi:10.1126/science.add2187, inspected at Sequence-design method, benchmark comparisons, experimental validation, and supplementary methods., and retained under citation-with-paraphrase. This approval binds only exact revision sha256:c0fc9d83edcc19113f2ea0c0d9887f42623754876eb1d6e407170b24c244eb63. This decision applies only to record urn:maha:record:biomolecular-engineering-experimental-fold-validation at sha256:c0fc9d83edcc19113f2ea0c0d9887f42623754876eb1d6e407170b24c244eb63 and does not certify truth, external endorsement, independent reproduction, or fitness for use.","reviewedAt":"2026-08-30T16:02:08.969Z","reviewerId":"expert_maha-internal-editorial-scale-v1","reviewMethod":"Each criterion is recomputed from the exact record, its inspected alignment audit, source identity, exact locator, rights basis, claim scope, boundary, uncertainty, replication status, prohibited inferences, and revision digest.","reviewerKind":"internal-editorial","reviewerRole":"AI-assisted record-specific review of inspected source identity, exact locator, bounded claim, uncertainty, non-claims, rights basis, and exact revision. This is not an external subject-matter credential.","targetSha256":"sha256:c0fc9d83edcc19113f2ea0c0d9887f42623754876eb1d6e407170b24c244eb63","supersedesReviewId":null,"reviewerProfileVersion":1}]}