Evidence claim

Relevance of structural-20261009-protocol-fromm2026-abag-model-selection to "Which predicted interfaces or structures are reliable enough to guide my next experiment?"

Use case: Choose structural hypotheses to guide experiments
Assessed by: Picking the top AlphaFold3 model per antibody-antigen target with a confidence score
Relevance: Proxy evidence
Endpoint: Mean DockQ of the AlphaFold3 model picked by each of five scores a user can compute (ipTM, pTM, ranking confidence, pDockQ2 and ipSAE) among 200 models per target, and Spearman correlation with DockQ over all models and per target, on 110 antibody-antigen complexes released after the training cutoff

Answers whether the confidence a user sees can pick the right antibody-antigen model: every score based on predicted errors picks models with mean DockQ near 0.35 when the best available is 0.54, and the per-target correlation stays below 0.25. Proxy evidence: structural accuracy only, one tool, one complex class.

5 reviewed evaluations

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

21 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-11-02-a7f03c9e55a5
Property and statement	Original source and location	Review and provenance
Citation locations 1 values source id structural-20261009-source-fromm2026 locator Section 2.1; sections 3.3 and 3.6; Table 1 Context-only references	Evaluating deep learning based structure prediction methods on antibody-antigen complexes Original source ↗ structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML Retrieved: 2026-10-09T21:21:58Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.citation_locators` Source artifact SHA-256: `a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d` Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py Inspected artifact
Comparison group antibody-antigen-interfaces Context-only references	Evaluating deep learning based structure prediction methods on antibody-antigen complexes Original source ↗ structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML Retrieved: 2026-10-09T21:21:58Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.comparison_group` Source artifact SHA-256: `a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d` Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py Inspected artifact
Comparison title Antibody-antigen interface predictions Context-only references	Evaluating deep learning based structure prediction methods on antibody-antigen complexes Original source ↗ structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML Retrieved: 2026-10-09T21:21:58Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.comparison_title` Source artifact SHA-256: `a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d` Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py Inspected artifact
Constraints 4 values Inspect every linked evaluation's source locator and preserved limitations before citing a result. Do not combine this mapping's evaluations with any other protocol's results; complex sets, sampling and metrics differ. Results for one complex class do not establish performance in another. Structural accuracy and confidence are not evidence that an interaction exists, how strong it is or that an experiment will work. Context-only references	Evaluating deep learning based structure prediction methods on antibody-antigen complexes Original source ↗ structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML Retrieved: 2026-10-09T21:21:58Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.constraints` Source artifact SHA-256: `a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d` Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py Inspected artifact
Endpoint Mean DockQ of the AlphaFold3 model picked by each of five scores a user can compute (ipTM, pTM, ranking confidence, pDockQ2 and ipSAE) among 200 models per target, and Spearman correlation with DockQ over all models and per target, on 110 antibody-antigen complexes released after the training cutoff Context-only references	Evaluating deep learning based structure prediction methods on antibody-antigen complexes Original source ↗ structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML Retrieved: 2026-10-09T21:21:58Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.endpoint` Source artifact SHA-256: `a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d` Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py Inspected artifact
Excluded evaluations 4 values id structural-20261009-eval-fromm2026-af3-dockq-oracle reason Oracle row: the model is picked with the experimental structure, so it is the best achievable pick, not a selection rule a user could apply. Excluded at independent review 2026-10-09 so it is never shown as a usable method; the gap it defines is kept in claim structural-20261009-claim-fromm2026-top-vs-best. id structural-20261009-eval-fromm2026-af3-aetm-oracle reason Oracle row: pTM recomputed from the true aligned errors of the experimental structure, so it cannot be computed before an experiment. Excluded at independent review 2026-10-09; see claim structural-20261009-claim-fromm2026-top-vs-best. id structural-20261009-eval-fromm2026-af3-aeitm-oracle reason Oracle row: ipTM recomputed from the true aligned errors of the experimental structure, so it cannot be computed before an experiment. Excluded at independent review 2026-10-09; see claim structural-20261009-claim-fromm2026-top-vs-best. id structural-20261009-eval-fromm2026-af3-aerankconf-oracle reason Oracle row: 0.2 aeTM + 0.8 aeiTM, both recomputed from the true aligned errors of the experimental structure, so it cannot be computed before an experiment. Excluded at independent review 2026-10-09; see claim structural-20261009-claim-fromm2026-top-vs-best. Context-only references	Evaluating deep learning based structure prediction methods on antibody-antigen complexes Original source ↗ structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML Retrieved: 2026-10-09T21:21:58Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.excluded_evaluations` Source artifact SHA-256: `a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d` Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py Inspected artifact
Claimed field links:assessed_by:structural-20261009-protocol-fromm2026-abag-model-selection Context-only references	Evaluating deep learning based structure prediction methods on antibody-antigen complexes Original source ↗ structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML Retrieved: 2026-10-09T21:21:58Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d` Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py Inspected artifact
Headline metric dockq Context-only references	Evaluating deep learning based structure prediction methods on antibody-antigen complexes Original source ↗ structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML Retrieved: 2026-10-09T21:21:58Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.headline_metric` Source artifact SHA-256: `a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d` Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py Inspected artifact
Limitations 9 values Antibody-antigen complexes only; results do not transfer to other complex classes. Structural accuracy against the deposited complex, not binding, affinity or the outcome of an experiment. Only AlphaFold3 models are ranked in Table 1; the source does not print the same table for AlphaFold2.3, Boltz-1 or Chai-1. Table 1 does not state the number of models ranked per target. Its DockQ row (0.544) matches the best-of-200 value given in the text (0.54), so the table is read as selection among all 200 models; this is an inference. The DockQ, aeTM, aeiTM and aeRankConf rows use the experimental structure, so they are upper bounds, not usable selection rules. Figure 6 and the text give a per-target ranking-confidence correlation of 0.28, while Table 1 prints 0.214 for RankConf under a Spearman caption; the figure's correlation type is not stated. No uncertainty is printed. The four oracle rows of Table 1 (DockQ, aeTM, aeiTM, aeRankConf) are excluded: each needs the experimental structure, so none is a rule a user could apply. The gap between the best achievable pick and the best computable score is kept as a descriptive claim. Section 3.5 gives the mean per-target correlation for ranking confidence as 0.28 while Table 1 prints 0.214; the stored value is the table's and carries the conflict as a source warning. Context-only references	Evaluating deep learning based structure prediction methods on antibody-antigen complexes Original source ↗ structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML Retrieved: 2026-10-09T21:21:58Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.limitations` Source artifact SHA-256: `a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d` Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py Inspected artifact
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attributes.printed_value attributes.numeric_value sha256 db63449a78ec2766569ad5c07d992b90d9f8ac2ea28db3e478288f0ee837b75b record id structural-20261009-source-fromm2026 fields status attributes.artifact_sha256 sha256 fc5e1cd7bc0e52f5e571a570ab34dd372cf60df04f3c288f0fd04a89eb993a55 record id use-case-structural-hypotheses-experiments fields status name description facets attributes.question attributes.decision attributes.inputs attributes.output attributes.exclusions attributes.setting attributes.clinical_scope attributes.intended_users sha256 323c287cae7885e983feb5d58f24115afe49ecfbb868a0238c75b1247ffcf228 Context-only references	Evaluating deep learning based structure prediction methods on antibody-antigen complexes Original source ↗ structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML Retrieved: 2026-10-09T21:21:58Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.pins` Source artifact SHA-256: `a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d` Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py Inspected artifact

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Release 2026-11-02-a7f03c9e55a5 · Record review: source checked

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Evaluating deep learning based structure prediction methods on antibody-antigen complexes · Original source · Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML

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Stable ID: use-case-mapping-structural-20261009-fromm2026-model-selection

field: links:assessed_by:structural-20261009-protocol-fromm2026-abag-model-selection
value: structural-20261009-protocol-fromm2026-abag-model-selection
relevance: proxy
endpoint: Mean DockQ of the AlphaFold3 model picked by each of five scores a user can compute (ipTM, pTM, ranking confidence, pDockQ2 and ipSAE) among 200 models per target, and Spearman correlation with DockQ over all models and per target, on 110 antibody-antigen complexes released after the training cutoff
rationale: Answers whether the confidence a user sees can pick the right antibody-antigen model: every score based on predicted errors picks models with mean DockQ near 0.35 when the best available is 0.54, and the per-target correlation stays below 0.25. Proxy evidence: structural accuracy only, one tool, one complex class.
constraints: Inspect every linked evaluation's source locator and preserved limitations before citing a result.; Do not combine this mapping's evaluations with any other protocol's results; complex sets, sampling and metrics differ.; Results for one complex class do not establish performance in another.; Structural accuracy and confidence are not evidence that an interaction exists, how strong it is or that an experiment will work.
limitations: Antibody-antigen complexes only; results do not transfer to other complex classes.; Structural accuracy against the deposited complex, not binding, affinity or the outcome of an experiment.; Only AlphaFold3 models are ranked in Table 1; the source does not print the same table for AlphaFold2.3, Boltz-1 or Chai-1.; Table 1 does not state the number of models ranked per target. Its DockQ row (0.544) matches the best-of-200 value given in the text (0.54), so the table is read as selection among all 200 models; this is an inference.; The DockQ, aeTM, aeiTM and aeRankConf rows use the experimental structure, so they are upper bounds, not usable selection rules.; Figure 6 and the text give a per-target ranking-confidence correlation of 0.28, while Table 1 prints 0.214 for RankConf under a Spearman caption; the figure's correlation type is not stated.; No uncertainty is printed.; The four oracle rows of Table 1 (DockQ, aeTM, aeiTM, aeRankConf) are excluded: each needs the experimental structure, so none is a rule a user could apply. The gap between the best achievable pick and the best computable score is kept as a descriptive claim.; Section 3.5 gives the mean per-target correlation for ranking confidence as 0.28 while Table 1 prints 0.214; the stored value is the table's and carries the conflict as a source warning.
citation locators: source id: structural-20261009-source-fromm2026; locator: Section 2.1; sections 3.3 and 3.6; Table 1
source locator: structural-20261009-source-fromm2026: Section 2.1; sections 3.3 and 3.6; Table 1
revision: 1
reason: Add comparisons of structure-prediction reliability by complex class from the structural hypotheses use-case pass 2026-10-09.
comparison group: antibody-antigen-interfaces
comparison title: Antibody-antigen interface predictions
headline metric: dockq
stratum label: Picking AlphaFold3 models by confidence (independent)
stratum order: 2
excluded evaluations: id: structural-20261009-eval-fromm2026-af3-dockq-oracle; reason: Oracle row: the model is picked with the experimental structure, so it is the best achievable pick, not a selection rule a user could apply. Excluded at independent review 2026-10-09 so it is never shown as a usable method; the gap it defines is kept in claim structural-20261009-claim-fromm2026-top-vs-best.; id: structural-20261009-eval-fromm2026-af3-aetm-oracle; reason: Oracle row: pTM recomputed from the true aligned errors of the experimental structure, so it cannot be computed before an experiment. Excluded at independent review 2026-10-09; see claim structural-20261009-claim-fromm2026-top-vs-best.; id: structural-20261009-eval-fromm2026-af3-aeitm-oracle; reason: Oracle row: ipTM recomputed from the true aligned errors of the experimental structure, so it cannot be computed before an experiment. Excluded at independent review 2026-10-09; see claim structural-20261009-claim-fromm2026-top-vs-best.; id: structural-20261009-eval-fromm2026-af3-aerankconf-oracle; reason: Oracle row: 0.2 aeTM + 0.8 aeiTM, both recomputed from the true aligned errors of the experimental structure, so it cannot be computed before an experiment. Excluded at independent review 2026-10-09; see claim structural-20261009-claim-fromm2026-top-vs-best.
reviewed evaluations: structural-20261009-eval-fromm2026-af3-ipsae; structural-20261009-eval-fromm2026-af3-iptm; structural-20261009-eval-fromm2026-af3-pdockq2; structural-20261009-eval-fromm2026-af3-ptm; structural-20261009-eval-fromm2026-af3-ranking-confidence
review: method: automated-source-review; reviewer: claude; reviewer note: Separate Claude review agent, independent of the extractor; no human review claimed; reviewed at: 2026-10-10T00:30:00Z; note: Independent review of endpoint fit, relevance grade, limitations, exclusions and grouping against the use case and the article; every value of the listed evaluations was checked against Table 1. No execution, reproduction, human scientific review or clinical validation.
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subject: Choose structural hypotheses to guide experiments

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