Protocol

Adaptyv EGFR competition BLI affinity

Evidence concern: excluded from comparisons

The paper prints three different round-1 binder rates and two round-2 expression rates. Section 2.4 gives round 1 as '2.5% (5/201)' and round 2 as 'a 95% expression rate (378 of 400 tested)'; Section 4.1 gives expression as improving 'from 73% to 94%'; Section 4.1 gives the binder percentage as rising 'from 3% in the first round to 13%'. All the printed values are recorded with their locators and none is reconciled here.Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Sections 2.4, 4.1 and 4.2

Section 4.1 says Figure 8 shows 'the total number of validated binders across the 3 binding thresholds' but no section of the paper defines those three thresholds. No hit definition is therefore recorded for the Figure 8 counts, and the binding protocol's success threshold is left unstated with a reason rather than guessed.Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Section 4.1; Figure 8 caption

Dissociation constant of a competition design for EGFR, fitted from multi-cycle kinetic BLI.

3 evaluations · 3 results

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3 recorded evaluations, 3 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.

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Results

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All evaluations

3 evaluations · 3 results. Different protocols are not a single leaderboard.

Protocol: Adaptyv EGFR competition BLI affinity

Sorted by Dissociation constant (best KD for EGFR by BLI among the round 1 BindCraft entries; the strongest binding affinity in round 1) (lower is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Protocol and dataset	Dissociation constant (best KD for EGFR by BLI among the round 1 BindCraft entries; the strongest binding affinity in round 1)	Dissociation constant (best KD for EGFR by BLI among the round 2 BindCraft entries; the tightest binding de novo binder in round 2)	Dissociation constant (KD for EGFR by BLI of the overall competition winner, an optimised cetuximab scFv)
BindCraft default settings as entered in Adaptyv round 1Evaluation	Adaptyv EGFR competition round 1 tested designs	491 nM	Not reported	Not reported
BindCraft with helical loss -2.0 as entered in Adaptyv round 2Evaluation	Adaptyv EGFR competition round 2 tested designs	Not reported	82 nM	Not reported
Cradle lead optimisation of a cetuximab single-chain variable fragmentEvaluation	Adaptyv EGFR competition round 2 tested designs	Not reported	Not reported	1.21 nM

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: BindCraft default settings as entered in Adaptyv round 1	Protocol: Adaptyv EGFR competition BLI affinity Dataset: Adaptyv EGFR competition round 1 tested designs	491 nM Dissociation constant (best KD for EGFR by BLI among the round 1 BindCraft entries; the strongest binding affinity in round 1) nanomolar · lower Uncertainty: Not reported by the source: No interval is printed for this value in the prose; the repository records per-replicate fits. Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The paper prints three different round-1 binder rates and two round-2 expression rates. Section 2.4 gives round 1 as '2.5% (5/201)' and round 2 as 'a 95% expression rate (378 of 400 tested)'; Section 4.1 gives expression as improving 'from 73% to 94%'; Section 4.1 gives the binder percentage as rising 'from 3% in the first round to 13%'. All the printed values are recorded with their locators and none is reconciled here.Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Sections 2.4, 4.1 and 4.2 Section 4.1 says Figure 8 shows 'the total number of validated binders across the 3 binding thresholds' but no section of the paper defines those three thresholds. No hit definition is therefore recorded for the Figure 8 counts, and the binding protocol's success threshold is left unstated with a reason rather than guessed.Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Section 4.1; Figure 8 caption Methods, coverage and source BindCraft in Adaptyv round 1: Adaptyv EGFR competition BLI affinity uc-binder-20261022-protocol-adaptyv-bli-affinity Aggregation: Not reported Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Section 3.1: 'one exhibited the strongest binding affinity in this round of the competition at 491 nM'
Configuration: BindCraft with helical loss -2.0 as entered in Adaptyv round 2	Protocol: Adaptyv EGFR competition BLI affinity Dataset: Adaptyv EGFR competition round 2 tested designs	82 nM Dissociation constant (best KD for EGFR by BLI among the round 2 BindCraft entries; the tightest binding de novo binder in round 2) nanomolar · lower Uncertainty: Not reported by the source: No interval is printed for this value in the prose; the repository records per-replicate fits. Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The paper prints three different round-1 binder rates and two round-2 expression rates. Section 2.4 gives round 1 as '2.5% (5/201)' and round 2 as 'a 95% expression rate (378 of 400 tested)'; Section 4.1 gives expression as improving 'from 73% to 94%'; Section 4.1 gives the binder percentage as rising 'from 3% in the first round to 13%'. All the printed values are recorded with their locators and none is reconciled here.Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Sections 2.4, 4.1 and 4.2 Section 4.1 says Figure 8 shows 'the total number of validated binders across the 3 binding thresholds' but no section of the paper defines those three thresholds. No hit definition is therefore recorded for the Figure 8 counts, and the binding protocol's success threshold is left unstated with a reason rather than guessed.Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Section 4.1; Figure 8 caption Methods, coverage and source BindCraft with helical loss -2.0 in Adaptyv round 2: Adaptyv EGFR competition BLI affinity uc-binder-20261022-protocol-adaptyv-bli-affinity Aggregation: Not reported Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Section 3.1: 'One of those designs bound with 82 nM affinity to the EGFR target, making it the tightest binding de novo binder in R2'
Configuration: Cradle lead optimisation of a cetuximab single-chain variable fragment	Protocol: Adaptyv EGFR competition BLI affinity Dataset: Adaptyv EGFR competition round 2 tested designs	1.21 nM Dissociation constant (KD for EGFR by BLI of the overall competition winner, an optimised cetuximab scFv) nanomolar · lower Uncertainty: Not reported by the source: No interval is printed for this value in the prose; the repository records per-replicate fits. Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The paper prints three different round-1 binder rates and two round-2 expression rates. Section 2.4 gives round 1 as '2.5% (5/201)' and round 2 as 'a 95% expression rate (378 of 400 tested)'; Section 4.1 gives expression as improving 'from 73% to 94%'; Section 4.1 gives the binder percentage as rising 'from 3% in the first round to 13%'. All the printed values are recorded with their locators and none is reconciled here.Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Sections 2.4, 4.1 and 4.2 Section 4.1 says Figure 8 shows 'the total number of validated binders across the 3 binding thresholds' but no section of the paper defines those three thresholds. No hit definition is therefore recorded for the Figure 8 counts, and the binding protocol's success threshold is left unstated with a reason rather than guessed.Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Section 4.1; Figure 8 caption Methods, coverage and source Cradle lead optimisation in Adaptyv round 2: Adaptyv EGFR competition BLI affinity uc-binder-20261022-protocol-adaptyv-bli-affinity Aggregation: Not reported Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025); Adaptyv EGFR design competition round 2 results repository · Section 3.2: 'The overall winner of the competition (by binding affinity), and the winner of R2, was the ML-guided lead optimization strategy of Cradle, with a binding affinity of 1.21 nM'

Source checking is not independent reproduction. Release 2026-10-22-1b7cf027e4f6.

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External evaluations: 3

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Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

2 source records and release history

Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition (Cotet et al., bioRxiv 2025) · Original source · bioRxiv preprint version 1
Adaptyv EGFR design competition round 2 results repository · Original source · commit fc91b91ddc367830b755b215dbc69669675ad6a0

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Technical metadata and extraction receipts

Stable ID: uc-binder-20261022-protocol-adaptyv-bli-affinity

areas: proteins-complexes; protein-structure
contexts: research
control: Cetuximab positive control measured 9.94 nM in this assay.
limitations: Third-party measurement, independent of the design methods.; Figure 10 reports the distribution over all 60 binders; the figure was not read. Only the affinities printed in the prose are recorded.; Section 4.3 states that a Kruskal-Wallis test found no statistically significant difference in median affinity between the two rounds.; The Cradle entry is an optimisation of cetuximab, so its affinity is relative to an existing antibody rather than a de novo design.
metric: dissociation-constant
metric direction: lower
metric qualifier: KD for EGFR by multi-cycle kinetic BLI, 1:1 fit
protocol: Multi-cycle kinetic BLI against EGFR from 1000 nM to 10 nM in duplicate, baseline-corrected and fitted with a 1:1 binding model per replicate; equilibrium analysis where the signal is below the quantifiable threshold or the kinetics are too fast. Each experimental replicate is analysed independently.
review: artifact sha256: 17039d69ecc37f45f6c402511b8f4497ce631003689fa95ad1ab221f8f787a4f; method: source-hash-verification; independent-cell-check; ai-assisted-source-review; method note: Re-downloaded (HTTP 200, 493,217 bytes, the same byte count as the pinned retrieval) but the SHA-256 differs, as expected for a mutable bioRxiv page whose hash describes one retrieval. Every extracted value and every quoted sentence was then located verbatim in the new retrieval, so the difference is in page furniture and not in the evidence. One section attribution and one quotation were corrected.; note: Reviewed in docs/reviews/use-cases/binder-2026-10-22.md. Automated AI review of the source transcription only; it is not human scientific review and nothing here was reproduced experimentally.; retrieval url: https://www.biorxiv.org/content/10.1101/2025.04.17.648362v1.full; reviewed at: 2026-10-22T15:00:00Z; reviewer: claude; reviewer note: Claude review agent, independent of the extraction agent; no human review claimed
source locator: Section 2.3; Sections 3.1, 3.2 and 4.3; results/result_summary.csv
unit: nanomolar
version: bioRxiv v1 Section 2.3; round 2 repository README Methods at commit fc91b91ddc367830b755b215dbc69669675ad6a0

Related records

assessment: BindCraft in Adaptyv round 1: Adaptyv EGFR competition BLI affinity
assessment: BindCraft with helical loss -2.0 in Adaptyv round 2: Adaptyv EGFR competition BLI affinity
assessment: Cradle lead optimisation in Adaptyv round 2: Adaptyv EGFR competition BLI affinity
assessed by: Design a new binder to a chosen target

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