Claude protein binder design — data release v1.0

1,440 de novo miniprotein binders (50 to 120 residues) against 16 targets, designed by two Claude models operating as autonomous protein-design agents (Mythos Preview, 900 designs; Opus 4.8, 540 designs) and characterized at two contract research organizations, Adaptyv Bio (cell-free expression; SPR/BLI kinetics with the design immobilized) and Twist Bioscience (Fc-fusion expression; capture SPR with a six-point antigen titration). The release links, per design: binding calls and kinetics from both vendors with raw sensorgrams and report images, a per-design comparison and final assessment of the two, the design model, seed-best co-folded predictions from ten structure predictors with per-seed scores, and step-level design provenance.

_{Nine experimentally confirmed de novo binders from this release, shown first as design models bound to their targets (grey) and then on their own, arranged to spell ANTHROPIC. Each letter is a real structure from data/designs/…/insilico/ drawn as a cartoon, rotated and uniformly scaled only (no deformation); the BHRF1 “C” has no design-time complex in the release and is shown as its co-folded prediction. A = mythos_preview_single_target_nipahg_rank15, N = opus_4_8_multi_target_trem2_rank11, T = mythos_preview_single_target_trka_rank06, H = mythos_preview_single_target_il7ra_rank20, R = mythos_preview_single_target_il7ra_rank19, O = mythos_preview_single_target_egfr_rank24, P = mythos_preview_multi_target_vegfa_rank26, I = mythos_preview_single_target_trem2_rank02, C = opus_4_8_multi_target_bhrf1_rank01. KD values in the captions are the pooled values in design_summary.}

Dataset viewer: the default subset is design_summary (one row per design, 1,440 rows). Every Parquet table in data/tables/ is exposed as its own subset (e.g. wetlab_summary, adaptyv_replicates, twist_fits, insilico_cofold_predictions); pick it from the Subset menu or load it with load_dataset("Anthropic/claude-protein-binder-design", "<subset>", split="full"). Per-design folders under data/designs/ (structures, sensorgram PNGs, per-curve CSVs) are files to browse/download, not viewer tables.

Targets: 15-PGDH, BBF-14, BHRF1, SpCas9, EGFR, IL-7Rα, latent GDF-8 (pro-myostatin), mature GDF-8, MBP, Nipah virus G, PD-L1, RBX1, TNF-α, TREM2, TrkA, VEGF-A. Wet-lab measurements of the 120 mature GDF-8 designs were inconclusive (the antigen aggregated and bound assay surfaces non-specifically) and are not included; those designs ship with design models, co-folds and provenance only. Across the other 15 targets, 354 of 1,320 designs are binders by the release's two-vendor assessment.

Repository layout

README.md                          this card
data/                              the data release, unpacked (129,003 files, 9.9 GB)
  README.md                        start here: layout, the two assays in brief, headline numbers
  docs/                            DATA_NOTES.md, WETLAB.md, INSILICO.md, PROVENANCE.md, LOOKUP_TABLES.md, scripts/
  tables/                          20 Parquet tables (design summary, wet-lab summary and measurements,
                                   Adaptyv Bio results/replicates/reads/fits, Twist Bioscience fits/titer/raw segments,
                                   antigens and controls, co-fold predictions, epitope contacts, provenance,
                                   target constructs, column dictionary)
  manifests/                       file-level manifests for structures, sensorgram images and raw traces
  designs/<target>/<design>/       per design: design model, seed-best co-fold per predictor, Adaptyv Bio raw reads,
                                   fit curves and sensorgram images, Twist Bioscience raw traces and report images
  controls/                        assay controls and reference binders measured alongside the designs
  MANIFEST.sha256, LICENSE.md, CITATION.cff
protein_binder_design_data_release_docs_tables.zip
                                   README, docs/, tables/ and manifests/ only (33 MB), for a quick start without
                                   the per-design files
structure_and_pae/                 companion structure tier
  README.md                        layout and column definitions of the structure tier
  predictions.parquet              one row per predicted model (113,550): predictor, seed, stoichiometry, scores, paths
  design_names.csv, design_models.csv, MANIFEST.sha256, LICENSE.md, CITATION.cff
  protein_binder_design_structure_and_pae_release.zip
                                   74.5 GB: for every design, the design model and every seed of every co-fold
                                   (ten predictors, five seeds each) as mmCIF with the full PAE matrix (pae.npz)
prompts/                           the campaign prompts
  README.md                        how the prompt, kickoff message and resource corpus fit together
  prompts/                         multi-target campaign prompt, 17 single-target prompts, kickoff messages, figures
  REDISTRIBUTION_NOTES.csv         provenance and licence class of every corpus source
  protein_binder_design_prompts_release.zip
                                   1.16 GB: the prompts plus the external resource corpus they reference
                                   (cited web pages, ProteinBase collections, method and model papers)

The complete data release is also what data/ was unpacked from; its archive form (protein_binder_design_data_release.zip, 5.65 GB, 129,003 files with MANIFEST.sha256) can be rebuilt by zipping data/, and python data/docs/scripts/verify_manifest.py re-hashes every file against the manifest.

SHA-256 of the archives in this repository:

bb7d45f9957bee70e715191566577d59e69363e42f18981aa96468b4b6547dec  protein_binder_design_data_release_docs_tables.zip
2b39a3c07cda2823eedc66886aeeeb1d79c7a6788e2b8e979827262ed40ce359  structure_and_pae/protein_binder_design_structure_and_pae_release.zip
ce86bae3d22922f3b1a06235688be79ccc209fcb1a61066d32471d256eb017c5  prompts/protein_binder_design_prompts_release.zip

Getting started

from huggingface_hub import hf_hub_download
import pandas as pd

repo = "Anthropic/claude-protein-binder-design"
designs = pd.read_parquet(hf_hub_download(repo, "data/tables/design_summary.parquet", repo_type="dataset"))   # one row per design (1,440)
wetlab  = pd.read_parquet(hf_hub_download(repo, "data/tables/wetlab/summary.parquet", repo_type="dataset"))   # two-vendor comparison and final call
print(designs[["full_name", "target", "design_model", "binder_final", "kd_nM_final"]].head())

# tables and documentation only
hf download Anthropic/claude-protein-binder-design --repo-type dataset --include "data/tables/*" "data/docs/*" "data/README.md"
# one design's folder
hf download Anthropic/claude-protein-binder-design --repo-type dataset --include "data/designs/EGFR/mythos_preview_single_target_egfr_rank02/*"

Every table joins on uuid; full_name (<model>_<campaign>_<target>_rank<NN>) names the per-design folder. data/tables/column_dictionary.parquet defines every column of every table; data/docs/DATA_NOTES.md lists the caveats that matter when interpreting the measurements.

License and citation

Data and documentation: CC BY 4.0. Scripts included in the archives: MIT. Third-party material (structure-prediction outputs, reference sequences, vendor report images, reagent names) keeps its own terms; see LICENSE.md in each archive. Please cite as given in CITATION.cff.