litscrape-af2af3-k5: AF2 + AF3 metrics with a K=5 AF3 diffusion ensemble

Per-design AlphaFold2 and AlphaFold3 confidence metrics for the binder-target pairs in yk0/litscrape, extended with a K=5 AlphaFold3 diffusion ensemble for uncertainty quantification. 4020 rows (one per binder-target pair); af3_k5_persample.csv holds all 5 samples per design.

Two views (config selector in the viewer)

• ensemble (default): one row per design, with AF2, AF3 top-1, and the K=5 ensemble aggregates (af3k5_* mean/std/min/max/best).
• per_sample: the raw 5 AF3 diffusion samples per design (sample-0..4), each with iptm/ptm/binder_plddt/ranking_score, plus the design metadata and label. This is where the 5 generations are visible.

How metrics were computed (sequences only; no input structures)

1. Target folded once with AF2 monomer (ColabDesign hallucination) to get a template.
2. AF2 (af2_*): de-novo AF2-Multimer (model_1_multimer_v3, 3 recycles, no MSA, no initial guess) on templated target + binder sequence.
3. AF3 top-1 (af3_*): AF3Score with init_guess=true — scores the AF2-predicted complex (anchored, deterministic, 1 sample).
4. AF3 K=5 ensemble (af3k5_*): AF3Score with init_guess=false, num_samples=5 — genuine de-novo diffusion sampling. For ptm, iptm, binder_plddt, ranking_score we report _mean / _std / _min / _max / _best (best = highest ranking_score sample). _std is the diffusion-noise uncertainty.

Key caveats

• Do not compare af3_* (top-1) with af3k5_* directly: they use different modes (init_guess=true vs false). The K=5 ensemble requires init_guess=false, since the anchored scoring mode is deterministic (1 sample). Comparisons should stay within-mode.
• Diffusion spread is small here (iptm_std median 0.015); the 5 samples agree closely for these mostly-low-confidence de-novo predictions.
• These are de-novo predictions from sequence, not refolds of designed structures, so absolute confidences are far below initial-guess pipelines and are not comparable to them.
• As classifiers of the experimental label, binder_plddt (AF2 or AF3) is the most predictive (~0.60-0.68 AUC); the ensemble mean gives a small lift on proteinbase. The uncertainty (iptm_std) is itself not a reliable binding classifier.

Ensemble structures (for Rosetta / energy computation)

The 5 AF3 diffusion structures per design are provided:

• litscrape_k5_structures_cif.tar.gz — AF3 native mmCIF
• litscrape_k5_structures_pdb.tar.gz — same models as legacy PDB (gemmi-converted; chain A = target, B = binder)
• structures_index.csv — maps every structure file -> (description, sample_idx) + that sample's AF3 metrics, so externally-computed energies (e.g. Rosetta) can be joined back to the ensemble.

Each tarball extracts to <description>/<description>_sample-{0..4}.{cif,pdb}. Structures and the af3k5_* / per-sample metrics come from the same run (regenerated together with write_cif_model=true), so coordinates and scores correspond exactly.

Columns

• Source metadata + label carried over from yk0/litscrape.
• af2_*: complex_plddt, ptm, iptm, pae, ipae, min_ipae.
• af3_*: top-1 init_guess metrics (ptm, iptm, per-chain plddt/pae/ptm/iptm).
• af3k5_*: ensemble mean/std/min/max/best for iptm, ptm, binder_plddt, ranking_score; plus k_samples, has_clash_frac.