AkikJana/scramble-control-panels

Scramble-control panels for cofolding confidence metrics

Per-fold confidence scores for peptide–protein complexes, folded under Boltz-1, Boltz-2, Chai-1 and a few-step-distilled model, with each cognate peptide scored against permutations of itself as well as against unrelated decoys.

2,456 folds across 16 inference arms and 75 receptors.

A permutation — a scramble — preserves amino-acid composition and length exactly and destroys only sequence order. Decoy comparisons cannot separate a confidence metric that tracks binding from one that tracks composition; a composition-matched permutation can. On these panels ipTM ranks cognates above decoys while failing to distinguish them from their own permutations, and permutations in fact outscore decoys.

Files

file	rows	what
folds.csv	2,456	every fold, every arm, one row each
scores/<arm>.csv	96–132	the same rows split by inference arm
sequences.csv	132	receptor and peptide sequences as folded
panels.csv	103	every panel by PDB ID, 75 unique receptors
posebusters_reduced.csv	144	structural validity, 10 sampling steps
posebusters_converged.csv	354	structural validity, 200 sampling steps
posebusters_summary.json	—	pass rates per check

Arms

panel is in-training (22 receptors, mostly pre-cutoff) or held-out (22 receptors released after the model's training cutoff).

arm	panel	sampling	recycling	MSA depth
boltz2_reduced	in-training	10	1	32
boltz1_reduced	in-training	10	1	32
decaf_reduced	in-training	10	1	32
boltz1_full	in-training	200	3	full
boltz1_sampling_only	in-training	200	1	32
boltz1_alignment_only	in-training	10	1	full
boltz1_recycling_only	in-training	10	3	32
heldout_reduced	held-out	10	1	32
heldout_full_draw1	held-out	200	3	full
heldout_full_draw2	held-out	200	3	full
decaf_replicates	in-training	10	1	32
boltz1_full_gpu	in-training	200	3	full
heldout_full_draw3	held-out	200	3	full
panel59_full	extended (59 rec.)	200	3	full
panel59_reduced	extended (59 rec.)	10	1	32
chai1_full	in-training	200	3	full

The last five were folded on a rented RTX 4090 in August 2026. chai1_full is Chai-1, not Boltz: the same panel and the same readout code, so the model is the only variable. It carries cognates and scrambles only — its decoys were not folded, so the ranking test is Boltz-only. Its interface pLDDT is calibrated differently (near 95 where Boltz-1 sits near 90 on the same complexes), so only standardised effects compare across the two.

Settings are taken from each run's own recorded configuration where it kept one; the three arms without a settings block are labelled from the run that produced them. decaf_replicates is 96 repeated folds of 4 receptors, used to measure run-to-run spread.

Labels

cognate — the peptide crystallised with that receptor. scrambled — a permutation of that cognate, composition and length fixed. decoy — a cognate peptide of a different receptor in the panel.

Each receptor contributes 1 cognate, 2 scrambles and 3 decoys.

Readouts

iptm, iface_plddt (mean CA pLDDT over residues within 8 Å across the interface, both sides), receptor_side / peptide_side (the same restricted to one side), peptide_whole (whole-chain pLDDT), and interface residue counts.

Suggested AUC convention

Scores are not comparable across receptors. Standardise within receptor, then pool into one ROC, with the cognate positive and everything else folded against that receptor negative.

Known limitations

• No structure files. The predictions are ~198 MB and most did not survive a disk cleanup. The scores did, and the controls are computed from the scores.

• sequences.csv covers the in-training panel only — all 132 of its folds, read from the Boltz input files. The held-out panel's inputs were lost in the same cleanup; those 22 receptors are listed by PDB ID in panels.csv and can be refetched from the RCSB. Note that sequences recovered from deposited crystals contain only observed residues and will not exactly match what was folded, so they are not a drop-in substitute.

  Integrity of this file is checkable and was checked: all 44 scrambles have amino-acid composition identical to their own receptor's cognate, and all 66 decoys are another receptor's cognate. A permutation is a permutation by construction, so a mis-joined table fails that test.

• The panels overlap by 6 receptors (7OKL, 7S7J, 8HLO, 8KDX, 9F6S, 9GRF). These postdate the training cutoff and appear in both.

• Three arms do not record a per-fold name, so their rows join on receptor_id and label rather than on fold identity.

• PoseBusters is not usable at face value on peptides. Its bond_lengths, bond_angles and flatness checks are vacuous here: RDKit perceives bonds in a PDB by distance, so a backbone bond stretched past bonding range is never perceived as a bond and cannot fail a length test. Those checks report 100% pass on structures that are in forty pieces. Worse, all_atoms_connected reports 0% pass on the converged set while RDKit's own GetMolFrags returns one fragment for the identical molecule. Use the fragment counts and nothing else. They are unambiguous: at 10 sampling steps 0 of 144 peptides is a single connected fragment (median 40.5); at 200 steps it is 354 of 354 (median 1).

Licence

CC BY 4.0.

Panel receptors are derived from the Protein Data Bank.

No model weights are redistributed here, and none of the models is ours. This dataset contains scores computed from their outputs:

• Boltz-1 / Boltz-2 — MIT-licensed (Wohlwend et al. 2024; Passaro et al. 2025)
• Chai-1 — the chai1_full arm (Chai Discovery team, 2024)
• DeCAF — the few-step distilled arm, from Scarpellini et al., Few-step cofolding with all-atom flow maps, arXiv:2606.08375

The designed-miniprotein comparison referenced in the boundary result uses the per-design release of Autonomous de novo protein binder design with Claude (CC BY 4.0), which is cited per its own CITATION.cff and is not redistributed here.