complexa-evaluate-pdbs

name	complexa-evaluate-pdbs
description	Standalone evaluation of an existing PDB directory with Proteina-Complexa. Use when the user wants to "evaluate PDB files", "re-fold these designs", "compute interface pAE", "compute i_pLDDT for a folder", "run AF2 / RF3 / ESMFold on my designs", "score binder candidates", "designability of this folder", "scRMSD for designs", "motif RMSD for these PDBs", "complexa analysis", "complexa evaluate from a PDB directory", "evaluate from pdb dir", or to score third-party outputs (BindCraft, AlphaProteo, RFdiffusion, hand-curated decoys). Picks the correct evaluate_*.yaml config, wires ++dataset.pdb_dir and the folding backend, runs the evaluate → analyze chain, parses the result CSV, and reports pass-rates against the right result_type thresholds.
compatibility	complexa CLI installed (pip install -e .); CUDA GPU; AF2_DIR (colabdesign) or RF3_CKPT_PATH+RF3_EXEC_PATH (rf3_latest); ESMFold weights for monomer paths
allowed-tools	Bash, Read, Write, AskUserQuestion

Complexa Evaluate-PDBs Skill

Score a directory of pre-existing PDB files against the same metrics Proteina-Complexa uses internally. Wraps complexa analysis <evaluate_config> ++sample_storage_path=<dir>: the CLI runs the evaluate step (refold + interface metrics + monomer metrics) and then the analyze step (success thresholds, diversity, pass-rate CSVs). Do not run complexa generate here — the inputs already exist.

What this skill enables

• Re-fold a directory of designed PDBs with AF2 (colabdesign), RF3 (rf3_latest), ESMFold (esmfold), or Boltz2 (boltz2_default).
• Compute binder interface metrics: i_pAE, min_ipAE, i_pTM, pLDDT, binder/complex scRMSD.
• Compute monomer designability (ProteinMPNN-redesigned scRMSD) and codesignability (original sequence refold scRMSD).
• For motif inputs: motif RMSD (CA + all-atom), motif-region designability/codesignability, sequence recovery.
• Aggregate into per-PDB CSVs plus pass-rate summaries using the default thresholds for the result_type.

Step 1: Pre-flight

Always check GPU / disk / tool binaries before launching a refold job. RF3 and ColabDesign-AF2 are large.

bash .claude/skills/_shared/scripts/preflight.sh

Surface from preflight.json:

• gpu.available and gpu.vram_gb — colabdesign/RF3 need ≥40 GB; ESMFold tolerates ≥24 GB.
• env.missing_required — must include the keys for the chosen folding backend:
  • colabdesign → AF2_DIR
  • rf3_latest → RF3_CKPT_PATH, RF3_EXEC_PATH
  • esmfold → ESMFold weights resolvable
• tools.{foldseek,mmseqs} — required by aggregation.compute_diversity / compute_mmseqs_diversity (both default true).

If any required key is missing, route the user to the complexa-setup skill.

Step 2: Identify the design type

Like complexa design, evaluation has one default flow (protein binder) and two extensions (ligand binder, AME). The evaluate config you pass to complexa analysis decides everything else (which metrics, which refolder defaults, which thresholds the analyze step applies).

Default — protein binder

complexa analysis configs/evaluate_from_pdb_dir.yaml \
    ++sample_storage_path=/abs/path/to/pdbs \
    ++dataset.task_name=02_PDL1 \
    ++result_type=protein_binder \
    ++metric.binder_folding_method=colabdesign \
    ++metric.inverse_folding_model=soluble_mpnn \
    ++run_name=eval_pdl1_af2

Use this when the user's PDBs are protein-binder designs (multi-chain, binder is the last chain) or third-party outputs from BindCraft / AlphaProteo / RFdiffusion. Pulls thresholds for protein_binder (i_pAE * 31 ≤ 7.0, pLDDT ≥ 0.9, scRMSD_ca < 1.5 Å).

Extensions — pick the matching config

Design type	Use the protein-binder default?	Evaluate config	Analyze config	result_type	Default backend
Protein binder	Yes (default)	configs/evaluate_from_pdb_dir.yaml	configs/analyze.yaml	protein_binder	colabdesign (AF2)
Ligand binder (binder + small-molecule)	Same evaluate config, swap 3 overrides	configs/evaluate_from_pdb_dir.yaml	configs/analyze.yaml	ligand_binder	rf3_latest
AME / motif + ligand (enzyme outputs)	No — needs motif-aware config	configs/evaluate_ame_from_pdb_dir.yaml	configs/analyze_motif_binder.yaml	motif_ligand_binder	rf3_latest
Motif protein binder (standalone)	No — no _from_pdb_dir variant	configs/evaluate_motif_binder.yaml + ++input_mode=pdb_dir	configs/analyze_motif_binder.yaml	motif_protein_binder	colabdesign / rf3_latest

Extending to ligand binder (same evaluate config as default, three override swaps):

complexa analysis configs/evaluate_from_pdb_dir.yaml \
    ++sample_storage_path=/abs/path/to/pdbs \
    ++dataset.task_name=39_7V11_LIGAND \
    ++result_type=ligand_binder \
    ++metric.binder_folding_method=rf3_latest \
    ++metric.inverse_folding_model=ligand_mpnn \
    ++run_name=eval_v11_rf3

Extending to AME (different config; ligand auto-completion gotcha — see Step 4):

complexa analysis configs/evaluate_ame_from_pdb_dir.yaml \
    ++sample_storage_path=/abs/path/to/pdbs \
    ++dataset.task_name=M0096_1chm \
    ++run_name=eval_ame_chm

See reference/eval_configs.md for the full matrix (every result_type, every threshold default, every supported folding backend, motif-protein-binder variant).

Step 3: Gather inputs (AskUserQuestion)

Ask in one batched AskUserQuestion:

1. pdb_dir — absolute path to the directory of PDBs to evaluate.
2. Design type — protein binder / ligand binder / AME (motif + ligand).
3. Folding backend — colabdesign (AF2, protein binders), rf3_latest (ligand / AME), boltz2_default (alternative).
4. Target / task name — must match a key in configs/targets/targets_dict.yaml, configs/targets/ligand_targets_dict.yaml, or configs/design_tasks/ame_dict_v2.yaml. Required for binder + AME evaluation (needed to identify the target reference and, for AME, the motif contigs).
5. AME-only: confirm ligand residue name is already renamed to L:0 in every PDB (see Troubleshooting). If not, do that rename first.

Step 4: Run evaluate → analyze

Prefer complexa analysis (the evaluate→analyze chain) — it reuses the same config for both steps and writes a single log dir.

# Protein binder PDB dir, AF2 refold
complexa analysis configs/evaluate_from_pdb_dir.yaml \
  ++sample_storage_path=/abs/path/to/pdbs \
  ++dataset.task_name=02_PDL1 \
  ++metric.binder_folding_method=colabdesign \
  ++metric.inverse_folding_model=soluble_mpnn \
  ++result_type=protein_binder \
  ++run_name=eval_pdl1_af2

For ligand binders flip binder_folding_method=rf3_latest, inverse_folding_model=ligand_mpnn, result_type=ligand_binder. For AME use configs/evaluate_ame_from_pdb_dir.yaml — see reference/eval_configs.md for full worked examples.

If you need to inspect output between stages, run them separately. The configs above are shared between evaluate and analyze:

complexa evaluate configs/evaluate_from_pdb_dir.yaml ++sample_storage_path=/abs/path/to/pdbs ++run_name=eval_pdl1_af2
complexa analyze  configs/evaluate_from_pdb_dir.yaml ++run_name=eval_pdl1_af2

Dry-run first if the user is unsure (no GPU work happens; the planned file walk + invocation prints):

complexa analysis configs/evaluate_from_pdb_dir.yaml ++sample_storage_path=/abs/path/to/pdbs ++dryrun=true

Direct module invocation (debug fallback)

complexa evaluate / analyze are subprocess wrappers around the Hydra modules with logging + parallel job splitting bolted on. To attach a debugger or run under a profiler, invoke the module directly:

python -m proteinfoundation.evaluate \
    --config-path "$(realpath configs)" \
    --config-name evaluate_from_pdb_dir \
    ++sample_storage_path=/abs/path/to/pdbs \
    ++dataset.task_name=02_PDL1 \
    ++metric.binder_folding_method=colabdesign \
    ++run_name=eval_debug

For normal one-shot runs prefer complexa analysis — you get the shared log dir and a single replayable invocation, instead of having to thread the same overrides through two python -m calls.

Step 5: Parse results

Output lands under ./evaluation_results/${run_name}/:

• Per-PDB metrics CSV — *_results_*.csv (one row per input PDB × sequence_types).
• Pass-rate summaries — written by the analyze step (e.g. res_designability.csv, res_filter_ligand_pass_*.csv, success_criteria_*.json).
• Diversity output — FoldSeek/MMseqs2 cluster files when aggregation.compute_diversity=true (default).

Summarize to the user:

• Per-PDB row count and number of successful designs vs total.
• Default-threshold pass rate by result_type (e.g. for protein_binder: i_pAE*31 <= 7.0 AND pLDDT >= 0.9 AND scRMSD_ca < 1.5).
• Top 5 designs by primary metric (i_pAE for protein, min_ipAE for ligand, motif_rmsd_pred_all for motif binders).

Step 6: Emit manifest

Capture the resolved invocation + outputs for replay.

python3 .claude/skills/_shared/scripts/write_manifest.py \
  --output-dir ./evaluation_results/${run_name} \
  --command "complexa analysis configs/evaluate_from_pdb_dir.yaml ++sample_storage_path=<dir> ++dataset.task_name=<task> ++metric.binder_folding_method=<backend> ++run_name=<run>" \
  --skill complexa-evaluate-pdbs \
  --out ./eval_manifest.json

The manifest pins: resolved config, git SHA, ckpt SHA-256s, the result CSV paths, and the user-stated result_type for replay.

Most common overrides

Override	Effect
++sample_storage_path=<dir>	The directory of PDBs to evaluate (required).
++dataset.task_name=<name>	Target / AME task name (binders, AME). Resolves target PDB + contigs.
++metric.binder_folding_method=<backend>	colabdesign / rf3_latest / boltz2_default.
++metric.inverse_folding_model=<model>	protein_mpnn / soluble_mpnn / ligand_mpnn.
++metric.sequence_types=[self,mpnn,mpnn_fixed]	Which sequence flavors to refold.
++metric.num_redesign_seqs=N	ProteinMPNN/LigandMPNN redesign count.
++metric.compute_pre_refolding_metrics=true	Add bioinformatics/TMOL/HBPLUS metrics on the input structures.
++metric.keep_folding_outputs=true	Save the refolded PDBs (large, but useful for inspection).
++result_type=<type>	Override default thresholds: protein_binder / ligand_binder / motif_protein_binder / motif_ligand_binder.
++aggregation.success_thresholds.<…>	Tighten or loosen specific thresholds (see reference/eval_configs.md).
++eval_njobs=N	Parallel GPUs for the evaluate step.
++dryrun=true	Plan without running any folding.
++file_limit=N	Cap input PDBs (handy for first-pass smoke tests).

Hardware

• GPU: ≥1 CUDA GPU. AF2 (colabdesign) and RF3 (rf3_latest) need ≥40 GB VRAM (A100/H100/L40S). ESMFold runs on ≥24 GB. Multi-GPU via ++eval_njobs=N.
• CPU/disk: 24 CPUs default (ncpus_: 24). Each refolded PDB + intermediate output is ~1–5 MB; keep_folding_outputs=true can balloon to tens of GB for thousands of inputs.
• See _shared/reference/hardware.md for per-backend wall-clock and VRAM tables.

Troubleshooting

• Error: Config file not found — paths are relative to the repo root; cd to the repo before invoking complexa analysis.
• compute_motif_binder_metrics=True but result_type=protein_binder — result_type and the underlying compute_*_metrics must agree. Use configs/evaluate_ame_from_pdb_dir.yaml for AME inputs rather than mutating evaluate_from_pdb_dir.yaml.
• RF3 shape errors on AME PDBs — RF3 tries to auto-complete the ligand atoms from CCD. Rename the ligand residue to L:0 in every input PDB before evaluation; see the snippet in README.md (atom_array.res_name[ligand_mask] = "L:0").
• Diversity step fails (foldseek not found) — FOLDSEEK_EXEC not on PATH. Either fix .env (preferred) or disable: ++aggregation.compute_diversity=false ++aggregation.compute_mmseqs_diversity=false.
• All pass-rates are 0% — check the binder_folding_method matches the target type (RF3 for ligand, AF2 for protein) and that ++dataset.task_name resolves to the correct reference PDB (complexa target show <name> to verify).

Reference

Full evaluate/analyze config matrix, every supported result_type, per-threshold defaults, and three worked examples (protein binder / ligand binder / AME): see reference/eval_configs.md.