Complexa Target

complexa-target

Add or edit a design target in Proteina-Complexa. Targets live in three YAML files, one per complexa design pipeline:

• configs/targets/targets_dict.yaml — protein binder (default pipeline)

• configs/targets/ligand_targets_dict.yaml — ligand binder

• configs/design_tasks/ame_dict_v2.yaml — AME / enzyme scaffolding

The complexa target CLI only manages the first two. AME tasks use an extended schema (the same core fields as ligand targets plus contig_atoms for hand-curated per-residue motif atom selections) and are file-edit-only.

Instructions

complexa target add is a thin wrapper around "load YAML → build dict → append a block" (see src/proteinfoundation/cli/target_manager.py:add_target_cli and append_target_to_dict). For agentic use, just edit the targets dict directly — the schema is short, the existing entries are great copy templates, and you skip 14 CLI flags / SMILES shell-escaping. Use the CLI only if you explicitly want its YAML auto-quoter or its overwrite-prompt safety.

For an offline edit, work on the supplied registry or requested output copy. When a copy is requested, create its parent directory and copy into an absent destination. If the destination already exists, inspect and preserve it: reuse it when it is the intended working copy, otherwise report the path conflict. Do not delete the output tree to restart a configuration edit. An installed Complexa runtime, GPU, or target structure is not required to write the configuration. Use the field schema below, preserve unrelated entries, and read back the saved block. Coordinate validation remains pending when the structure is absent; finish the configuration task without installing a runtime. With Read/Edit tools, read the destination copy before editing it; reading the original input path does not satisfy Edit's requirement for the output path.

Use the schema below and the selected registry; the CLI reference is needed only for CLI use. Finish with the saved entry, its consumer pipeline, the downstream generation.task_name, and the validation status.

Step 1: Decide the target type

Targets live in three different files, one per complexa design pipeline. Pick the file by working out which pipeline the user will run, then add the entry to that file. Each file is consumed by exactly one pipeline.

AME tasks (enzyme pipeline)

AME tasks live under motif_target_dict_cfg: with source, target_filename, ligand, contig_atoms, binder_length, and use_bonds_from_file. The contig_atoms string contains curated chain/residue/atom selections. Existing entries illustrate the field structure, not the selections for a new motif. If motif atoms, ligand selection, or structure location are missing, list the missing inputs and write a separate, explicitly incomplete template if useful. Leave the registry unchanged until those scientific inputs are supplied. A task name or PDB ID alone does not determine them. In the missing-input handoff, explain that registration will be a direct edit under motif_target_dict_cfg once the curated selections are supplied; complexa target add cannot register AME tasks. Completed entries are selected with ++generation.task_name=<NAME> and configs/search_ame_local_pipeline.yaml.

Step 2: Gather required info

Use the supplied values and ask only for missing required information. Required fields differ for protein, ligand, and AME definitions.

Protein target

Hotspot selectors encode chain + residue number, not amino-acid identity. For example, Tyr42 (Y42) on explicitly specified chain B becomes B42; already supplied chain/residue selectors stay unchanged. Preserve the user's residue numbering. Without coordinates, report that residue identities and membership in the structure are unverified.

Ligand target — protein fields above (minus target_input), plus:

Check whether the proposed key already exists in the selected YAML mapping. Preserve an existing entry unless the user requested that entry's update.

Copy a supplied SMILES exactly. Chain-specific ligand selection belongs to preparation of the input structure; do not invent a chain field or protein target_input to represent it in the ligand entry.

Examples

Step 3a: Append the YAML block directly (preferred)

Open configs/targets/targets_dict.yaml (or ligand_targets_dict.yaml for ligand targets), find a similar existing entry as a style template, and append the new block under target_dict_cfg:. Two-space indent, single blank line between entries.

Protein template

  02_PDL1:
    source: bindcraft_targets
    target_filename: PD-L1
    target_input: "A1-115"
    hotspot_residues: ["A37", "A39", "A49", "A98"]
    binder_length: [64, 155]
    pdb_id: "4z18"

Rules to match the existing file style (mirrors what complexa target add would emit):

• Quote chain/residue ranges ("A1-115", "A33") and any SMILES — flow-style strings get tripped up by : and brackets otherwise.

• Use flow-style lists (["A33", "A95"], [64, 155]) — that's what the on-disk dump produces.

• target_input, source, target_filename are required for protein. target_path (absolute path) can replace source + target_filename if the PDB lives outside $DATA_PATH/target_data/.

Ligand template

  41_7BKC_LIGAND:
    source: ligand_targets
    target_filename: 7BKC_ligand_centered
    hotspot_residues: [null]
    binder_length: [100]
    pdb_id: 7BKC
    ligand: 'FAD'
    ligand_only: True
    SMILES: "O=C2C3=Nc1cc(c(cc1N(C3=NC(=O)N2)CC(O)C(O)C(O)COP(=O)(O)OP(=O)(O)OCC6OC(n5cnc4c(ncnc45)N)C(O)C6O)C)C"
    use_bonds_from_file: True

The presence of the ligand: key flips the target into ligand mode — there is no separate is_ligand flag. target_input is not required for ligands.

After saving, skip to Step 4 to verify.

Step 3b: CLI alternative (complexa target add)

Use the CLI when you want its automatic chain/residue quoting, the overwrite-confirm prompt, or to wire target creation into a non-Python script. Prefer non-interactive mode for agentic use (-i / --editor opens an editor and blocks).

Confirmed flags (from src/proteinfoundation/cli/target_cli.py)

Read target_schema.md for the flag table and worked CLI examples when this path is needed. Select the intended dictionary with --dict; --source and --target-filename should be explicit when they differ from the CLI defaults. AME registration remains a direct edit.

Step 4: Verify

First parse the saved YAML with an available YAML parser and check the selected mapping, field types, requested values, and preservation of other entries. Read back the resulting block. This verifies configuration editing only.

With a prepared Complexa installation, pipeline configs, environment, and target structure, additionally check runtime path resolution from that checkout:

# 1. Select the dictionary that was actually edited.
complexa target show 02_PDL1 --dict configs/targets/targets_dict.yaml

# 2. Resolve the PDB path and validate it exists on disk.
#    This traverses Hydra defaults to find the target dict.
complexa validate target configs/search_binder_local_pipeline.yaml --target 02_PDL1

complexa validate target (from src/proteinfoundation/cli/validate.py::validate_target) checks:

• DATA_PATH is set and target_data/ exists.

• The target name resolves in target_dict_cfg.

• Either target_path exists, or $DATA_PATH/target_data/<source>/<target_filename>.pdb exists.

• target_input, hotspot_residues, binder_length are reported back so a human can sanity-check them.

For ligand targets, use configs/targets/ligand_targets_dict.yaml for show and configs/search_ligand_binder_local_pipeline.yaml for validation; --target is the target key, not a config path. This validator does not check residue identities, ligand chemistry, or AME contig_atoms; do not describe a path check as coordinate or scientific validation.

Step 5: Emit artifact

Save the edited entry and validation status at the user's requested output location (otherwise ./target_<name>/). For an offline edit, the saved YAML block is sufficient; no CLI-generated artifact is required. On a prepared installation, a target-show record can also be saved:

mkdir -p target_02_PDL1
complexa target show 02_PDL1 > target_02_PDL1/target_show.txt

If a standalone definition is requested, save the actual entry under its registry key in target_definition.yaml. The handoff should include the saved path, target key, consumer pipeline from Step 1, and ++generation.task_name=<NAME>, together with what validation actually ran. For an incomplete AME request, report the separate template, missing inputs, and the direct registry edit needed after those inputs arrive.

Hardware requirements

None for target definition — this is a YAML edit, not a training/inference step. Disk impact is a few KB appended to the targets dict (plus an automatic .yaml.bak backup written by save_targets_dict).

For the downstream design / evaluate runs that consume the target, defer to complexa-design and references/hardware.md.

Troubleshooting

Reference

• references/target_schema.md — every field, chain-spec grammar, AME task-name grammar, three worked examples.

• configs/targets/targets_dict.yaml — live protein entries (copy a known-good one as a template).

• configs/targets/ligand_targets_dict.yaml — live ligand entries.

• configs/design_tasks/ame_dict_v2.yaml — AME task definitions (file-edit only, not exposed via complexa target CLI).

• src/proteinfoundation/cli/target_cli.py — argparse source of truth.

• src/proteinfoundation/cli/target_manager.py — add_target_cli, list_targets, show_target, schema in TARGET_FIELDS.

• src/proteinfoundation/cli/validate.py — validate_target implementation.