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Boltz Structure And Binding

OpenAIOfficialCreated by BoltzDistributed by OpenAI

  • Skill
  • Tool integration
  • Science and research
  • MIT
  • No issues found

From the author

Predict structures and binding for one defined complex with Boltz. Use when folding a protein, RNA, DNA, or ligand complex, docking one ligand, predicting an interface, or scoring binding. Not for screening libraries or design.
— the description by Boltz

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured workflow for using the Boltz API to perform structural prediction and binding analysis on specific molecular complexes. It covers the entire process from input normalization and cost estimation to job submission and asynchronous result retrieval. It is designed for single-complex analysis rather than high-throughput screening or molecular design.

What it does

  • Normalizes molecular entities including proteins, RNA, DNA, and ligands into the required API format.
  • Configures binding analysis for ligand-protein or protein-protein interactions.
  • Estimates computational costs before job submission.
  • Manages asynchronous job execution and background result downloading.
  • Summarizes key metrics such as binding confidence and optimization scores from output files.

When to use it

  • When you need to fold a specific protein, RNA, DNA, or ligand complex.
  • When you need to predict the interface or binding affinity of a defined complex.
  • When you need to dock a single ligand into a protein structure.
  • When you have a specific molecular system and require structural confidence metrics.

Advantages

  • Ensures correct payload formatting to avoid common API validation errors.
  • Provides a robust, non-blocking workflow for handling long-running structural predictions.
  • Standardizes the handling of complex molecular inputs and binding configurations.
  • Facilitates clear reporting of structural and binding confidence metrics.

Usage examples

“Predict the structure of a protein-ligand complex with protein chain A and ligand chain B.”
The agent will normalize the entities, request a cost estimate, submit the job, and initiate a background download of the resulting CIF structure and metrics.
“What is the binding confidence for this protein-protein complex?”
The agent will configure the binding block for protein-protein interaction, run the prediction, and extract the binding_confidence metric from the results.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/openai/boltz-structure-and-binding/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip boltz-structure-and-binding.zip -d .agents/skills

Documentation by Boltz

Workflow

If boltz-api is missing from PATH, use boltz-cli-setup for install/update guidance before retrying. If a command reports missing or expired authentication, use boltz-cli-setup to start boltz-api auth login --device-code before retrying; do not ask permission first. If the agent host sandbox blocks boltz-api install/auth/API calls, use boltz-cli-setup to request the host sandbox bypass/escalation needed for user-wide CLI install, browser login, credential storage, temp files, or API access before retrying.

Use this skill for one defined complex, not a library workflow.

  1. Normalize the inputs into entities. Each entity is {type, chain_ids, value} — note plural chain_ids (an array, even for one chain) and the field is value, not sequence:

    {"entities": [{"type": "protein", "chain_ids": ["A"], "value": "MKTAYIAKQRQISFVKSHFSRQ"}]}

    type is one of protein | rna | dna | ligand_smiles | ligand_ccd. Chain IDs go in entity order (A, B, C, …) unless the user specifies otherwise. Read references/api.md for per-type field variants (cyclic, modifications, ligand CCD codes, etc.) before authoring your first payload — agent guesses like sequence: or chain_id: "A" (singular) fail with unclear 400 errors.

  2. If the user wants binding metrics, add a flat binding block with an explicit type field. For ligand-protein binding use:

    binding:
      type: ligand_protein_binding
      binder_chain_id: B

    For protein-protein binding use:

    binding:
      type: protein_protein_binding
      binder_chain_ids: [B]

    Do not nest the variant name under binding (for example, no binding.ligand_protein_binding object).

  3. Supported optional features include constraints, bonds, modifications, model_options, and binding metrics; only add them if the user asks. Read references/api.md for exact shapes and examples.

  4. Author the payload YAML or JSON, run estimate-cost, show the USD cost, wait for explicit confirmation.

  5. start to submit (synchronous). Capture the ID.

  6. Launch download-results with the agent runtime's background/non-blocking command facility so polling + download continue without blocking the agent session. In Claude Code, use Bash with run_in_background: true. In Codex, run download-results as a foreground shell command with yield_time_ms: 1000; if Codex returns a session_id, keep it for optional same-thread polling, but treat download-status plus the run directory as the durable source of truth. In Codex app/desktop runtimes that expose same-thread heartbeat automations, create a heartbeat that checks download-status periodically and posts a concise completion or failure update when the download reaches a terminal state. After launching the downloader, always report the job ID, run name, and output directory. Include the next check cadence if the heartbeat was created; otherwise include the download-status command.

Command Pattern

# Replace placeholders with concrete absolute paths before running.
# Use a short descriptive run name, for example: sab-<target>-<ligand>-v1

# 1. estimate
boltz-api predictions:structure-and-binding estimate-cost \
  --model boltz-2.1 \
  --input @yaml:///absolute/path/payload.yaml

# 2. confirm with user, then submit
boltz-api predictions:structure-and-binding start \
       --model boltz-2.1 \
       --idempotency-key "<run-name>" \
       --input @yaml:///absolute/path/payload.yaml \
       --raw-output --transform id

# 3. Copy the printed job ID into this command, then launch it in the agent
# runtime's background/non-blocking mode.
# Claude Code: Bash with run_in_background=true.
# Codex: foreground shell command with yield_time_ms=1000; keep the returned session_id if one is provided.
# Do not append "&" or use nohup in Codex.
boltz-api download-results \
  --id "<job-id-from-start>" --name "<run-name>" \
  --root-dir "/absolute/path/boltz-experiments" \
  --poll-interval-seconds 10
# -> /absolute/path/boltz-experiments/<run-name>/outputs/archive.tar.gz, .boltz-run.json

Always Do This

  • Keep payload field names exactly as the API body names shown in references/api.md; then pass the merged payload with --input @yaml:///absolute/path/payload.yaml or @json:///absolute/path/payload.json. Never use @./payload.yaml or @file:// for object-typed payloads.

  • Use absolute paths for the output root, payload files, and embedded structure files. Do not cd into the run directory for follow-up commands; pass the same --root-dir and use absolute paths so later relative paths do not drift.

  • Residue indices are 0-based wherever the payload asks for residue positions (constraints, modifications, contact tokens).

  • For CIF/PDB bytes embedded in --target / structure.data, use @data:///absolute/path/file.cif — it detects binary and base64-encodes. Don't use bare @path for binary data.

  • Use the same slug as both --idempotency-key at submit time and --name at download time so re-runs are idempotent and resume from .boltz-run.json.

  • In permission-gated agents such as Claude Code, keep each Boltz call as a top-level command that starts with boltz-api. Prefer concrete arguments over sh -c, inline environment assignments, aliases, wrapper scripts, loops, or pipelines around the boltz-api invocation unless the user already allowed that exact command form. Use --raw-output --transform id, read the printed ID, then paste that literal ID into the next download-results command.

  • Prefer the agent runtime's background/non-blocking command mode for download-results. In Codex specifically, keep download-results in the foreground and set the shell tool yield to 1000 ms; Codex will return a session_id if the command is still running. Do not append & or use nohup in Codex because the tool runner may clean up shell-backgrounded descendants before .boltz-run.json is fully written.

  • After the background/session starts, do not manually wait on it or run ad hoc polling loops. download-results emits JSONL progress on stderr by default; add --progress-format text --verbose only when you explicitly want human-readable logs.

  • In Codex app/desktop runtimes with same-thread heartbeat automation support, schedule a heartbeat after launching download-results. The heartbeat should run boltz-api --format json download-status --name "<run-name>" --root-dir "/absolute/path/boltz-experiments" on a sensible cadence, post only material status changes or terminal completion/failure, and stop once terminal. Poll the saved session_id with an empty write_stdin only for interactive, user-requested progress checks. Do not loop retrieve yourself.

  • If the current host has no heartbeat automation support, do not claim an automatic next check. Report the job ID, run name, output directory, and the command needed to check download-status.

  • If detached download needs to be restarted, re-run boltz-api download-results with the same --name "<run-name>" and the same --root-dir.

  • Poll interval: keep --poll-interval-seconds 10 for SAB — predictions usually finish in under a few minutes.

  • Cost: there is no published per-unit rate to cite for SAB — run estimate-cost and state only the figure it returns. Don't estimate or comment on cost.

Escape Hatch

For anything not covered in references/api.md:

Read references/api.md for entity shapes, binding variants, bonds, constraints, model options, and input examples. Read references/results.md when summarizing downloaded outputs, metrics, or validation quirks.

Outputs

Summarize metrics.json and point the user at the downloaded CIF path. Read references/results.md for the local layout, nested metrics, binding metric variants, and SAB validation quirks.

SAB 400 validation quirk

If the server rejects a payload with only {"code":"VALIDATION_ERROR","message":"Request validation failed"}, inspect entities, binding, and constraints; read references/results.md for details.

Files

references/api.md

Structure and Binding — Payload Reference

This covers the predictions:structure-and-binding endpoint. The request body becomes the --input object passed via @yaml:///absolute/path/payload.yaml. Field names below are API body field names (what you write in the YAML) — not CLI flag names.

Contents

  • Top-level request

  • Entity types

  • binding

  • bonds

  • constraints

  • model_options

  • templates

  • MSA control

  • Structure templates in a constraint / binding setup

  • Outputs (after download-results)

  • 400 validation quirk

  • Escape hatch

Top-level request

# payload.yaml
entities:
  - type: protein
    chain_ids: [A]
    value: MKTAYIAKQRQISFVKSHFSRQ
  - type: ligand_smiles
    chain_ids: [B]
    value: CCO
binding:
  type: ligand_protein_binding
  binder_chain_id: B
num_samples: 1
model_options:
  recycling_steps: 3
  sampling_steps: 200
  step_scale: 1.638

Top-level fields:

  • entities (required) — list of polymer / ligand entities. Chain IDs across entities must be unique.

  • binding (optional) — include only when you want binding metrics. Two variants below.

  • num_samples (optional, 1-10) — structure samples to generate. Omit for the server default.

  • bonds (optional) — custom covalent bonds; see below.

  • constraints (optional) — pocket / contact constraints; see below.

  • model_options (optional) — see below.

  • templates (optional) — up to 4 CIF/PDB templates to guide protein-chain geometry (Boltz-2.1); see templates.

Also passed as separate start flags, not inside the body:

  • --model boltz-2.1 (currently the only option)

  • --idempotency-key <slug>

  • --workspace-id <id> (admin keys only)

Entity types

All entities take type, chain_ids, value.

protein
- type: protein
  chain_ids: [A]
  value: MKTAYIAKQRQISFVKSHFSRQ
  cyclic: false              # optional
  modifications: []          # optional; server defaults to [] if omitted
  # msa: <optional>          # omit = automatic MSA generation; see "MSA control" below
rna
- type: rna
  chain_ids: [B]
  value: ACGUN
  cyclic: false
  modifications: []
dna
- type: dna
  chain_ids: [C]
  value: ACGTN
  cyclic: false
  modifications: []
ligand_smiles
- type: ligand_smiles
  chain_ids: [D]
  value: "CCO"
ligand_ccd
- type: ligand_ccd
  chain_ids: [E]
  value: ATP
Polymer modifications

Each entry in modifications:

modifications:
  - residue_index: 12           # 0-based
    type: ccd
    value: MSE

type must be ccd — SMILES polymer modifications are not supported (the API rejects them with modifications[].type must be "ccd").

binding

Include only when you want binding metrics.

Ligand–protein
binding:
  type: ligand_protein_binding
  binder_chain_id: D

Constraints:

  • binder_chain_id must reference a single ligand chain.

  • Binder ligand must have fewer than 50 atoms.

  • The entity set may only contain proteins and ligands (no RNA / DNA).

Protein–protein
binding:
  type: protein_protein_binding
  binder_chain_ids: [B]

Returned binding metrics (under output.binding_metrics):

  • For ligand_protein_binding: {binding_confidence, optimization_score, type: "ligand_protein_binding_metrics"}.

  • For protein_protein_binding: {binding_confidence, type: "protein_protein_binding_metrics"} — no optimization_score.

What they mean:

  • binding_confidence (0–1) — confidence that binding occurs; the primary signal for whether this complex binds.

  • optimization_score — ranks binding strength for lead optimization (higher = stronger predicted binding). Emitted only for ligand_protein_binding, not protein_protein_binding.

bonds

bonds:
  - atom1:
      type: polymer_atom
      chain_id: A
      residue_index: 12        # 0-based
      atom_name: SG
    atom2:
      type: ligand_atom
      chain_id: D
      atom_name: C1

Atom variants:

  • {type: polymer_atom, chain_id, residue_index, atom_name} — residue_index is 0-based.

  • {type: ligand_atom, chain_id, atom_name}. Atom-level ligand references support ligand_ccd only — referencing a ligand_smiles chain by atom is rejected (bad_request: … references SMILES ligand chain … Use ligand_ccd instead). Use a ligand_ccd entity for any atom-level bond/contact.

constraints

Pocket constraint
constraints:
  - type: pocket
    binder_chain_id: D
    contact_residues:
      A: [10, 11, 35]          # 0-based residue indices on each target chain
    max_distance_angstrom: 6.0
    force: false                # optional
Contact constraint
constraints:
  - type: contact
    max_distance_angstrom: 5.0
    token1:
      type: polymer_contact
      chain_id: A
      residue_index: 42        # 0-based
    token2:
      type: ligand_contact
      chain_id: D
      atom_name: C1
    force: false

Token variants:

  • {type: polymer_contact, chain_id, residue_index} — residue_index is 0-based.

  • {type: ligand_contact, chain_id, atom_name}. As with bonds, atom-level ligand contacts support ligand_ccd only — ligand_smiles chains cannot be referenced by atom.

model_options

model_options:
  recycling_steps: 3           # default 3 (per spec)
  sampling_steps: 200          # default 200 (per spec)
  step_scale: 1.638            # default 1.638 (per spec)

Hosted API Reference bounds:

  • recycling_steps >= 1

  • sampling_steps >= 1

  • 1.3 <= step_scale <= 2

templates

Up to 4 CIF/PDB templates to guide protein-chain geometry (Boltz-2.1). Each template maps request chains to chains in the template file:

templates:
  - template_structure:
      type: url                       # or base64 (use @data:/// for a local file)
      url: "https://files.rcsb.org/download/1ABC.cif"
    template_chains:
      - input_chain_id: A             # chain in THIS request
        template_chain_id: A          # corresponding chain in the template file
    force_threshold_angstroms: 10.0   # optional; omit to use the template without forcing

template_chains entries are objects ({input_chain_id, template_chain_id}), not bare chain-ID strings. This is distinct from embedding a CIF as raw structure bytes (see the next section).

MSA control

By default (omit msa on every protein entity) Boltz generates an MSA automatically. To override per protein entity:

  • msa: {type: empty} — single-sequence mode (no MSA).

  • msa: {type: custom, format: a3m|csv, source: {url: "..."}} — user-provided MSA. Base64 uploads use media type text/x-a3m (A3M) or text/csv (CSV).

Custom and automatic MSAs cannot be mixed: if any protein entity uses a custom MSA, every other protein entity must use custom or empty.

Structure templates in a constraint / binding setup

If you're embedding a CIF/PDB file as a source of structure coordinates (for example in a custom constraint body), use @data:// in the CLI, not @file://:

structure:
  type: base64
  media_type: chemical/x-cif
  data: "@data:///abs/path/template.cif"

Alternatively, use {type: url, url: "https://..."} to point at a presigned CIF URL.

Outputs (after download-results)

Under <output-root>/<run-name>/:

  • .boltz-run.json — run metadata, cursor, idempotency key, timing

  • outputs/archive.tar.gz — contains prediction/metrics.json, prediction/sample_*_predicted_structure.cif, prediction/sample_*_pae.npz

Metrics in metrics.json are split across best_sample.metrics, each all_sample_results[].metrics, and a separate top-level binding_metrics object.

Per-sample metrics (nine lowercase keys, present on both best_sample.metrics and each entry in all_sample_results[].metrics):

  • structure_confidence — aggregate confidence

  • ptm — predicted TM-score

  • iptm — interface predicted TM-score

  • ligand_iptm — interface iptm restricted to ligand chains (only when ligands are present)

  • protein_iptm — interface iptm restricted to protein chains (only for multi-protein complexes)

  • complex_plddt — pLDDT averaged across the complex

  • complex_iplddt — pLDDT restricted to interface residues

  • complex_pde — predicted distance error across the complex

  • complex_ipde — predicted distance error restricted to interface residues

Binding metrics (top-level binding_metrics, only present when binding was requested):

  • ligand_protein_binding → {binding_confidence, optimization_score, type: "ligand_protein_binding_metrics"}

  • protein_protein_binding → {binding_confidence, type: "protein_protein_binding_metrics"} — no optimization_score

Uppercase aliases like pLDDT, pTM, ipTM, PDE, ipDE are not what the server emits — use the lowercase keys above.

400 validation quirk

predictions:structure-and-binding is the one endpoint that may return {"code": "VALIDATION_ERROR", "message": "Request validation failed"} with no details field. If you hit this, inspect entities, binding, and constraints by hand — the other four endpoints surface a field-level path.

Escape hatch

For any field not listed here:

Requirements and compatibility

Written for: OpenAI Codex

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

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Attribution and licence

Created by
Boltz
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/plugins · plugins/boltz-api-cli/skills/boltz-structure-and-binding · commit d420d13
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from plugins/boltz-api-cli/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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