nvidia-bionemo/bionemo-agent-toolkit

rfd4-proteina-annotated-cif

Use when creating, inspecting, editing, or validating RFD4-Proteina annotated CIF inputs and conditioning with AtomWorks.

ソースを見る

使い方

  1. インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
  2. 必要なツール、認証情報、対応環境を原文で確認します。
  3. 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/nvidia-bionemo/bionemo-agent-toolkit --skill rfd4-proteina-annotated-cif

開始前の確認

  • 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
  • スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
リポジトリの原文

見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。

RFProteína annotated CIF authoring

Purpose

An annotated CIF is a structure plus AtomWorks StandardAnnotation columns that encode the RFProteína design problem. Prefer direct Python manipulation of an AtomArrayPlus: parse, derive selection annotations, set conditions, validate, write, and round-trip. Visually inspect chemistry and intent, especially for ligands, metals, PTMs, or PDB sources.

This skill defines CIF authoring semantics. Workspace instructions and the active campaign govern shared-input ownership, output locations, length materialization, generation, and database accounting. Authoring a CIF does not by itself authorize a generation run or establish a campaign ledger policy.

Prerequisites

An RFD4-Proteina environment with AtomWorks, NumPy, Biotite, and Python Fire; access to the matching input manual and source structures.

Use the user-selected model checkout at RFPROTEINA_ROOT for model commands, configs, and upstream manuals. This catalog contains the skills, not that checkout. In helper examples, resolve open-models-skills/rfd4-proteina/<name>/... to its absolute installed path when the catalog and model checkout are separate.

Inputs

Required: the user-requested operation, source structure paths/provenance, and destination for authored CIFs. Optional: assembly, chains, and conditioning selections when already unambiguous. Reuse established intent; resolve any ambiguity that changes the design problem.

Instructions

  1. Inspect the source, existing conditions, requested assembly, and design intent.

Ask only about ambiguities that change the design problem.

  1. Read python-authoring.md for registry import,

parser selection, condition edits, expandable segments, and combining arrays.

  1. Read annotators-and-selections.md

when translating structural intent into atom masks or checking installed annotation support.

  1. Use the canonical condition reference

and interaction rules for condition levels, values, and model support. Do not infer support from an annotation's ability to round-trip.

  1. Complete validation.md, including chemistry and

indexing review, pre-save checks, and save/reload comparison.

For a saved CIF, use bash open-models-skills/rfd4-proteina/rfd4-proteina-annotated-cif/scripts/inspect_cif.sh <file.cif> to inventory its structure, chemistry, expandable segments, and conditions. Use bash open-models-skills/rfd4-proteina/rfd4-proteina-annotated-cif/scripts/check_annotated_cif.sh <file.cif> for pipeline validation and chemistry checks. Both helpers are read-only; see the validation reference for options and limits.

Output standard

Return the annotated CIF artifacts required by the task, identifying source templates and any sampled children. Report input provenance and parse preset, selections and conditioning counts, segment and chemistry edits, validation results, and any unresolved scientific ambiguity.

Limitations

Successful parsing and round-trip checks do not establish biological correctness. Pipeline validation samples one expandable-segment realization, not every possible length.

Troubleshooting

If an import or annotation is unavailable, check the selected environment and installed registries. If validation or round-trip comparison fails, retain the source and report the specific condition or chemistry mismatch.

Examples

Inspect and validate a saved input without modifying it:

bash
bash open-models-skills/rfd4-proteina/rfd4-proteina-annotated-cif/scripts/inspect_cif.sh input.cif
bash open-models-skills/rfd4-proteina/rfd4-proteina-annotated-cif/scripts/check_annotated_cif.sh input.cif --strict

For authoring, follow the Python recipes and save/round-trip checks.

Available Scripts

ScriptPurposeArguments
scripts/inspect_cif.shRead-only structure and condition inventoryCIF path
scripts/check_annotated_cif.shValidate conditions and chemistryCIF path; --strict, --quiet, --skip_pipeline

タスクに関連する項目

同じリポジトリから

関連する Skills

すべての Skills
nvidia-bionemo
コミュニティ

msa-search-nim

Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Use for homolog search, UniRef30/ColabFold env searches, A3M or FASTA alignments, paired MSA search for complexes, PDB70 structural templates, hosted NVIDIA API calls, or local Docker deployment. For local deployment, download the databases in parallel with aria2c and launch via NIMMODELNAME (the recommended default fast path, ~14 min vs over 80 min for the built-in downloader); a plain docker

導入数
2
GitHub Stars
488
更新日
10月21日
nvidia-bionemo
コミュニティ

msa-structure-prediction-pipeline

NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with ma

導入数
2
GitHub Stars
488
更新日
10月21日
nvidia-bionemo
コミュニティ

openfold3-nim

Use this skill for OpenFold3, NVIDIA's BioNeMo NIM microservice for biomolecular structure prediction. Invoke whenever the user mentions OpenFold3 or needs protein, protein-ligand, protein-DNA/RNA, or multi-chain complex prediction with the hosted NVIDIA API or local Docker NIM. Covers endpoint choice, auth, request payloads, output artifacts, confidence scores, and local container setup.

導入数
2
GitHub Stars
488
更新日
10月21日
nvidia-bionemo
コミュニティ

protein-binder-design

Orchestrate an end-to-end de novo protein binder design campaign against a protein target by composing BioNeMo NIM skills. Use for binder design, minibinder design, de novo binders, RFdiffusion + ProteinMPNN + Boltz2/OpenFold3 pipelines, epitope/hotspot-targeted design, in-silico binder validation, and ranking designs by interface confidence.

導入数
2
GitHub Stars
488
更新日
10月21日