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.

Vedi sorgente

Come usare

  1. Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
  2. Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
  3. Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/nvidia-bionemo/bionemo-agent-toolkit --skill rfd4-proteina-annotated-cif

Prima di iniziare

  • L’installazione non include abbonamenti, quote API o accesso ai file.
  • Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale

Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.

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

Correlati per attività

dallo stesso repository

Altri Skills

Tutti gli Skills
nvidia-bionemo
Community

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

installazioni
2
GitHub Stars
488
Aggiornato
21 ott
nvidia-bionemo
Community

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

installazioni
2
GitHub Stars
488
Aggiornato
21 ott
nvidia-bionemo
Community

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.

installazioni
2
GitHub Stars
488
Aggiornato
21 ott
nvidia-bionemo
Community

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.

installazioni
2
GitHub Stars
488
Aggiornato
21 ott