dyno-phi

Phi CLI for the dyno protein structure analysis platform.

phi is the command-line interface for uploading protein structures, running structure prediction and inverse-folding pipelines, filtering and scoring candidates, and downloading results from the dyno API.

Results and scores are viewable in the web dashboard at design.dynotx.com/dashboard/datasets/<dataset_id>.

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Table of contents

• Installation
• Authentication
• Quick start
• Command reference
• Filter presets
• State caching
• Claude Code skill
• Development

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Installation

pip install dyno-phi

Requires Python ≥ 3.9.

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Authentication

Create an API key at Settings → API keys in the dyno web app (https://design.dynotx.com/dashboard/settings), then export it:

export DYNO_API_KEY=ak_...

The key is cached to .phi/state.json on first use so you don't need to re-export it in future sessions.

Verify your connection:

phi login

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Quick start

Try the tutorial

The fastest way to get started — downloads five example PD-L1 binder structures and walks you through the full pipeline:

phi tutorial

This fetches the example files, prints step-by-step instructions, and leaves you ready to run phi filter.

Scoring your own structures

# 1. Upload PDB/CIF files
phi upload ./designs/

# 2. Run the full filter pipeline
phi filter --preset default --wait

# 3. View scores and download results
phi scores
phi download --out ./results/

After each command, phi prints the active dataset and a link to the dashboard:

Active: dataset [d7c3a1b2-...] · job [cb4553f5-...]
Dashboard: https://design.dynotx.com/dashboard/datasets/d7c3a1b2-...

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Command reference

Command	Alias	Description
phi tutorial	—	Download example structures and print a step-by-step walkthrough
phi login	—	Verify API key and print identity
phi upload	—	Upload PDB/CIF files or a directory
phi fetch	—	Download a structure from RCSB PDB or AlphaFold DB, crop, and optionally upload
phi datasets	—	List datasets
phi dataset	—	Show dataset details
phi use <dataset_id>	—	Set active dataset (cached to .phi/state.json)
phi folding	esmfold	Single-sequence structure prediction (ESMFold)
phi complex_folding	alphafold	Multi-chain complex prediction (AlphaFold2 multimer)
phi inverse_folding	proteinmpnn	Sequence design via inverse folding (ProteinMPNN)
phi esm2	—	Sequence embedding and scoring (ESM2)
phi boltz	—	Structure prediction (Boltz-1)
phi filter	—	Full filter pipeline: inverse folding → folding → complex folding → score
phi status <job_id>	—	Poll job status
phi jobs	—	List recent jobs
phi logs <job_id>	—	Stream job logs
phi cancel <job_id>	—	Cancel a running job
phi scores	—	Display scores table for a completed filter job
phi download	—	Download job artifacts (structures, scores, raw JSONs)
phi research	—	Run a research query against the platform
phi notes	—	Manage dataset research notes

Common flags

Flag	Commands	Description
--dataset-id ID	most	Target dataset (omit to use cached)
--wait	most	Poll until job completes, then print summary
--out DIR	download, scores	Output directory (default: ./results)
--preset default|relaxed	filter	Filter threshold preset
--num-sequences N	inverse_folding	Sequences per design (default: 4)
--models 1,2	complex_folding	AlphaFold2 model numbers (default: 1,2,3)
--poll-interval S	global	Seconds between status polls (default: 5)

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Filter presets

phi filter applies a multi-stage quality-control pipeline and scores each design against configurable thresholds.

Metric	default	relaxed	Description
pLDDT	≥ 0.80	≥ 0.80	ESMFold per-residue confidence
pTM	≥ 0.55	≥ 0.45	Global TM-score proxy (ESMFold)
ipTM	≥ 0.50	≥ 0.50	Interface pTM (AF2 multimer)
iPAE	≤ 10.85 Å	≤ 12.4 Å	AF2 interface PAE in Å
RMSD	≤ 3.5 Å	≤ 4.5 Å	Backbone RMSD vs. reference

Override any threshold with an explicit flag:

phi filter --dataset-id ... --plddt 0.75 --iptm 0.45

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State caching

phi caches the most recently used dataset ID, job ID, and API key in .phi/state.json so you don't need to pass --dataset-id or re-export your key repeatedly:

phi use d7c3a1b2-...         # set active dataset
phi filter --preset default  # uses cached dataset
phi scores                   # uses cached job
phi download --out ./results # uses cached job

The dashboard URL for the active dataset is printed after every command:

Dashboard: https://design.dynotx.com/dashboard/datasets/d7c3a1b2-...

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Claude Code skill

The phi skill is bundled at skills/phi/SKILL.md and is automatically available when you open this repo in Claude Code (Cursor or the claude CLI). No installation needed — just open the project and ask naturally:

Upload the PDB files in ./examples/ and run the default filter pipeline.

To make the skill available in all your projects (outside this repo):

mkdir -p ~/.claude/skills/phi
cp skills/phi/SKILL.md ~/.claude/skills/phi/SKILL.md

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Development

git clone https://github.com/dynotx/phi-cli
cd phi-cli
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Lint
ruff check src/

# Type check
mypy src/phi/

# Run tests
pytest tests/

Releasing to PyPI

Releases are published via GitHub Actions. Push a version tag to trigger the workflow:

# Bump src/phi/_version.py, update CHANGELOG.md, then:
git tag v0.1.0
git push origin main --tags

The workflow publishes to TestPyPI automatically, then waits for manual approval before publishing to the real PyPI. See .github/workflows/publish.yml for details.