Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
First-time setup for protein design tools. Use this skill when: (1) User is new and hasn't run any tools yet, (2) Commands fail with "file not found" or "modal: command not found", (3) Modal authentication errors occur, (4) User asks how to get started or set up the environment, (5) biomodals directory is missing or tools aren't working.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Help users get their environment ready to run protein design tools.
Run through this checklist when a user encounters setup issues:
| Step | Check | Fix |
|---|---|---|
| 1. Modal CLI | modal --version | pip install modal |
| 2. Modal auth | modal token show | modal setup |
| 3. biomodals | ls biomodals/modal_*.py | git clone https://github.com/hgbrian/biomodals |
| 4. Test | cd biomodals && modal run modal_boltzgen.py --help | See troubleshooting |
Cause: Modal CLI not installed.
Fix:
pip install modal
Then restart the terminal or run hash -r.
Cause: Modal not authenticated.
Fix:
modal setup
This opens a browser. Click "Authorize" to complete authentication.
Cause: biomodals repository not cloned or not in correct directory.
Fix:
git clone https://github.com/hgbrian/biomodals
cd biomodals
Cause: uvx is an optional wrapper from the uv package. It's not required.
Fix: Run modal directly (recommended):
modal run modal_boltzgen.py --help
Or install uv if you prefer using uvx:
pip install uv
pip install modal
Verify: modal --version
modal setup
This opens a browser. Click "Authorize".
Verify: modal token show
git clone https://github.com/hgbrian/biomodals
cd biomodals
Verify: ls modal_*.py should show files like modal_boltzgen.py
cd biomodals
modal run modal_boltzgen.py --help
Expected: Usage instructions appear showing --input-yaml, --protocol, --num-designs options.
Once setup is complete, users can:
cd biomodals
# Design binders with BoltzGen (requires YAML config)
modal run modal_boltzgen.py --input-yaml binder.yaml --protocol protein-anything --num-designs 50
# Generate backbones with RFdiffusion
modal run modal_rfdiffusion.py --pdb target.pdb --contigs "A1-150/0 70-100" --num-designs 100
# Validate with Chai
modal run modal_chai1.py --input-faa designs.fasta
Set GPU with environment variable:
GPU=A10G modal run modal_rfdiffusion.py --pdb target.pdb --contigs "A1-100/0 50-80" --num-designs 10
GPU=L40S modal run modal_boltzgen.py --input-yaml config.yaml --num-designs 50
GPU=A100 modal run modal_chai1.py --input-faa complex.fasta
| GPU | VRAM | Best For |
|---|---|---|
| T4 | 16GB | ProteinMPNN, ESM |
| A10G | 24GB | RFdiffusion, Chai |
| L40S | 48GB | BoltzGen, BindCraft |
| A100 | 40-80GB | Large complexes |
Modal offers $30/month in free credits - enough for:
Full documentation: See Installation Guide
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
日本語の概要は準備中です。原文の説明を表示しています。
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
日本語の概要は準備中です。原文の説明を表示しています。
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
日本語の概要は準備中です。原文の説明を表示しています。