# Pharmolix Openbiomed Openbiomed

> Protein Subcellular Localization Prediction

- Skill: `tomevault-io/pharmolix-openbiomed-openbiomed` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/pharmolix-openbiomed-openbiomed`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/pharmolix-openbiomed-openbiomed/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/pharmolix-openbiomed-openbiomed

---


# Protein Subcellular Localization Prediction

Predict subcellular localization for proteins from their amino acid sequences using the BioT5 model.

## When to Use

- You have a protein FASTA sequence and need to know its cellular location
- You want to identify if a protein is cytoplasmic, nuclear, membrane-bound, or secreted
- You need quick localization insights without experimental data
- You're characterizing novel or unannotated protein sequences

## Workflow

```python
from open_biomed.data import Protein, Text
from open_biomed.core.pipeline import InferencePipeline

# Create protein from FASTA sequence
protein = Protein.from_fasta("YOUR_AMINO_ACID_SEQUENCE")

# Create the question for subcellular localization
question = Text.from_str(
    "Please provide information about the subcellular localization of this protein."
)

# Load the BioT5 model for protein question answering
pipeline = InferencePipeline(
    task="protein_question_answering",
    model="biot5",
    model_ckpt="./checkpoints/server/protein_question_answering_biot5.ckpt",
    device="cuda:0"
)

# Run inference to get localization prediction
outputs = pipeline.run(protein=protein, text=question)
localization = outputs[0][0].str
print(localization)
```

See `examples/basic_example.py` for a complete runnable script.

## Expected Outputs

The model returns subcellular localization information:

| Output | Description |
|--------|-------------|
| **Cytoplasm** | Cytoplasmic proteins, soluble enzymes |
| **Nucleus** | Nuclear proteins, transcription factors |
| **Membrane** | Membrane-bound proteins, receptors |
| **Secreted** | Extracellular proteins, secreted factors |
| **Mitochondria** | Mitochondrial proteins |
| **Peroxisome** | Peroxisomal enzymes |
| **Endoplasmic reticulum** | ER-resident proteins |
| **Golgi apparatus** | Golgi-localized proteins |

### Example Output

> Cytoplasm

## Input Formats

The skill accepts protein sequences in FASTA format (amino acid string):

```python
# From raw sequence string
protein = Protein.from_fasta("MRVGVIRFPGSNCDRDVHHVLELAGAEPEYVWW...")

# From UniProt (get sequence first)
from open_biomed.tools.tool_registry import TOOLS
tool = TOOLS["protein_uniprot_request"]
protein, _ = tool.run(accession="P00533")  # Example: EGFR
```

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| `FileNotFoundError` | Model checkpoint not found | Download checkpoint to `./checkpoints/server/` |
| `CUDA out of memory` | GPU memory insufficient | Use smaller batch or CPU device |
| `Sequence too long` | Exceeds 512 amino acid limit | Truncate sequence or use sliding window |

## Model Details

- **Model**: BioT5 (protein-text foundation model)
- **Max sequence length**: 512 amino acids
- **Inference time**: ~2-3 seconds per sequence on GPU
- **Capabilities**: Subcellular localization prediction

## Limitations

- Sequences longer than 512 residues are truncated
- Model trained on known proteins; novel sequences may have lower accuracy
- Single localization prediction (does not handle multi-localized proteins well)

## Related Skills

- `protein-function-annotation`: For function prediction
- `protein-mutation-analysis`: For mutation effect prediction
- `uniprot-query`: For retrieving protein metadata from UniProt

---
> Source: [PharMolix/OpenBioMed](https://github.com/PharMolix/OpenBioMed) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-06-19 -->