protein-subcellular-localization-prediction-biot5

name	protein-subcellular-localization-prediction-biot5
description	Predict protein subcellular localization from amino acid sequence using BioT5. Use this skill when: (1) You have a protein sequence and want to know where it localizes in the cell, (2) You need to identify cellular compartment (nucleus, cytoplasm, membrane, etc.), (3) You want quick localization prediction without experimental data.
license	MIT
category	protein-engineering
tags	["protein","subcellular-localization","localization","sequence-analysis","biot5"]

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

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):

# 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