Protein Function Prediction

Protein Function Prediction

Predict functional annotations and properties for proteins from their amino acid sequences using the BioT5 model.

When to Use

• You have a protein FASTA sequence and need to understand its biological role
• You want to identify enzyme function, pathway involvement, or molecular mechanisms
• You need quick functional 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 functional annotation
question = Text.from_str(
    "Inspect the protein sequence and offer a concise description of its properties."
)

# 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 functional annotation
outputs = pipeline.run(protein=protein, text=question)
function_description = outputs[0][0].str
print(function_description)

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

Expected Outputs

The model returns a text description that typically includes:

Example Output

Part of the phosphoribosylformylglycinamidine synthase complex involved in the purines biosynthetic pathway. Catalyzes the ATP-dependent conversion of formylglycinamide ribonucleotide (FGAR) and glutamine to yield formylglycinamidine ribonucleotide (FGAM) and glutamate.

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

Model Details

• Model: BioT5 (protein-text foundation model)
• Max sequence length: 512 amino acids
• Inference time: ~2-3 seconds per sequence on GPU
• Capabilities: Function prediction, property description, pathway annotation

Limitations

• Sequences longer than 512 residues are truncated
• Model trained on known proteins; novel folds may have lower accuracy
• Does not predict 3D structure or binding sites (use protein_folding or protein_binding_site_prediction tools)

Related Skills

• protein-structure-design-boltzgen: For 3D structure prediction
• protein-mutation-analysis: For mutation effect prediction
• uniprot-query: For retrieving protein metadata from UniProt