protein-function-prediction

Research

Predict protein function and properties from amino acid sequence using BioT5. Use this skill when: (1) You have a protein sequence and want to understand its biological function, (2) You need to identify enzyme activity, pathway involvement, or molecular interactions, (3) You want a concise description of protein properties from sequence alone.

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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:

Output Component	Example
Enzyme name	Phosphoribosylformylglycinamidine synthase
Biological pathway	Purine biosynthesis pathway
Catalytic activity	FGAR to FGAM conversion
Complex membership	Part of FGAM synthase complex (PurQ, PurL, PurS)
Mechanism details	ATP-dependent, glutamine amidotransferase activity

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

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: 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