gogpt-test

GO-GPT: Gene Ontology Prediction from Protein Sequences

GO-GPT is a decoder-only transformer model for predicting Gene Ontology (GO) terms from protein sequences. It combines ESM2 protein language model embeddings with an autoregressive decoder to generate GO term annotations across all three ontology aspects: Molecular Function (MF), Biological Process (BP), and Cellular Component (CC).

Quick Start

1. Clone the repository:

git clone https://github.com/YOUR_ORG/gogpt
cd gogpt

1. Run the inference notebook or use Python directly:

import sys
sys.path.insert(0, "src")

from gogpt import GOGPTPredictor

# Load from HuggingFace (downloads ~4GB on first run)
predictor = GOGPTPredictor.from_pretrained("armansa1/gogpt-dev")

# Predict GO terms
predictions = predictor.predict(
    sequence="MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQQIAAALEHHHHHH",
    organism="Homo sapiens"
)

print(predictions)
# {'MF': ['GO:0003674', 'GO:0005488', ...],
#  'BP': ['GO:0008150', 'GO:0008152', ...],
#  'CC': ['GO:0005575', 'GO:0110165', ...]}

Model Architecture

Supported Organisms

GO-GPT supports organism-conditioned prediction for 200 organisms plus an <UNKNOWN> category (201 total). See organism_list.txt for the full list.

Common organisms include:

• Homo sapiens
• Mus musculus
• Escherichia coli (various strains)
• Saccharomyces cerevisiae
• Arabidopsis thaliana
• Drosophila melanogaster

For organisms not in the training set, predictions will use the <UNKNOWN> embedding.

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