---
library_name: transformers
tags:
- gene-ontology
- proteomics
datasets:
- andrewdalpino/AmiGO
metrics:
- precision
- recall
- f1
base_model:
- facebook/esm2_t30_150M_UR50D
pipeline_tag: text-classification
---

# ESM2 Protein Function Caller

An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on [UniRef50](https://www.uniprot.org/help/uniref), and fine-tuned on the [AmiGO](https://huggingface.co/datasets/andrewdalpino/AmiGO) dataset, this model predicts the GO subgraph for a particular protein sequence - giving you insight into the molecular function, biological process, and location of the activity inside the cell.

**Note**: This version only models the `molecular function` subgraph of the gene ontology.

## What are GO terms?

> "The Gene Ontology (GO) is a concept hierarchy that describes the biological function of genes and gene products at different levels of abstraction (Ashburner et al., 2000). It is a good model to describe the multi-faceted nature of protein function."

> "GO is a directed acyclic graph. The nodes in this graph are functional descriptors (terms or classes) connected by relational ties between them (is_a, part_of, etc.). For example, terms 'protein binding activity' and 'binding activity' are related by an is_a relationship; however, the edge in the graph is often reversed to point from binding towards protein binding. This graph contains three subgraphs (subontologies): Molecular Function (MF), Biological Process (BP), and Cellular Component (CC), defined by their root nodes. Biologically, each subgraph represent a different aspect of the protein's function: what it does on a molecular level (MF), which biological processes it participates in (BP) and where in the cell it is located (CC)."

From [CAFA 5 Protein Function Prediction](https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/data)

## Code Repository

https://github.com/andrewdalpino/esm2-function-classifier

## Model Specs

- **Vocabulary Size**: 33
- **Embedding Dimensions**: 640
- **Attention Heads**: 20
- **Encoder Layers**: 30
- **Context Length**: 1026

## Basic Example

For a basic demonstration we can rank the GO terms for a particular sequence. For a more advanced example see the [predict-subgraph.py](https://github.com/andrewdalpino/esm2-function-classifier/blob/master/predict-subgraph.py) source file.

```python
import torch

from transformers import EsmTokenizer, EsmForSequenceClassification

model_name = "andrewdalpino/ESM2-35M-Protein-Molecular-Function"

tokenizer = EsmTokenizer.from_pretrained(model_name)

model = EsmForSequenceClassification.from_pretrained(model_name)

model.eval()

sequence = "MCNAWYISVDFEKNREDKSKCIHTRRNSGPKLLEHVMYEVLRDWYCLEGENVYMM"

top_k = 10

out = tokenizer(sequence)

input_ids = out["input_ids"]

input_ids = torch.tensor(input_ids, dtype=torch.int64).unsqueeze(0)

with torch.no_grad():
    outputs = model.forward(input_ids)

    probabilities = torch.sigmoid(outputs.logits.squeeze(0))

    probabilities, indices = torch.topk(probabilities, top_k)

probabilities = probabilities.tolist()

terms = [model.config.id2label[index] for index in indices.tolist()]

print(f"Top {args.top_k} GO Terms:")

for term, probability in zip(terms, probabilities):
    print(f"{probability:.4f}: {term}")
```

## References:

>- A. Rives, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences, 2021.
>- Z. Lin, et al. Evolutionary-scale prediction of atomic level protein structure with a language model, 2022.
>- G. A. Merino, et al. Hierarchical deep learning for predicting GO annotations by integrating protein knowledge, 2022.
>- M. Ashburner, et al. Gene Ontology: tool for the unification of biology, 2000.