README.md

---
datasets:
- andrewdalpino/AmiGO-Boost
metrics:
- precision
- recall
- f1
base_model:
- EvolutionaryScale/esmc-300m-2024-12
pipeline_tag: text-classification
tags:
- gene-ontology
---

# ESMC Protein Function Predictor

An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM Cambrian Transformer architecture, pre-trained on [UniRef](https://www.uniprot.org/help/uniref), [MGnify](https://www.ebi.ac.uk/metagenomics), and the Joint Genome Institute's database and fine-tuned on the [AmiGO Boost](https://huggingface.co/datasets/andrewdalpino/AmiGO-Boost) protein function 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.

## 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)

## Pretrained Models

The following pretrained models are available on HuggingFace Hub.

| Name | Embedding Dim. | Attn. Heads | Encoder Layers | Context Length | QAT | Total Parameters |
|---|---|---|---|---|---|---|
| [andrewdalpino/ESMC-300M-Protein-Function](https://huggingface.co/andrewdalpino/ESMC-300M-Protein-Function) | 960 | 15 | 30 | 2048 | None | 361M |
| [andrewdalpino/ESMC-300M-QAT-Protein-Function](https://huggingface.co/andrewdalpino/ESMC-300M-QAT-Protein-Function) | 960 | 15 | 30 | 2048 | int8w | 361M |
| [andrewdalpino/ESMC-600M-Protein-Function](https://huggingface.co/andrewdalpino/ESMC-600M-Protein-Function) | 1152 | 18 | 36 | 2048 | None  | 644M |
| [andrewdalpino/ESMC-600M-QAT-Protein-Function](https://huggingface.co/andrewdalpino/ESMC-600M-QAT-Protein-Function) | 1152 | 18 | 36 | 2048 | int8w | 644M |

## Basic Pretrained Example

First, install the `esmc_function_classifier` package using [pip](https://pypi.org/project/pip/).

```sh
pip install esmc_function_classifier
```

Then, we'll load the model weights from HuggingFace Hub, tokenize the amino acid sequence, and infer the GO terms.

```python
import torch

from esm.tokenization import EsmSequenceTokenizer

from esmc_function_classifier.model import EsmcGoTermClassifier


model_name = "andrewdalpino/ESMC-300M-Protein-Function"

sequence = "MPPKGHKKTADGDFRPVNSAGNTIQAKQKYSIDDLLYPKSTIKNLAKETLPDDAIISKDALTAIQRAATLFVSYMASHGNASAEAGGRKKIT"

top_p = 0.5

tokenizer = EsmSequenceTokenizer()

model = EsmcGoTermClassifier.from_pretrained(model_name)

out = tokenizer(sequence, max_length=2048, truncation=True)

input_ids = torch.tensor(out["input_ids"], dtype=torch.int64)

go_term_probabilities = model.predict_terms(
    input_ids, top_p=top_p
)
```

You can also output the gene-ontology (GO) `networkx` subgraph for a given sequence like in the example below. You'll need an up-to-date gene ontology database that you can import using the `obonet` package.

```sh
pip install obonet
```

```python
import networkx as nx

import obonet


# Visit https://geneontology.org/docs/download-ontology/ to download.
go_db_path = "./dataset/go-basic.obo"

graph = obonet.read_obo(go_db_path)

model.load_gene_ontology(graph)

subgraph, go_term_probabilities = model.predict_subgraph(
    input_ids, top_p=top_p
)

json = nx.node_link_data(subgraph)

print(json)
```

### Quantized Model

To quantize the model weights using int8 call the `quantize_weights()` method. Any model can be quantized, but we recommend one that has been quantization-aware trained (QAT) for the best performance. The `group_size` argument controls the granularity at which quantization scales are computed.

```python
model.quantize_weights(group_size=64)
```

## Code Repository

The training code can be found at [https://github.com/andrewdalpino/ESMC-Function-Classifier](https://github.com/andrewdalpino/ESMC-Function-Classifier).

## References:

>- T. Hayes, et al. Simulating 500 million years of evolution with a language model, 2024.
>- M. Ashburner, et al. Gene Ontology: tool for the unification of biology, 2000.