Metadata-Version: 2.4
Name: esmc-protein-function
Version: 1.0.0
Summary: An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
Author: Andrew DalPino
License: MIT
Project-URL: Homepage, https://github.com/andrewdalpino/ESMC-Protein-Function
Project-URL: Documentation, https://github.com/andrewdalpino/ESMC-Protein-Function/README.md
Project-URL: Source, https://github.com/andrewdalpino/ESMC-Protein-Function
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.md
License-File: NOTICE
Requires-Dist: esm~=3.2.3
Requires-Dist: torch~=2.7.1
Requires-Dist: torchao~=0.12.0
Requires-Dist: huggingface_hub~=0.33.4
Requires-Dist: networkx~=3.4.2
Provides-Extra: dev
Requires-Dist: mypy; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: matplotlib; extra == "dev"
Provides-Extra: test
Requires-Dist: mypy; extra == "test"
Requires-Dist: black; extra == "test"
Dynamic: license-file

# 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 protein language 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.

## Key Features

- **Sequence-to-function prediction** — Predicts Molecular Function, Biological Process, and Cellular Component ontologies directly from raw amino acid sequences, eliminating the need for homology searches, structural data, or multiple sequence alignments.

- **Hierarchy-aware GO subgraph reconstruction** — Outputs a full GO directed acyclic graph (DAG) ensuring predictions respect the ontology structure rather than treating each term as an independent binary label.

- **Efficient inference at scale** — Supports weight quantization and quantization-aware training (QAT), enabling memory-efficient, high-throughput screening of large sequence datasets without accuracy loss.

## 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)

## V1 Pretrained Models

The following pretrained models are available on HuggingFace Hub and require the `esmc-protein-function` library version `1.x.x` for inference. All V1 models have been optimized with quantization-aware post-training.

| Name | Embedding Dimensions | Encoder Layers | Context Length | Total Parameters |
| --- | --- | --- | --- | --- |
| [andrewdalpino/ESMC-Protein-Function-V1-300M](https://huggingface.co/andrewdalpino/ESMC-Protein-Function-V1-300M) | 960 | 30 | 2048 | 397M |

## V0 Pretrained Models

The following pretrained models are available on HuggingFace Hub and require the `esmc_function_classifier` library version `0.1.x` for inference.

| Name | Embedding Dimensions | Encoder Layers | Context Length | Total Parameters |
| --- | --- | --- | --- | --- |
| [andrewdalpino/ESMC-Protein-Function-V0-300M](https://huggingface.co/andrewdalpino/ESMC-Protein-Function-V0-300M) | 960 | 30 | 2048 | 361M |
| [andrewdalpino/ESMC-Protein-Function-V0-300M-QAT](https://huggingface.co/andrewdalpino/ESMC-Protein-Function-V0-300M-QAT) | 960 | 30 | 2048 | 361M |
| [andrewdalpino/ESMC-Protein-Function-V0-600M](https://huggingface.co/andrewdalpino/ESMC-Protein-Function-V0-600M) | 1152 | 36 | 2048 | 644M |
| [andrewdalpino/ESMC-Protein-Function-V0-600M-QAT](https://huggingface.co/andrewdalpino/ESMC-Protein-Function-V0-600M-QAT) | 1152 | 36 | 2048 | 644M |

## Basic Pretrained Example

First, install the `esmc-protein-function` package using [pip](https://pypi.org/project/pip/).

```sh
pip install esmc-protein-function
```

Then, we'll load the model weights from HuggingFace Hub by calling the `from_pretrained()` method. We'll also need the ESM tokenizer from the `esm` library. Then, tokenize the sequence and query the model like in the example below.

```python
import torch

from esm.tokenization import EsmSequenceTokenizer

from esmc_protein_function.model import ESMCProteinFunction


model_name = "andrewdalpino/ESMC-Protein-Function-V1-300M"

sequence = "MPPKGHKKTADGDFRPVNSAGNTIQAKQKYSIDDLLYPKSTIKNLAKETLPDDAIISKDALTAIQRAATLFVSYMASHGNASAEAGGRKKIT"

top_p = 0.5

tokenizer = EsmSequenceTokenizer()

model = ESMCProteinFunction.from_pretrained(model_name)

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

input_ids = torch.tensor(out["input_ids"], dtype=torch.int32)

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

### Predict GO Subgraph

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
```

Then, load the GO DAG and call the `predict_all_subgraphs()` method like in the example below.

```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_all_subgraphs(
    input_ids, top_p=top_p
)

json = nx.node_link_data(subgraph)

print(json)
```

![Example GO Subgraph](https://raw.githubusercontent.com/andrewdalpino/esm2-function-classifier/master/docs/images/Q0E9J9-mf.png)

### 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)
```

## 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.
