---
license: mit
tags:
- METL
---

# METL

<!-- Provide a quick summary of what the model is/does. -->

Mutational Effect Transfer Learning (METL) is a framework for pretraining and finetuning biophysics-informed protein language models. 


## Model Details

This 🤗 repository contains a wrapper meant to facilitate the ease of use of METL models. Usage of this wrapper will be provided below. Models are hosted on (Zenodo)[https://zenodo.org/records/11051645] and will be downloaded by this wrapper when used.

### Model Description

METL is discussed in the (paper)[https://www.biorxiv.org/content/10.1101/2024.03.15.585128v1] in further detail.

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [https://github.com/gitter-lab/metl]
- **Paper:** [https://www.biorxiv.org/content/10.1101/2024.03.15.585128v1]
- **Demo:** [https://huggingface.co/spaces/gitter-lab/METL_demo]

## How to Get Started with the Model

Use the code below to get started with the model.

Running METL requires the following packages:
```
transformers==4.42.4
numpy>=1.23.2
networkx>=2.6.3
scipy>=1.9.1
biopandas>=0.2.7
```

In order to run the example, a PDB file must be installed. It is provided (here)[https://github.com/gitter-lab/metl-pretrained/blob/main/pdbs/2qmt_p.pdb] and in raw format (here)[https://raw.githubusercontent.com/gitter-lab/metl-pretrained/main/pdbs/2qmt_p.pdb].

After installing those packages and the above file, you may run METL with the following code example (assuming the downloaded file is in the same place as the script):

```python
from transformers import AutoModel
import torch

metl = AutoModel.from_pretrained('gitter-lab/METL', trust_remote_code=True)


model = "metl-l-2m-3d-gb1"
wt = "MQYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTE"
variants = '["T17P,T54F", "V28L,F51A"]'
pdb_path = './2qmt_p.pdb'

metl.load_from_ident(model_id)

metl.eval()

encoded_variants = metl.encoder.encode_variants(sequence, variant)

with torch.no_grad():   
  predictions = metl(torch.tensor(encoded_variants), pdb_fn=pdb_path)
  
```

## Training Details

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

Biophysics-based protein language models for protein engineering
Sam Gelman, Bryce Johnson, Chase Freschlin, Sameer D’Costa, Anthony Gitter, Philip A. Romero
bioRxiv 2024.03.15.585128; doi: https://doi.org/10.1101/2024.03.15.585128

## Model Card Contact