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--- |
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license: mit |
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tags: |
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- METL |
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- biology |
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- protein |
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--- |
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# METL |
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Mutational Effect Transfer Learning (METL) is a framework for pretraining and finetuning biophysics-informed protein language models. |
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## Model Details |
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This repository contains a wrapper meant to facilitate the ease of use of METL models. |
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Usage of this wrapper will be provided below. |
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Models are hosted on [Zenodo](https://zenodo.org/doi/10.5281/zenodo.11051644) and will be downloaded by this wrapper when used. |
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### Model Description |
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METL is discussed in the [paper](https://doi.org/10.1038/s41592-025-02776-2) in further detail. |
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The GitHub [repo](https://github.com/gitter-lab/metl) contains more documentation and includes scripts for training and predicting with METL. |
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Google Colab notebooks for finetuning and predicting on publicly available METL models are available as well [here](https://github.com/gitter-lab/metl/tree/main/notebooks). |
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### Model Sources |
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- **Repository:** [METL repo](https://github.com/gitter-lab/metl) |
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- **Paper:** [METL publication](https://doi.org/10.1038/s41592-025-02776-2) |
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- **Demo:** [Hugging Face Spaces demo](https://huggingface.co/spaces/gitter-lab/METL_demo) |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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Running METL requires the following packages: |
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``` |
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transformers==4.42.4 |
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numpy>=1.23.2 |
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networkx>=2.6.3 |
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scipy>=1.9.1 |
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biopandas>=0.2.7 |
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``` |
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In order to run the example, a PDB file for the GB1 protein structure must be installed. |
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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). |
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After installing those packages and downloading the above file, you may run METL with the following code example (assuming the downloaded file is in the same place as the script): |
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```python |
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from transformers import AutoModel |
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import torch |
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metl = AutoModel.from_pretrained('gitter-lab/METL', trust_remote_code=True) |
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model = "metl-l-2m-3d-gb1" |
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wt = "MQYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTE" |
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variants = '["T17P,T54F", "V28L,F51A"]' |
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pdb_path = './2qmt_p.pdb' |
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metl.load_from_ident(model_id) |
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metl.eval() |
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encoded_variants = metl.encoder.encode_variants(sequence, variant) |
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with torch.no_grad(): |
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predictions = metl(torch.tensor(encoded_variants), pdb_fn=pdb_path) |
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``` |
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## Citation |
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[Biophysics-based protein language models for protein engineering](https://doi.org/10.1038/s41592-025-02776-2). |
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Sam Gelman, Bryce Johnson, Chase R Freschlin, Arnav Sharma, Sameer D'Costa, John Peters, Anthony Gitter<sup>+</sup>, Philip A Romero<sup>+</sup>. |
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*Nature Methods* 22, 2025. |
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<sup>+</sup> denotes equal contribution. |
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## Model Card Contact |
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For questions and comments about METL, the best way to reach out is through opening a GitHub issue in the [METL repository](https://github.com/gitter-lab/metl/issues). |
