Update README.md

README.md

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----
-license: mit
----
+---
+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
+
+<!-- Do we want something here? -->
+
+## 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
+