SaProt_650M_PDB

<!-- ##### 🔴 <font color=red>Note: SaProt requires structural (SA token) input for optimal performance. AA-sequence-only mode works but must be finetuned - frozen embeddings work only for SA, not AA sequences! With structural input, SaProt surpasses ESM2 in most tasks.</font> -->

We provide two ways to use SaProt, including through huggingface class and through the same way as in esm github. Users can choose either one to use.

Huggingface model

The following code shows how to load the model.

from transformers import EsmTokenizer, EsmForMaskedLM

model_path = "/your/path/to/SaProt_650M_PDB"
tokenizer = EsmTokenizer.from_pretrained(model_path)
model = EsmForMaskedLM.from_pretrained(model_path)

#################### Example ####################
device = "cuda"
model.to(device)

seq = "MdEvVpQpLrVyQdYaKv"
tokens = tokenizer.tokenize(seq)
print(tokens)

inputs = tokenizer(seq, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}

outputs = model(**inputs)
print(outputs.logits.shape)

"""
['Md', 'Ev', 'Vp', 'Qp', 'Lr', 'Vy', 'Qd', 'Ya', 'Kv']
torch.Size([1, 11, 446])
"""

esm model

The esm version is also stored in the same folder, named SaProt_650M_AF2.pt. We provide a function to load the model.

from utils.esm_loader import load_esm_saprot

model_path = "/your/path/to/SaProt_650M_PDB.pt"
model, alphabet = load_esm_saprot(model_path)