metadata

license: mit
language:
  - en
metrics:
  - accuracy
  - precision
  - recall
  - f1
  - roc_auc
  - matthews_correlation
library_name: peft
pipeline_tag: token-classification
tags:
  - protein language model
  - post translational modification
  - biology
  - proteins
  - ESM-2

ESM-2 for Post Translational Modification

This is a LoRA finetuned version of esm2_t12_35M_UR50D for predicting post translational modification sites.

Metrics

  "eval_loss": 0.4661065936088562,
  "eval_accuracy": 0.9876599555715365,
  "eval_auc": 0.8673592596422711,
  "eval_precision": 0.14941997670219148,
  "eval_recall": 0.7463955099754822
  "eval_f1": 0.24899413187145658,
  "eval_mcc": 0.3305508498121041,

Using the Model

To use this model, run the following:

!pip install transformers -q
!pip install peft -q

from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
import torch

# Path to the saved LoRA model
model_path = "AmelieSchreiber/esm2_t12_35M_ptm_lora_2100K"
# ESM2 base model
base_model_path = "facebook/esm2_t12_35M_UR50D"

# Load the model
base_model = AutoModelForTokenClassification.from_pretrained(base_model_path)
loaded_model = PeftModel.from_pretrained(base_model, model_path)

# Ensure the model is in evaluation mode
loaded_model.eval()

# Load the tokenizer
loaded_tokenizer = AutoTokenizer.from_pretrained(base_model_path)

# Protein sequence for inference
protein_sequence = "MAVPETRPNHTIYINNLNEKIKKDELKKSLHAIFSRFGQILDILVSRSLKMRGQAFVIFKEVSSATNALRSMQGFPFYDKPMRIQYAKTDSDIIAKMKGT"  # Replace with your actual sequence

# Tokenize the sequence
inputs = loaded_tokenizer(protein_sequence, return_tensors="pt", truncation=True, max_length=1024, padding='max_length')

# Run the model
with torch.no_grad():
    logits = loaded_model(**inputs).logits

# Get predictions
tokens = loaded_tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])  # Convert input ids back to tokens
predictions = torch.argmax(logits, dim=2)

# Define labels
id2label = {
    0: "No ptm site",
    1: "ptm site"
}

# Print the predicted labels for each token
for token, prediction in zip(tokens, predictions[0].numpy()):
    if token not in ['<pad>', '<cls>', '<eos>']:
        print((token, id2label[prediction]))