README.md

metadata

language: en
tags:
  - transformers
  - protein
  - peptide-receptor
license: apache-2.0
datasets:
  - custom

Model Description

This model predicts receptor classes, identified by their PDB IDs, from peptide sequences using the ESM2 (Evolutionary Scale Modeling) protein language model with esm2_t12_35M_UR50D pre-trained weights. The model is fine-tuned for receptor prediction using datasets from PROPEDIA and PepNN, as well as novel peptides experimentally validated to bind to their target proteins, with binding conformations determined using ClusPro, a protein-protein docking tool. The name pep2rec_cppp reflects the model's ability to predict peptide-to-receptor relationships, leveraging training data from ClusPro, PROPEDIA, and PepNN. It's particularly useful for researchers and practitioners in bioinformatics, drug discovery, and related fields, aiming to understand or predict peptide-receptor interactions.

How to Use

Here is how to predict the receptor class for a peptide sequence using this model:

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from joblib import load

MODEL_PATH = "littleworth/esm2_t12_35M_UR50D_pep2rec_cppp"
model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)

LABEL_ENCODER_PATH = f"{MODEL_PATH}/label_encoder.joblib"
label_encoder = load(LABEL_ENCODER_PATH)


input_sequence = "GNLIVVGRVIMS"

inputs = tokenizer(input_sequence, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    probabilities = torch.softmax(outputs.logits, dim=1)
    predicted_class_idx = probabilities.argmax(dim=1).item()

predicted_class = label_encoder.inverse_transform([predicted_class_idx])[0]

class_probabilities = probabilities.squeeze().tolist()
class_labels = label_encoder.inverse_transform(range(len(class_probabilities)))

sorted_indices = torch.argsort(probabilities, descending=True).squeeze()
sorted_class_labels = [class_labels[i] for i in sorted_indices.tolist()]
sorted_class_probabilities = probabilities.squeeze()[sorted_indices].tolist()

print(f"Predicted Receptor Class: {predicted_class}")
print("Top 10 Class Probabilities:")
for label, prob in zip(sorted_class_labels[:10], sorted_class_probabilities[:10]):
    print(f"{label}: {prob:.4f}")

Which gives this output:

Predicted Receptor Class: 1JXP
Top 10 Class Probabilities:
1JXP: 0.9839
3KEE: 0.0001
5EAY: 0.0001
1Z9O: 0.0001
2KBM: 0.0001
2FES: 0.0001
1MWN: 0.0001
5CFC: 0.0001
6O09: 0.0001
1DKD: 0.0001

Evaluation Results

The model was evaluated on a held-out test set, yielding the following metrics:

{
  "train/loss": 0.727,
  "train/grad_norm": 4.4672017097473145,
  "train/learning_rate": 2.3235385792411667e-8,
  "train/epoch": 10,
  "train/global_step": 352910,
  "_timestamp": 1712189024.5060718,
  "_runtime": 503183.0418128967,
  "_step": 716,
  "eval/loss": 0.7138708829879761,
  "eval/accuracy": 0.7794731752930051,
  "eval/runtime": 5914.5446,
  "eval/samples_per_second": 15.912,
  "eval/steps_per_second": 15.912,
  "train/train_runtime": 497231.6027,
  "train/train_samples_per_second": 5.678,
  "train/train_steps_per_second": 0.71,
  "train/total_flos": 600463318555361300,
  "train/train_loss": 0.9245198557043193,
  "_wandb": {
    "runtime": 503182
  }
}