FusOn-pLM: A Fusion Oncoprotein-Specific Language Model via Focused Probabilistic Masking
In this work, we introduce FusOn-pLM, a novel pLM that fine-tunes the state-of-the-art ESM-2-650M protein language model (pLM) on fusion oncoprotein sequences, those that drive a large portion of pediatric cancers but are heavily disordered and undruggable. We specifically introduce a novel cosine-scheduled masked language modeling (MLM) strategy which varies the number of masked residues throughout each epoch, thereby increasing the difficulty of the training task and generating more optimal fusion oncoprotein-aware embeddings. Our model improves performance on both fusion oncoprotein-specific benchmarks and disorder prediction tasks in comparison to baseline ESM-2 representations, as well as manually-constructed biophysical embeddings, motivating downstream usage of FusOn-pLM embeddings for therapeutic design tasks targeting these fusions. Please feel free to try out our embeddings and reach out if you have any questions!
How to generate FusOn-pLM embeddings for your fusion oncoprotein:
from transformers import AutoTokenizer, AutoModel
import logging
import torch
# Suppress warnings about newly initialized 'esm.pooler.dense.bias', 'esm.pooler.dense.weight' layers - these are not used to extract embeddings
logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR)
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
# Load the tokenizer and model
model_name = "ChatterjeeLab/FusOn-pLM"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
model.to(device)
model.eval()
# Example fusion oncoprotein sequence: MLLT10:PICALM, associated with Acute Myeloid Leukemia (LAML)
# Amino acids 1-80 are derived from the head gene, MLLT10
# Amino acids 81-119 are derived from the tail gene, PICALM
sequence = "MVSSDRPVSLEDEVSHSMKEMIGGCCVCSDERGWAENPLVYCDGHGCSVAVHQACYGIVQVPTGPWFCRKCESQERAARVPPQMGSVPVMTQPTLIYSQPVMRPPNPFGPVSGAQIQFM"
# Tokenize the input sequence
inputs = tokenizer(sequence, return_tensors="pt", padding=True, truncation=True,max_length=2000)
inputs = {k: v.to(device) for k, v in inputs.items()}
# Get the embeddings
with torch.no_grad():
outputs = model(**inputs)
# The embeddings are in the last_hidden_state tensor
embeddings = outputs.last_hidden_state
# remove extra dimension
embeddings = embeddings.squeeze(0)
# remove BOS and EOS tokens
embeddings = embeddings[1:-1, :]
# Convert embeddings to numpy array (if needed)
embeddings = embeddings.cpu().numpy()
print("Per-residue embeddings shape:", embeddings.shape)
Repository Authors
Sophia Vincoff, PhD Student at Duke University Pranam Chatterjee, Assistant Professor at Duke University
Reach out to us with any questions!