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license: cc-by-nc-nd-4.0 |
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# FusOn-pLM: A Fusion Oncoprotein-Specific Language Model via Focused Probabilistic Masking |
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In this work, we introduce **FusOn-pLM**, a novel pLM that fine-tunes the state-of-the-art [ESM-2-650M](https://huggingface.co/facebook/esm2_t33_650M_UR50D) 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 masked language modeling (MLM) strategy, employing a binding-site probability predictor to focus masking on key amino acid residues, thereby 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! |
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**How to generate FusOn-pLM embeddings for your fusion oncoprotein:** |
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``` |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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# Load the tokenizer and model |
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model_name = "ChatterjeeLab/FusOn-pLM" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModel.from_pretrained(model_name) |
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# Example fusion oncoprotein sequence: MLLT10:PICALM, associated with Acute Myeloid Leukemia (LAML) |
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# Amino acids 1-80 are derived from the head gene, MLLT10 |
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# Amino acids 81-119 are derived from the tail gene, PICALM |
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sequence = "MVSSDRPVSLEDEVSHSMKEMIGGCCVCSDERGWAENPLVYCDGHGCSVAVHQACYGIVQVPTGPWFCRKCESQERAARVPPQMGSVPVMTQPTLIYSQPVMRPPNPFGPVSGAQIQFM" |
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# Tokenize the input sequence |
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inputs = tokenizer(sequence, return_tensors="pt") |
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# Get the embeddings |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# The embeddings are in the last_hidden_state tensor |
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embeddings = outputs.last_hidden_state |
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# Convert embeddings to numpy array (if needed) |
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embeddings = embeddings.squeeze(0).numpy() |
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print("Per-residue embeddings shape:", embeddings.shape) |
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``` |
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## Repository Authors |
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[Sophia Vincoff](mailto:sophia.vincoff@duke.edu), PhD Student at Duke University <br> |
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[Pranam Chatterjee](mailto:pranam.chatterjee@duke.edu), Assistant Professor at Duke University |
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Reach out to us with any questions! |
