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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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 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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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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