DeepNeoAG: Neoantigen epitope prediction from melanoma antigens using a synergistic deep learning model combining protein language models and multi-window scanning convolutional neural networks

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Neoantigens, derived from tumor-specific mutations, play a crucial role in eliciting anti-tumor immune responses and have emerged as promising targets for personalized cancer immunotherapy. Accurately identifying neoantigens from a vast pool of potential candidates is crucial for developing effective therapeutic strategies. This study presents a novel deep learning model that leverages the power of protein language models (PLMs) and multi-window scanning convolutional neural networks (CNNs) to predict neoantigens from normal tumor antigens with high accuracy. In this study, we present DeepNeoAG, a novel framework combines the global sequence-level information captured by a pre-trained PLM with the local sequence-based information features extracted by a multi-window scanning CNN, enabling a comprehensive representation of the protein's mutational landscape. We demonstrate the superior performance of DeepNeoAG compared to existing methods and highlight its potential to accelerate the development of personalized cancer immunotherapies.