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DeepNeo: a webserver for predicting immunogenic neoantigens.

Jeong Yeon Kim1, Hyoeun Bang2, Seung-Jae Noh2

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We upgraded DeepNeo, a deep learning model, to better identify immunogenic neoepitopes. This tool enhances the prediction of cancer and viral neoantigens, improving immunotherapy development.

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • Non-self epitopes trigger immune responses when presented by major histocompatibility complex (MHC) molecules and recognized by T cells.
  • Identifying immunogenic neoepitopes is crucial for developing effective cancer and viral immunotherapies.
  • Current prediction methods often focus solely on peptide-MHC binding, neglecting T cell reactivity.

Purpose of the Study:

  • To upgrade the DeepNeo model for improved identification of immunogenic neoepitopes.
  • To enhance the prediction accuracy of neoantigens by incorporating updated training data.
  • To provide a more reliable tool for neoantigen prediction in cancer and virus research.

Main Methods:

  • Development of DeepNeo-v2, an upgraded deep learning model.
  • Utilizing updated training datasets for model refinement.
  • Capturing structural properties of peptide-MHC pairs and T cell reactivity.

Main Results:

  • The upgraded DeepNeo-v2 model demonstrated improved evaluation metrics compared to previous versions.
  • DeepNeo-v2 exhibited a prediction score distribution that more accurately reflects known neoantigen behavior.
  • Enhanced accuracy in predicting immunogenic neoepitopes.

Conclusions:

  • DeepNeo-v2 represents a significant advancement in computational neoantigen prediction.
  • The improved model facilitates more precise identification of potential targets for cancer and viral immunotherapies.
  • The tool is accessible online for broader research application.