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EpitopeTransfer: a Phylogeny-aware Transfer Learning Framework for Taxon-specific Linear B-cell Epitope Prediction.

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  • 1Department of Computer Science, University of Brasília, Brasília DF 70910-900, Brazil.

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Summary

EpitopeTransfer improves linear B-cell epitope (LBCE) prediction by using phylogeny-aware transfer learning. This method enhances accuracy for diverse pathogens, overcoming limitations of current generalist models.

Keywords:
Deep learningLinear B-cell epitopesPhylogeny-aware methodsTransfer learning

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Area of Science:

  • Immunoinformatics
  • Computational Biology
  • Vaccine Development

Background:

  • Accurate prediction of linear B-cell epitopes (LBCEs) is crucial for developing immunodiagnostics, vaccines, and therapeutics.
  • Current computational predictors often require extensive pathogen data, leading to biases and reduced performance on understudied or emerging pathogens.
  • Generalist models trained on broad datasets may not capture specific pathogen characteristics effectively.

Purpose of the Study:

  • To introduce EpitopeTransfer, a novel phylogeny-aware transfer learning strategy for enhanced LBCE prediction.
  • To address the limitations of existing LBCE prediction methods, particularly their performance on neglected or emerging pathogens.
  • To improve the accuracy and generalizability of computational epitope prediction models.

Main Methods:

  • Developed EpitopeTransfer, a transfer learning approach utilizing large protein language models.
  • Fine-tuned models with abundant data from higher-level taxa (e.g., broader taxonomic groups).
  • Applied refined feature embeddings to train pathogen- or lower taxon-specific LBCE prediction models.

Main Results:

  • EpitopeTransfer demonstrated substantially increased predictive performance across viruses, bacteria, and eukaryotes compared to state-of-the-art methods.
  • The gains in performance were attributed to the phylogeny-aware fine-tuning of the feature embedder and taxon-specific model optimization.
  • The strategy effectively overcomes biases associated with generalist models and improves prediction for diverse pathogens.

Conclusions:

  • Phylogeny-aware transfer learning significantly enhances the accuracy of linear B-cell epitope prediction.
  • EpitopeTransfer offers a robust solution for predicting LBCEs, especially for underrepresented pathogens.
  • This approach accelerates the discovery and prioritization of immunological targets for vaccine and therapeutic development.