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Estimating tissue-specific peptide abundance from public RNA-Seq data.

Angela Frentzen1, Jason A Greenbaum1, Haeuk Kim1

  • 1Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States.

Frontiers in Genetics
|January 30, 2023
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Summary
This summary is machine-generated.

Predicting immunogenicity is improved by including antigen abundance. Our tool, pepX, estimates protein expression levels from public databases, simplifying this process for MHC class I epitope prediction.

Keywords:
RNA sequencingRNA-Seqcancerligandspeptide (pep)predictiontool

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Area of Science:

  • Immunoinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Novel MHC class I epitope prediction tools enhance immunogenicity prediction by incorporating peptide source antigen abundance.
  • Accessing accurate expression data for source proteins is a significant challenge for users of these prediction tools.
  • Current methods for retrieving peptide source antigen expression levels from public databases are often complex and not user-friendly.

Purpose of the Study:

  • To develop a tool that simplifies the retrieval of source protein expression levels for MHC class I epitope prediction.
  • To provide users with estimated expression values for source proteins directly from public databases.
  • To optimize the estimation of peptide abundance when derived from multiple transcripts or proteins.

Main Methods:

  • Development of the Peptide eXpression annotator (pepX) tool.
  • Inputting peptide sequences into pepX to identify potential source proteins.
  • Retrieving and estimating source protein expression levels from selected public databases.
  • Investigating methods for abundance estimation from multiple transcripts, including summing transcript-level expression values.

Main Results:

  • pepX successfully identifies source proteins for a given peptide and estimates their expression levels.
  • The study found that summing transcript-level expression values is the optimal method for distinguishing true ligands from decoy peptides when a peptide originates from multiple sources.
  • The developed method improves the accuracy of immunogenicity prediction by providing crucial expression data.

Conclusions:

  • The Peptide eXpression annotator (pepX) tool streamlines the process of obtaining essential expression data for immunoinformatics.
  • Accurate estimation of source protein abundance significantly enhances the performance of MHC class I epitope prediction tools.
  • Summing transcript-level expression is a validated strategy for robust peptide abundance estimation in complex biological systems.