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Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
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An automated benchmarking platform for MHC class II binding prediction methods.

Massimo Andreatta1, Thomas Trolle2, Zhen Yan3

  • 1Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP1650, San Martín, Buenos Aires, Argentina.

Bioinformatics (Oxford, England)
|December 28, 2017
PubMed
Summary
This summary is machine-generated.

Choosing the best peptide-MHC binding prediction tool is challenging. An automated, weekly benchmarking platform provides unbiased performance evaluations, identifying NetMHCIIpan as the current top performer.

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Area of Science:

  • Immunoinformatics
  • Computational Biology
  • T cell epitope discovery

Background:

  • Computational peptide-MHC binding prediction is crucial for identifying T cell epitopes.
  • Numerous prediction methods exist, but their performance is often inconsistently reported, complicating tool selection for researchers.

Purpose of the Study:

  • To establish an automated platform for unbiased benchmarking of peptide-MHC class II binding prediction tools.
  • To provide transparent, up-to-date evaluations of prediction tool performance.

Main Methods:

  • Developed an automated platform to benchmark peptide-MHC class II prediction tools.
  • Evaluated tools on newly added, pre-publication data from the Immune Epitope Database (IEDB).
  • Conducted weekly benchmarks with results displayed on a public website.

Main Results:

  • The initial benchmark included six prediction servers, with others encouraged to join.
  • Performance was assessed on 59 datasets comprising over 10,000 binding affinity measurements.
  • NetMHCIIpan demonstrated the highest accuracy, followed by NN-align and the IEDB consensus method.

Conclusions:

  • The automated benchmarking platform offers a reliable method for assessing peptide-MHC II prediction tools.
  • NetMHCIIpan is currently the most accurate tool for peptide-MHC class II binding prediction based on this benchmark.
  • The platform provides continuous, unbiased performance monitoring for the immunoinformatics community.