Peptide length-based prediction of peptide--MHC class II binding

  • Authors:

  • Stewart T. Chang;Debashis Ghosh;Denise E. Kirschner;Jennifer J. Linderman

  • Affiliations:

  • Program in Bioinformatics, University of Michigan Ann Arbor, MI, USA;Department of Biostatistics, University of Michigan Ann Arbor, MI, USA;Department of Microbiology and Immunology, University of Michigan Ann Arbor, MI, USA;Department of Chemical Engineering, University of Michigan Ann Arbor, MI, USA

  • Venue:
  • Bioinformatics
  • Year:
  • 2006

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Abstract

Motivation: Algorithms for predicting peptide--MHC class II binding are typically similar, if not identical, to methods for predicting peptide--MHC class I binding despite known differences between the two scenarios. We investigate whether representing one of these differences, the greater range of peptide lengths binding MHC class II, improves the performance of these algorithms. Results: A non-linear relationship between peptide length and peptide--MHC class II binding affinity was identified in the data available for several MHC class II alleles. Peptide length was incorporated into existing prediction algorithms using one of several modifications: using regression to pre-process the data, using peptide length as an additional variable within the algorithm, or representing register shifting in longer peptides. For several datasets and at least two algorithms these modifications consistently improved prediction accuracy. Availability: http://malthus.micro.med.umich.edu/Bioinformatics Contact: linderma@umich.edu