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Classification of MHC I Proteins According to Their Ligand-Type Specificity

  • Eduardo Martínez-Naves
  • Esther M. Lafuente
  • Pedro A. Reche
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6825)

Abstract

Major histocompatibility complex class I (MHC I) molecules belong to a large and diverse protein superfamily whose families can be divided in three groups according to the type of ligands that they can accommodate (ligand-type specificity): peptides, lipids or none. Here, we assembled a dataset of MHC I proteins of known ligand-type specificity (MHCI556 dataset) and trained k-nearest neighbor and support vector machine algorithms. In cross-validation, the resulting classifiers predicted the ligand-type specificity of MHC I molecules with an accuracy ≥ 99%, using solely their amino acid composition. By holding out entire MHC I families prior to model building, we proved that ML-based classifiers trained on amino acid composition are capable of predicting the ligand-type specificity of MHC I molecules unrelated to those used for model building. Moreover, they are superior to BLAST at predicting the class of MHC I molecules that do not bind any ligand.

Keywords

classical MHC class I molecules non-classical MHC class I molecules machine learning ligand prediction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Eduardo Martínez-Naves
    • 2
  • Esther M. Lafuente
    • 2
  • Pedro A. Reche
    • 1
    • 2
  1. 1.Laboratory of ImmunomedicineUniversidad Complutense de MadridMadridSpain
  2. 2.Department of Microbiology I–Immunology, Facultad de MedicinaUniversidad Complutense de MadridMadridSpain

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