Accurate prediction of major histocompatibility complex class II epitopes by sparse representation via ℓ 1-minimization

Accurate prediction of major histocompatibility complex class II epitopes by sparse representation via ℓ 1-minimization

BioData Mining, November 2014

25392716

Clemente Aguilar-Bonavides, Reinaldo Sanchez-Arias, Cristina Lanzas

The major histocompatibility complex (MHC) is responsible for presenting antigens (epitopes) on the surface of antigen-presenting cells (APCs). When pathogen-derived epitopes are presented by MHC class II on an APC surface, T cells may be able to trigger an specific immune response. Prediction of MHC-II epitopes is particularly challenging because the open binding cleft of the MHC-II molecule allows epitopes to bind beyond the peptide binding groove; therefore, the molecule is capable of accommodating peptides of variable length. Among the methods proposed to predict MHC-II epitopes, artificial neural networks (ANNs) and support vector machines (SVMs) are the most effective methods. We propose a novel classification algorithm to predict MHC-II called sparse representation via ℓ 1-minimization.