NIEluter: Predicting peptides eluted from HLA class I molecules

The immune system has evolved to make a diverse repertoire of peptides processed from self and foreign proteomes, which are displayed in antigen-binding grooves of major histocompatibility complex (MHC) proteins at cell surface for surveillance by T cells. These antigenic peptides are termed Naturally Processed Peptides or Naturally Presented Peptides (NPPs), which play a major role in cell-mediated immunity and rational vaccine design. Therefore, it is intensely desirable to predict NPPs from a given protein antigen, or to foretell if an MHC-binding peptide can be eluted from a given MHC protein. In this paper, we describe NIEluter, an ensemble predictor based on support vector machine (SVM). It consists of a combination of five SVM models trained with position-specific amino acid composition, position-specific dipeptide composition, Hidden Markov Model, binary encoding, and BLOSUM62 feature. NIEluter can predict NPPs of length 8–11 from six HLA alleles (A0201, B0702, B3501, B4403, B5301, and B5701) at present. Evaluated with five-fold cross-validation and independent datasets if available, NIEluter shows good performance. It outperforms MHC-NP in 7 out of 24 types of situation and precedes NetMHC3.2 in most cases, indicating that it is a helpful complement to available tools. NIEluter has been implemented as a free web service, which can be accessed at http://immunet.cn/nie/cgi-bin/nieluter.pl.