Abstract
MotivationAccurate identification of peptides binding to specific Major Histocompatibility Complex Class II (MHC-II) molecules is of great importance for elucidating the underlying mechanism of immune recognition, as well as for developing effective epitope-based vaccines and promising immunotherapies for many severe diseases. Due to extreme polymorphism of MHC-II alleles and the high cost of biochemical experiments, the development of computational methods for accurate prediction of binding peptides of MHC-II molecules, particularly for the ones with few or no experimental data, has become a topic of increasing interest. TEPITOPE is a well-used computational approach because of its good interpretability and relatively high performance. However, TEPITOPE can be applied to only 51 out of over 700 known HLA DR molecules.MethodWe have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of HLA DR molecules characterized by TEPITOPE to those uncharacterized. First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE.ResultThe performance of TEPITOPEpan has been extensively evaluated using various data sets from different viewpoints: predicting MHC binding peptides, identifying HLA ligands and T-cell epitopes and recognizing binding cores. Among the four state-of-the-art competing pan-specific methods, for predicting binding specificities of unknown HLA-DR molecules, TEPITOPEpan was roughly the second best method next to NETMHCIIpan-2.0. Additionally, TEPITOPEpan achieved the best performance in recognizing binding cores. We further analyzed the motifs detected by TEPITOPEpan, examining the corresponding literature of immunology. Its online server and PSSMs therein are available at http://www.biokdd.fudan.edu.cn/Service/TEPITOPEpan/.
Highlights
Major histocompatibility complex (MHC) molecules play a crucial role in the adaptive immune system mediated by T cells [1], in which peptide fragments derived from pathogens first bind to MHC molecules and are presented on the surface of a cell for recognition by T cell receptor (TCR)
We have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of Human Leukocyte Antigens (HLA) DR molecules characterized by TEPITOPE to those uncharacterized
A drawback of TEPITOPE is that only 51 DR molecules are covered out of over 700 known DR molecules, which greatly limits its usability. To overcome this problem, keeping the advantage of TEPITOPE in rule comprehensibility, we propose a new method, TEPITOPEpan, which can extrapolate from the HLA-DR molecules with known binding specificities (PSSMs) in TEPITOPE to the HLA-DR molecules with unknown binding specificities based on pocket similarity
Summary
Major histocompatibility complex (MHC) molecules play a crucial role in the adaptive immune system mediated by T cells [1], in which peptide fragments derived from pathogens first bind to MHC molecules and are presented on the surface of a cell for recognition by T cell receptor (TCR). In contrast to biochemical experiments that takes lots of expenses and time, computational approaches for predicting MHC binding peptides have received extensive attentions [3,4] They have been utilized to select a small number of promising candidate epitopes for further experimental verification [5]. We focus on predicting MHC-II binding peptides, a more challenging problem
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