NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure
Open Access
- 1 January 2010
- journal article
- Published by Springer Science and Business Media LLC in Immunome Research
- Vol. 6 (1), 9
- https://doi.org/10.1186/1745-7580-6-9
Abstract
Binding of peptides to Major Histocompatibility class II (MHC-II) molecules play a central role in governing responses of the adaptive immune system. MHC-II molecules sample peptides from the extracellular space allowing the immune system to detect the presence of foreign microbes from this compartment. Predicting which peptides bind to an MHC-II molecule is therefore of pivotal importance for understanding the immune response and its effect on host-pathogen interactions. The experimental cost associated with characterizing the binding motif of an MHC-II molecule is significant and large efforts have therefore been placed in developing accurate computer methods capable of predicting this binding event. Prediction of peptide binding to MHC-II is complicated by the open binding cleft of the MHC-II molecule, allowing binding of peptides extending out of the binding groove. Moreover, the genes encoding the MHC molecules are immensely diverse leading to a large set of different MHC molecules each potentially binding a unique set of peptides. Characterizing each MHC-II molecule using peptide-screening binding assays is hence not a viable option.Keywords
This publication has 32 references indexed in Scilit:
- NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding predictionBMC Bioinformatics, 2009
- Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment methodBMC Bioinformatics, 2007
- Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scoresBMC Bioinformatics, 2006
- SVRMHC prediction server for MHC-binding peptidesBMC Bioinformatics, 2006
- Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical propertiesMolecular Immunology, 2006
- Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applicationsImmunogenetics, 2005
- Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approachBioinformatics, 2004
- Hidden Markov Model-Based Prediction of Antigenic Peptides That Interact with MHC Class II MoleculesJournal of Bioscience and Bioengineering, 2002
- Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matricesNature Biotechnology, 1999
- Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network.Bioinformatics, 1998