NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction
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Open Access
- 18 September 2009
- journal article
- Published by Springer Nature in BMC Bioinformatics
- Vol. 10 (1) , 296
- https://doi.org/10.1186/1471-2105-10-296
Abstract
The major histocompatibility complex (MHC) molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event.Keywords
This publication has 33 references indexed in Scilit:
- Major Histocompatibility Complex Class II Molecule-Human Immunodeficiency Virus Peptide Analysis Using a Microarray ChipClinical and Vaccine Immunology, 2009
- Evaluation of MHC-II peptide binding prediction servers: applications for vaccine researchBMC Bioinformatics, 2008
- Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methodsBioinformatics, 2008
- On Evaluating MHC-II Binding Peptide Prediction MethodsPLOS ONE, 2008
- Quantitative Predictions of Peptide Binding to Any HLA-DR Molecule of Known Sequence: NetMHCIIpanPLoS Computational Biology, 2008
- A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus ApproachPLoS Computational Biology, 2008
- Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithmsBMC Bioinformatics, 2007
- Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment methodBMC Bioinformatics, 2007
- Reliable prediction of T‐cell epitopes using neural networks with novel sequence representationsProtein Science, 2003
- Selection of representative protein data setsProtein Science, 1992