Prediction of epitopes using neural network based methods.
In this paper, we describe the methodologies behind three different aspects of the NetMHC family for prediction of MHC class I binding, mainly to HLAs. We have updated the prediction servers, NetMHC-3.2, NetMHCpan-2.2, and a new consensus method, NetMHCcons, which, in their previous versions, have been evaluated to be among the very best performing MHC:peptide binding predictors available. Here we describe the background for these methods, and the rationale behind the different optimization steps implemented in the methods. We go through the practical use of the methods, which are publicly available in the form of relatively fast and simple web interfaces. Furthermore, we will review results obtained in actual epitope discovery projects where previous implementations of the described methods have been used in the initial selection of potential epitopes. Selected potential epitopes were all evaluated experimentally using ex vivo assays.
Center for Biological Sequence Analysis, DTU Systems Biology, Building 208, Technical University of Denmark, DK-2800 Lyngby, Denmark. lunde@cbs.dtu.dk
Claus
C
Lundegaard
Ole
O
Lund
Morten
M
Nielsen
D000465:Algorithms; D000483:Alleles; D000595:Amino Acid Sequence; D018604:Epitope Mapping / Q000379:methods*; D018984:Epitopes, T-Lymphocyte / Q000235:genetics / Q000378:metabolism*; D015395:Histocompatibility Antigens Class I / Q000235:genetics / Q000378:metabolism*; D006801:Humans; D020407:Internet; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010455:Peptides / Q000235:genetics / Q000276:immunology / Q000378:metabolism
D018984:Epitopes, T-Lymphocyte; D015395:Histocompatibility Antigens Class I; D010455:Peptides